Steve Staso - Computing Solutions Strategist

At the core of digital transformation lies data democratization, the practice of making data accessible throughout all departments of a business, not simply the C-suite and IT team.

Datasets are growing fast. Forget data analytics – business leaders are struggling just to visualize and map out data to begin with. Data visualization works to fix this.

Also see: Top Data Visualization Tools

What is Data Visualization?

Data visualization is the graphical representation of datasets and information. Data visualization is an umbrella term for visualizing all types of data through charts, graphs, and maps.

The ultimate goal is to visually represent your data in an accessible and easy-to-understand manner. Visualizing data is a fundamental step in understanding trends, uncovering patterns, and tracking outliers.

Experts predict that the data visualization market will expand greatly within the next five years, with compound annual growth rates of over 10%, growing to a $19.2 billion market valuation by 2027. Although the rise of data democratization and digital transformation are causes of this rise, much of this growth is due to growing interest in business intelligence.

What are the Benefits of Data Visualization?

There are three key benefits to data visualization:

1. Making Big Data Digestible

There is no question that datasets are growing tenfold. Combine this with the growing advancement of IT systems that are increasingly more advanced, and you have a colossal pain point for businesses.

The fact that data is becoming so overwhelming for organizations has spurred the rise of AIOps. AIOps helps businesses with various use cases such as:

• Predictive alerting
• Root cause analysis
• Prioritizing events
• Predictive outages
• Service desk ticketing

How does this relate to data visualization? Data visualization tools are an effective way to map out data in an automated manner – and this automation is core to AIOps. This hands-off approach makes approaching large datasets rather easy to gather, and then in turn to digest using data visualization techniques.

2. Greater Accessibility

We mentioned how large datasets are now accessible for a greater number of users. This demonstrates that data visualization is a key factor in data democratization.

Data visualization tools help simplify complex robust data points and present them in highly digestible ways. This growing accessibility can help upskill employees and make businesses more efficient.

3. Greater Efficiency & Understanding

Traditional means of sifting through data were meticulous and time-consuming. Data visualization helps businesses discover insights at a much faster rate than prior to the advent of visualization tools.

Speed is central here. This growing scalability means that business leaders have more room to be granular in their analysis. If data is mapped out more quickly, IT teams and data scientists have more time to draw more complex insights from their well-organized databases.

Prior to data democratization, gaps in communication were all too common for enterprises and businesses alike. Boiling down and explaining advanced insights can be difficult without a common understanding of what the datasets behind these insights mean. With modern data visualization software, such as Tableau and Microsoft Power BI, data analysis is broadened to virtually any department within your organization.

Also see: Best Business Intelligence Tools 

What are the Different Types of Data Visualization?

Here are examples of various forms of data visualization and uses cases:

Bar graphs: These types of graphs are best utilized to compare aspects of different groups or to track those aspects over time. Bar graphs are best used when changes are rather large.

Line graphs: One of the most popular and fundamental forms of data visualization are line graphs, which are used to track changes over short and long periods of time. Line graphs are particularly useful to highlight smaller changes.

Graphs are, for the most part, are rather modular. Above is an example of a line graph tracking bounce rates; this is in contrast to a bar graph that represents page load times.

Pie Charts: Another fundamental form of data visualization, pie charts are effective for comparing parts of a whole. Because they are not placed on an X-Y plot, tracking data over time is not possible with a pie chart.

These charts are very basic examples of data visualization. Many modern tools are designed to open up complex methods to the everyday user. For instance, here’s one way a management trainee in the finance industry used data visualization software for their needs:

“Power BI is a widely used software in our organization where we deal with a huge amount of raw data and process it to gather actionable insights. It helps us to visualize scattered and unfiltered information efficiently and easy to understand manner…Overall, I would say that this is a must-have software for any enterprise that directly witnesses a lot of data being gathered to formulate strategies and plan of actions” – Management Trainee in finance industry, review of Microsoft Power BI at Gartner Peer Insights.

Scatter Plots: A slightly more advanced data visualization method is scatter plotting. Scatter plots are an effective way to explore the relationship between two variables and multiple sets of data. Below is an example of a scatter plot mapping out profitability in various American cities. Note how cities with larger profitability have larger circles.

As you can see above, data visualization tools can map out different graphs on the same page to help present holistic insights more clearly. Advanced software solutions will let users build interactive graphs that help stakeholders understand insights even more deeply.

Also see: Top Data Mining Techniques 

Data Visualization Best Practices

Keep in mind that the main goal behind data visualization is to present data in a more interesting, relevant, and accessible manner to diverse audiences. Each best practice is specifically designed ultimately for this outcome.

Best practices include:

Choose the Optimal Chart and Graph: Make sure to consider your needs before selecting the proper way to visualize your data. Ponder whether you want to track data over time or how many variables you intend on analyzing. These questions can help your decision making process.

Use Templates: Many data visualization tools offer templates that businesses can “plug and play” with. You want your data to be interesting, but don’t stray away too much from the norm. Templates are an effective way to get a jumpstart on well-presented data and information. Don’t overthink it.

Leverage Color: Color is a subtle, yet highly effective way to map data. For example: you could choose to make an anomalous point in your dataset red to immediately differentiate it from other points. For instance, use red to indicate heat and blue to indicate cold, if you’re tracking weather.

Don’t Distort Data: If users go too overboard with their designs and data visualization charts, they could end up inadvertently distorting insights. For example, pie charts should always be 2D. 3D pie charts might look more interesting and even draw more attention, but the unfortunate result makes each proportion of the chart harder to decipher. An effective pie chart, or any graph for that matter, shouldn’t be a puzzle.

Also see: Top Data Analytics Tools 

The post What Is Data Visualization? Benefits, Types & Best Practices appeared first on eWEEK.

Qlik and Tableau are among the most highly regarded business intelligence and data analytics tools available. In this article, we will compare these two data leaders, and note the ideal user for each.

To compete in the business intelligence (BI) and data analytics market, vendors must provide functions such as data preparation, data mining, visual exploration, insight generation, and self-service analytics. The marketplace is also demanding cloud-based tools that offer data visualization capabilities and interactive key performance indicator (KPI) dashboards. Intuitive, easy-to-read charts have become a top priority. Tools must also be able to gather data from a wide range of sources.

Also see: Best Data Mining Tools & Software 

Qlik vs. Tableau: Key Feature Comparison

Qlik is an end-to-end data integration and data analytics solution for modern BI. It delivers real-time insights and automation through its Active Intelligence Platform, which leverages artificial intelligence and the cloud.

Unlike many other BI platforms on the market that rely on SQL, Qlik’s engine can work with any data source in its existing format, allowing users to execute analytics on all their data within Qlik. The company makes it available in several ways. In addition to SaaS, there is a choice of multi-cloud and on-premises options.

The Qlik Sense data analytics platform includes an associative analytics engine, AI capabilities, and operates in a high-performance cloud platform. It empowers executives, decision-makers, analysts, and staffers to freely search and explore insights to help promote digital transformation.

Tableau helps people see and understand data. It offers visual analytics with AI, data management, and collaboration. At the heart of Tableau is a proprietary technology called VizQL that makes interactive data visualization an integral part of understanding data. A traditional analysis tool forces you to analyze data in rows and columns, choose a subset of the data to present, organize that data into a table, then create a chart from that table. VizQL skips those steps and creates a visual representation right away, providing visual feedback as you analyze.

Tableau offers an integrated data analytics and AI suite. AI-powered analytics lowers the barrier to data science techniques, enabling business users and analysts to make smarter decisions faster. It goes beyond creating charts through a series of templates and wizards to offer control and customization around insights. Full functionality can be deployed on any cloud (private or public cloud) or on-premises, used on Mac or PC.

Qlik Sense’s AI assistant, called Insight Advisor, offers insight generation, task automation, and search and natural-language interaction. Users can combine and load data, create smart visualizations, and drag and drop to build analytics apps. It also offers interactive mobile analytics, and embedded analytics; its Associative Engine allows people to explore in any data direction.

Qlik’s AI-infused cloud analytics, then, are all about moving from passive backward-looking data consumption to activating relevant and timely data for real-time decision making. Its open cloud architecture means IT and users can leverage analytics with their cloud platform of choice such as AWS, Google Cloud Platform or Microsoft Azure.

Tableau has over 80 native data connectors, which allows users to access data from a great many sources. Web Data connectors allow you to connect to myriad data sources. Users have the option of either ingesting data into the data engine or pushing queries to their databases.

Tableau is now owned by Salesforce. This has positive implications in terms of integration with Salesforce and opening the door to a large new customer base. However, it remains to be seen if Salesforce will allow Tableau to grow or will be more inclined to view it as a value add to its own primary offerings.

Also see: Data Mining Techniques

Qlik vs. Tableau: Comparing Analyst Comments

Qlik has been rated a Leader by Gartner for many years. The latest Gartner Magic Quadrant for Analytics and Business Intelligence Platforms again places it in that category. Qlik was also named by Gartner as a Challenger in the 2021 MQ for Data Integration Tools. A recent BARC survey had Qlik earning six number one rankings and 45 leading positions in six peer groups in the annual survey of some 2,500 BI and analytics practitioners.

Overall, Gartner praises Qlik’s strong product vision for ML- and AI-driven augmentation, and a deployment flexibility that doesn’t limit customers to any particular cloud. Acquisitions of RoxAI, Knarr Analytics, and Blender.io are enhancing capabilities for alerting, continuous intelligence, and SaaS platform integration. This is serving to expand its capabilities across the data and analytics pipeline. In addition, its Data Literacy Program is designed to educate users on BI and analytics and eliminate the need for data science degrees in analytics. Gartner notes, however, that Tableau has greater market momentum based on Gartner search and client inquiry data.

Tableau is another Leader in the Gartner Magic Quadrant, graded a little higher than Qlik. The analyst firm gave it high marks for having a sleek user experience that enables users to perform analysis seamlessly. It also does well on advanced visualization capabilities, courtesy of its VizQL engine. On the marketing front, Gartner believes Tableau has stronger momentum than Qlik. With more than 145,000 people attending its online user conference, and its free Tableau Public platform including more than three million data visualizations, Gartner also gives Tableau praise for usability. Finally, the company has put a lot of time and effort into its partnership with Salesforce. Tableau Viz Lightning, for example, was developed to simplify integration of Tableau visualizations into Salesforce.

Qlik vs. Tableau: Pricing

Although Qlik Sense offers core analytic and BI platform capabilities in a single license, additional licensing is needed for add-on capabilities, such as Qlik Catalog, Qlik Insight Advisor Chat, and Qlik NPrinting for Mode 1 reporting, if deployed on-premises, according to Gartner. However, Qlik’s SaaS platform simplifies everything into a standard subscription. Yet product pricing complexity is a complaint that is sometimes leveled at Qlik.

Tableau is a little more open than Qlik on pricing. It offers enterprise subscription plans with set prices. Still, Gartner commented on premium pricing concerns raised among its customers. For example, the company has been introducing Tableau CRM priced at up to $150 per user per month.

Qlik vs. Tableau: Comparing Similarities and Differences

Both Qlik and Tableau are highly capable BI and analytics solutions that are among the best in the business. Users can’t go far wrong choosing either one. They are similar in many ways:

• Both companies enable customers to create interactive dashboards.
• Both offer the flexibility to deploy on-premises or with any major cloud provider.
• Both have added augmented analytics, using artificial intelligence and machine learning technologies to make data analysis easier on their platforms.
• Both offer data management capabilities to help organizations curate and govern the data analyzed by their products.

Tableau claims that it works better than Qlik with modern databases. This is because Tableau can connect to data live (performing queries in-database and returning results in real-time) or in-memory (ingesting data from source systems into Tableau). This allows customers to control performance, cost, and data freshness more easily than with Qlik. While both Tableau and Qlik can create dashboards, Tableau believes VizQL allows non-technical people to explore data without the help of IT. Qlik, at times, may require technical expertise to analyze data.

Another way to compare them is to look at their user communities. Qlik’s user community has a large cohort of IT developers and systems integrators. This indicates a strong focus on back-end integration and IT smarts. Tableau’s massive fanbase, on the other hand, is more focused on helping customers overcome roadblocks that slow the adoption of analytics products. While highly generalized, that might be a good way to contrast the platforms. One speaks well to developers and integrators. The other talks to broader, non-technical audience.

On the other side of the coin, Tableau is less capable as a cloud platform. With its heritage being on-premises deployments, its cloud-hosted solutions are limited mainly to Tableau Online and Tableau CRM. Gartner stated that Tableau lacks a cloud-native architecture for its large on-premises customer base to embrace the cloud’s full benefits.

Forrester, too, commented that the Qlik Associative Engine stands apart from the competition. Most of the competition relies on search-based BI platforms to provide guided analysis and data exploration. Qlik Sense search, however, is based on data values and relationships, which enables a user to select data points directly from a visualization. Forrester said this, “supports almost unrestricted data exploration.”

Qlik vs. Tableau: Who is the Ideal User for Each? 

Tableau targets business users who need an intuitive platform to stay more informed with data. Ideal users are non-technical business people who are interested in making smarter decisions through better understanding of data. And with its Salesforce ownership and greater Salesforce integration, users of Salesforce would be obviously inclined more towards it than Qlik.

Qlik could be characterized as more of a platform for IT personnel and data analysts who respond to business requests for data and analysis. However, its cloud architecture is more suited to organizations that are heavily invested in the cloud already. As such, Qlik offers greater scalability and flexibility in the cloud.

Also see: Guide to Data Pipelines

The post Qlik vs. Tableau: 2022 Software Comparison appeared first on eWEEK.

The amount of data at the Edge is already a challenge for IT leaders. Yet it’s becoming even more of a problem for managers of IT infrastructure as data levels expand exponentially.

Indeed, the overwhelming edge computing trend is expansion of data levels. IDC predicts that by 2025 there will be a total of 55.7 billion IoT (Internet of Things) devices, generating 73.1 zettabytes (ZB) of data worldwide. IDC projects that worldwide Edge revenues will grow to $250.6 billion in 2024, with a compound annual growth rate (CAGR) of 12.5% during that period.

As a result, businesses around the world will “feel the burn” of uncontrolled data transfers to the Cloud and the core data centers this year. Clearly, distributed data generated at the Edge cannot – and should not – be sent to a corporate headquarters for storing and processing. There’s simply too much of it. Now data management must extend to the Edge.

Decisions must be made about where, and how, to store and manage all that data. Considerations include a strategy about managing data from multiple data silos: IT data, operational data, network data and sensor-based IoT data. Plus, the data-types vary, by applications – including block data, file data and object data – with a variety of data that reflects changing computing technologies from the 1960s to the 2020s.

Adding to the challenge, there are myriad edge data categories – and some companies have more than one. They include:

• Enterprise Edge: bank branches, retail stores
• Industrial Edge: factories, automated equipment, robots
• Smart City Edge: self-driving vehicles, in-the-road embedded sensors
• Telco Edge: telecom equipment, 5G data, cell towers
• Tactical Edge: military, cybersecurity defense, ruggedized system uses

Let’s take a look at how we can best manage the explosive growth of data from edge computing.

Also see: Why the Future of Computing is at the Edge

Manage Edge Data with Multiple Partners

The Edge is not a cookie-cutter, repeatable landscape for data storage and data management. That’s another challenge for IT and business leaders planning their 2022 data strategy.

Because the data-management challenge is so complex, multiple partners will be needed to get the job done. Furthermore, leveraging these providers to assist with artificial intelligence (AI) and machine learning (ML) will support automated data discovery and software updates. Manual methods will not be fast enough to keep up with the sheer scale of Edge data changes.

We can see that CSPs and ISVs are already forming more partnerships to address end-to-end data management that extend to Edge locations. Notable partnerships include:

• IBM and Cloudera (IBM Cloud Pak software with Cloudera CDP)
• Equinix and Microsoft Azure
• AWS Outposts and VMware
• Google Distributed Cloud (Anthos) with HPE and NetApp storage
• HPE GreenLake and VMware

Data security and data protection must take place – at multiple tiers of the on-premises infrastructure – and must now extend to the Cloud and the Edge. Not a simple task. For most companies, scaling up data-management requires an end-to-end cohesive strategy, along with a set of consistent best practices, consistent security software deployments and consistent data policies. Without smart management and advanced software tools, distributing data to multiple clouds and all Edge sites would be chaotic.

Simplify Edge Data For Better Management

Data simplification at the Edge is key to avoiding data logjams that would slow down the network. When data transfers are slow – and data-transfers are large – the traffic snarls caused by network “latency” slow the pace of business considerably. That’s why data simplification, and local analytics of Edge data, is critical to good outcomes for data-management at the Edge.

Inventory the data. The first step is data discovery, making sure that all the data silos associated with different types of Edge use are inventoried, and understood, and made available for corporate tracking/monitoring and data management.

Given the spectrum of Edge data scenarios, IT alone cannot do everything for every piece of data – no matter where it lives, including data-at rest, data-in-flight and data being processed. Fortunately, IT organizations have many options for scaling up their ability to do data management at the Edge.

Get unified visibility. IT needs to be in-control, with software tools that can, together, provide unified visibility to data throughout the organization. To do so, they’ll need to have a unified dashboard to show data-monitoring logs across Edge locations – and a customer security “key” that preserves data privacy for remote data – and data traveling to and from the Edge. Many of these security keys are, in fact, “private” keys controlled by the IT customers, rather than software vendors, to ensure end-customer control and enforcement of corporate data-privacy policies.

Also see: Will Edge Computing Devour the Cloud? 

Scaling Up Data Management: Security, Protection, Management

Rapid edge data growth is giving rise to a new wave of data-management challenges that IT organizations need to address now. That’s why data simplification, data analytics, data management, data security and data protection at the Edge are going to be so important in 2022.

These criteria were voiced by Intel CEO Pat Gelsinger in a year-end video sent to Intel employees, saying of the Edge, “the top requirements are AI, low-latency, security.” He also acknowledged the emergence of zettascale computing and zettabytes of data in the 2020s.

So, here are three core areas for IT managers to consider for global Edge data management:

Data Security at the Edge. In the Edge, there is no traditional security “perimeter” to protect, challenging IT to find new ways to secure remote data. End-to-end security, enforced by data encryption, protects against attacks – cybersecurity and ransomware attacks – that brought some companies to a stand-still in 2021. If data security is breached, it may take hours or days for data to be restored – and business continuity to be achieved.

Data Protection at the Edge. There is a mind-set for consistent data protection, requiring an over-arching strategy and a solid implementation to protect data from the Core to the Cloud to the Edge of the network. IT managers will have to ensure careful data monitoring for Edge locations and have multiple scenarios for replicating business-critical data – and envision multiple paths for data to be recovered, if needed.

Data Management Strategies for the Edge. To extend an organization’s management of distributed data, reaching into the Edge, hybrid clouds, multi-clouds and Core locations, IT managers will see many data management software offered by a range of vendors. The specific features will be different, but it’s the IT strategy that matters most when building a data-management plan for the organization. Increasingly, organizations are looking to work with partners in the co-lo and CSP spaces, to extend their reach – and to become more efficient at data management. This is especially true for SMBs with limited IT resources at Edge locations. 

IT’s To-Do List Expands for Edge Management

So, let’s take the long view of where the rapid rise of distributed data is leading us – and how IT leaders can cope with fast-growing data resources at the Edge.

IT leaders must communicate with business leaders. We all have the point-products we need to create the next-generation of our corporate data infrastructure. It’s critical that business leaders and IT leaders will be on the same page – so that major initiatives can be funded in a timely manner, to keep up with our organization’s rapid data growth in 2022. We should do this with an eye to checking the boxes for compliance with data-privacy regulations, across geographies (e.g., GDPR in the European Union and CCPA in California).

“Mind the gaps” in data infrastructure. Look for gaps in your end-to-end data management strategy – and address them, one by one. Speak with business units, data scientists and end-users throughout your organization to identify aspects of distributed data management that simply aren’t working well right now. They will know where gaps are hurting their ability to track data quality – and to analyze the growth of your business.

IT leaders must create a unified strategy. Shop for partners to help with data discovery, data security, data protection and data management. Look to multiple data partners – including software vendors, cloud providers, co-lo operators, telecoms companies and services companies. Their solutions are already useful – as currently implemented – but it’s up to IT leaders to pull together a unified, consistent strategy for data expansion, data management and data security.

What’s Next for Managing Edge Data?

There’s no time to waste. IT managers cannot afford to wait until Q2 to map out its 2022 data-management moves; they must do so now.

Those data-planning meetings – whether virtual or in-person – need to happen right away. Managers should identify all the data that should be logged and monitored at the Edge, and they should gather and analyze all the metadata – the data about the data – to build a more unified, cohesive, and consistent plan for managing the entire corporate data portfolio from end to end.

The post Guide to Managing Edge Computing’s Explosive Data Growth appeared first on eWEEK.

Business intelligence (BI) software tools enable companies to analyze events, trends and market changes to gain competitive advantage.

These BI tools collect, manage and interpret mountains of structured and unstructured data. They construct models that deliver answers and insights through reports, charts, graphics and other data representation methods. BI software is the foundation of digital transformation. 

BI systems incorporate or use various data sources, including spreadsheets, querying software, reporting tools, online analytical processing (OLAP), data mining, data warehouses, and data marts. They usually encompass data extraction, data cleansing, data governance and dashboards or other mechanisms that allow users to view reports and visualizations.

The objective is to support business processes across an organization. These can range from past events, such as how a product or service performed, to predicting future events and scenarios. They can span various departments, including finance, human resources and operations. Increasingly, BI platforms intersect with machine learning (ML) and other artificial intelligence (AI) tools, such as natural language querying. They support advanced data mining techniques.

Not surprisingly, business intelligence—which dates back to the 1970s—continues to advance. Today’s systems span clouds and tap on-premises data sources, and work alongside data mining tools. They also rely on APIs to extend analytics capabilities across business partners and supply chains. Some platforms are also designed to pull data from edge computing deployments, and many platforms also include open-source tools and components that make them more flexible and adaptable.

• • How to Choose BI Software
  • Domo
  • Microsoft Power BI
  • MicroStrategy
  • Oracle Analytics Cloud
  • Qlik Sense
  • SAS Enterprise Guide
  • Sisense Fusion Analytics
  • Tableau Desktop
  • SAP Analytics Cloud
  • Business Intelligence Software: Additional Market Leaders
  • BI Software Tools: Vendor Comparison Chart

How to Choose a BI Software Tool

As you focus on selecting your BI software tools, it helps to focus on five crucial steps:

• Analyze your existing data sources. Obviously, any new solution must ingest your organization’s existing data. Either it will do this directly or you will have map data structures and perform conversions manually. Without knowing what data currently exists and where it resides, it is next-to-impossible to determine what BI platform to use and what’s involved with the transition.
• Identify your objectives. If you’re looking to take your business intelligence and data analytics to the next level, it’s critical to identify the features you need in order to find a good match. For instance, do you want to expand the use of BI to different groups or departments? What types of reporting and data visualizations do you require?
• Determine whether other sources of data can improve business results. A primary reason to adopt a more advanced BI platform is to add features and capabilities. Are you looking to tie together cloud repositories or various enterprise systems? Are you hoping to build out a framework with edge computing companies to expand data collection across a supply chain? These are fundamental questions to ask before selecting a vendor.
• Survey the vendor landscape. Of course, vendors and software solutions approach BI in fundamentally different ways. Is the vendor geared toward on-prem, or are they cloud only? As a result, a critical element in selecting a solution is matching requirements with vendor features, its support framework and its roadmap. It’s important to determine which vendor can unlock the greatest value from your data, support business transformation and continue to add value in the future.
• Understand what changes are required. Any large initiative, including BI, requires changes to both IT infrastructure and business processes. Consequently, it’s essential to match your organization’s needs, desires and requirements with the right BI solution—and ensure that it delivers the ability, flexibility and scalability you need to navigate today’s fast changing business environment.

Top BI Software Tools

Here are 10 of the top BI software tool providers:

Domo

Key Insight: Domo focuses is on tying together data from a variety of sources and building insights through rich reporting and visualizations. The solution delivers sophisticated tools for handling data integration—including through visual ETL—as well as dashboards and mobile BI platforms that use intelligent apps to display analytic insights.

Pros

• Connects to a wide array of data sources and systems and offers powerful data importing and ingestion tools.
• Delivers real-time monitoring and results through robust alerts and notifications.
• Offers a rich and powerful feature set that can be used by data scientists as well as non-technical line-of-business users.
• Powerful dashboard capabilities.

Cons

• Additional apps and other tools and add-ons can increase the price tag significantly.
• Some users find the platform confusing and overwhelming due to a large array of options and choices.
• Lacks some embedded advanced analytics capabilities.

Microsoft Power BI

Key Insight:  Power BI is a sophisticated but affordable platform for organizations of all sizes. It supports self-service analytics for line of business users and data scientists, incorporating AI and ML. Microsoft includes strong collaboration features along with rich reporting and data visualizations. Power BI delivers powerful automation and includes tools to align setup and operation with enterprise scorecards.

Pros

• Powerful data ingestion and connection capabilities combine on-premises and cloud data seamlessly.
• Offers a wide array of pre-built dashboards that connect to various applications, products and services.
• Delivers rich interactive capabilities, including real-time reports and visualizations.
• Provides strong data governance features.

Cons

• The learning curve for advanced analysis can be complex and difficult.
• Graphics and visualizations aren’t as robust as competing BI solutions.
• May be difficult to use with non-Microsoft products and technologies.
• Doesn’t support IOS and other Apple devices.

MicroStrategy

Key Insight: The vendor’s focus is “intelligence everywhere.” MicroStrategy offers tools to build powerful and attractive dashboards that pull data from more than 200 data sources. The solution works on both PCs and Macs. It can deliver services based on roles, devices and numerous other factors. MicroStrategy supports automation and embeds analytics functions directly into applications and websites.

Pros

• Includes powerful features, including a “hyperintelligence” function with strong automation capabilities.
• Provides a strong feature set and extremely flexible BI framework.
• Offers powerful data governance features along with strong security.
• Generates highly rated visualizations.

Cons

• Many users find the platform difficult to set up and use, particularly when handling extremely large data sets.
• The solution can be pricey.
• Lags other platforms with collaboration and social BI features.

Oracle Analytics Cloud

Key Insight: The cloud native BI platform is designed to handle the full spectrum of analytics requirements, from data ingestion and modeling to data preparation, enrichment, visualization and collaboration. It includes embedded ML and AI, including natural language search functionality. Open-source data connectivity with built-in connectors simplify data aggregation tasks.

Pros

• Provides more than 50 out-of-the-box connectors, including Java Database Connectivity.
• Delivers a scalable single source of truth data framework for line of business users as well as data scientists.
• Offers strong ML capabilities through a Python-based framework.
• Provides strong support for mobile devices and includes a powerful data governance and security framework.

Cons

• Users complain that the framework can break, particularly on upgrades. Error tracing can be difficult.
• The interface could be modernized and improved.
• User ratings for technical support and end-user training rank below competitors.

Qlik Sense

Key Insight: The vendor delivers robust BI through a cloud analytics platform. Qlik Sense dashboards connect to hundreds of data sources and support complex and varied use cases. This includes self-service visualizations, interactive dashboards, natural language search, and powerful mobility functions. AI and machine learning support augmented and predictive analytics.

Pros

• Comes with built-in connectors for hundreds of data sources and services, including all major cloud platforms.
• Strong support for both structured and unstructured data.
• Highly rated for ease-of-use, including analytics dashboards and interactive visual exploration features.
• Provides rich and varied reporting and visualizations.

Cons

• Lags behind competitors for collaboration and social BI features.
• Users rate governance and metadata management below other BI solutions.
• The software can present a steep learning curve. Training may be required to use the platform effectively.

SAS Enterprise Guide

Key Insight: SAS Enterprise Guide is a Windows .NET client application that offers a self-service framework for collecting, managing, manipulating and formatting data into reports and visualizations. It relies on a point-and-click, menu-and-wizard-driven framework that includes the ability to write code and use custom macros. Thus, developers and other coders can use existing programs to create stored processes, and then run them on demand or port them to external apps, such as Microsoft Excel.

Pros

• Delivers a highly customizable BI framework through a well-developed coding platform and macros.
• Strong IT-managed role-based security along with powerful data governance features.
• Handles huge data sets while delivering fast performance.
• Strong support for Microsoft Office, including Excel.

Cons

• Overall complexity can present challenges for non-technical users and those with limited coding knowledge.
• Expensive and sometimes confusing price structure.
• No support for Python or R tools.

Sisense Fusion Analytics

Key Insight: The platform delivers a highly scalable and flexible self-service framework for conducting enterprise grade BI. The vendor’s focus is on infusing “intelligence everywhere” through extended data discovery in the cloud and on-premises, dashboards and integration APIs. Sisense offers strong AI and ML support, and powerful data visualization capabilities.

Pros

• Offers data connectors for almost every major service and data source.
• Delivers a code-free experience for non-technical users, though the platform also supports Python, R, and SQL.
• Provides a wide array of templates that automate BI and embed actions into applications and processes.

Cons

• Can present a steep learning curve.
• Users complain that errors and problems can be difficult to trace.
• Some complaints about bugs and stability issues.
• Mobile device support not as strong as others in the BI space.

Tableau Desktop

Key Insight: Tableau has emerged as a leader in BI and analytics with a framework that supports rich visualizations and storytelling. It offers appealing and highly customizable dashboards, and it can accommodate huge data sets. Tableau Desktop connects to hundreds of data sources, both on-premises and in the cloud. It includes drag-and-drop functionality along with support for AI and ML.

Pros

• Delivers outstanding visuals.
• Strong data integration and automation features.
• Includes powerful data management and presentation features.
• Supports most types of devices and operating systems, including all major mobile platforms.
• Offers 24/7 support.

Cons

• Steep learning curve for setting up data frameworks.
• Expensive, with complex and inflexible pricing model.
• ETL capabilities are not as advanced as other BI and analytics platforms.

SAP Analytics Cloud

Key Insight: SAP combines BI, augmented and predictive analytics, and planning capabilities into a unified cloud environment. The solution serves as the analytics layer for SAP’s Business Technology Platform. It’s designed for both line-of-business users and data scientists. It incorporates machine-generated insights and analytics that are designed to link and create operational and financial plans—and deliver deeper insights.

Pros

• Highly rated by users for performance and reliability.
• Excellent user interface and high quality visualizations.
• Powerful automation features and strong data integration from numerous sources.
• Strong global language and currency support.

Cons

• Initial setup and data ingestion process can be challenging.
• Does not support on-premises applications and data for desktops and mobile devices.
• Expensive.

Tibco Spotfire

Key Insight: The self-service BI and data visualization platform incorporates a visually appealing interface along with strong AI features to deliver advanced data search capabilities. This includes natural language search, AI-driven recommendations and direct manipulation of data. The platform is designed for line of business users and data scientists. It includes more than 60 native connectors and extensive support for APIs.

Pros

• Extremely flexible and highly customizable.
• Supports extremely large data sets.
• Offers excellent data visualization tools and options.
• Highly rates customer and technical support.

Cons

• Lags behind competitors in mobile exploration and authoring.
• Collaboration and social BI tools are not as advanced as others in the BI space.
• Presents a steep learning curve. 

BI Software Tools: Additional Market Leaders Birst

The analytics provider focuses on pre-built industry and role-specific content and metrics to power BI throughout the enterprise.

Google Charts

Google’s free data visualization tool works with JavaScript to assemble data and generate presentations and reports.

HubSpot

The CRM data platform addresses marketing through customer insights. It features an appealing interface and robust analytics tools.

IBM Cognos Analytics

IBM delivers rich BI insights through reporting and data visualizations. The platform taps Watson AI and machine learning for predictive analytics and other features.

KNIME

The free, open-source data analytics solution supports data integration, modeling and visualization capabilities.

RapidMiner

The platform generates a wide variety of data insights through AI, including predictive analytics.

ThoughtSpot

Delivers AI-driven analytics through powerful data ingestion along with dashboards and visuals.

Xplenty

The platform supports ETL and reverse-ETL functionality within a highly scalable BI and analytics platform. It delivers strong compliance and security features.

Zoho

The self-service BI tool delivers robust uses powerful connectors and intuitive dashboards to deliver reports and visualizations.

 Business Intelligence Software Tools: Vendor Comparison Chart

Analytics Tool

Pros

Cons

Domo

Powerful data ingestion and excellent dashboards

 

May require add-ons

Microsoft Power BI

Excellent interactivity and top-notch visualizations

 

Not ideal for use with non-Microsoft applications and products

MicroStrategy Platform

Powerful automation; highly flexible

 

Expensive, interface can be challenging

Oracle Analytics Cloud

Suitable for non-technical users; strong AI and data governance

 

Interface isn’t as modern as some users would like

Qlik Sense 

Powerful and versatile platform with excellent connectivity to data

 

Lacks in collaboration and social BI; steep learning curve

SAS Enterprise Guide

Highly customizable; excellent governance and security features

 

Complex and can be expensive

Sisense Fusion Analytics

Outstanding data connectivity; offers numerous templates that automate BI

Steep learning curve

 

Tableau Desktop

Outstanding UI and UX; excellent visualizations

Expensive; could use more ETL features

 

SAP Analytics Cloud 

Excellent performance and reliability. High quality UI and rich visualizations

Setup can be challenging; expensive

 

The post BI Tools 2022: Best Business Intelligence Software appeared first on eWEEK.

The pandemic focus on work from home in 2021 put the spurs to cloud migration for many businesses, which had to migrate to the cloud almost overnight to support remote workforces. As a result, teleworking has become cloud computing’s killer app during the lockdown – but cybercriminals know this, too.

Clearly, hackers will keep targeting cloud environments as long as they see profit potential in those attacks, so they will be doubling down on targeting cloud infrastructures in 2022.

With that in mind, here are five cloud security developments to expect in the year ahead, and a few suggestions to tackle the challenges they bring.

Also see: Tech Predictions for 2022: Cloud, Data, Cybersecurity, AI, and More

Cloud Security Trends in 2022 1. Supply Chain Attacks Will Rise

The now year-old Solarwinds attack, where cybercriminals breached thousands of customers by inserting malicious code into software updates, was the canary in the coal mine. Expect hackers to target weak links in the supply chain as a way to breach the cloud and attack better-protected assets.

Tip: Cybersecurity defenders should focus on cutting off attackers’ ability to move laterally by reducing entitlements and privileges for all internal and external identities (including machine identities) that are allowed to access cloud resources

Also see: Top Cybersecurity Companies

2. Cloud Breaches Will Be a Fact of Life

McKinsey & Co. has noted that the pandemic has forced many enterprises to leapfrog years in their use of the cloud during the lockdown period. But it hasn’t been a smooth transition, and security has suffered in some cases; 40% of companies have suffered a cloud-based data breach. In 2022, expect every organization to endure at least one cloud breach.

Tip: Businesses should focus on reducing their cloud attack surface and containing the blast radius if a hacker breaches their security controls. They should invest in tools that provide visibility into cloud identities and give defenders a clear picture of the potential damage that could follow a breach so they can respond quickly and connect with all the stakeholders.

3. Cloud Maturity Will Make a Difference

Companies that are in the early stages of their cloud migration are at a greater risk than those that are optimizing their existing infrastructure. Expect those organizations in the building stages of cloud development to suffer more breaches.

Cloud-native companies have an advantage here, since they typically have a better understanding of the complexities associated with managing the security posture of cloud infrastructures to protect their data and assets, rather than relying on platform providers to step in.

Tip: Cybersecurity professionals should focus on building security controls for identities, access and configurations into their cloud migration roadmap at every stage.

4. Zero Trust Picks Up Speed

“Zero trust” is quickly becoming a buzzword that threatens to lose its meaning, but it hasn’t lost its importance. Having a consistent, uninterrupted security policy guarding who can access data and resources and enforcing least-privilege access is still the key principle for protecting cloud infrastructure. So expect initiatives to leverage zero trust architecture to keep catching on with enterprises.

Tip: Organizations that want to enforce zero trust and least privilege in the cloud need to manage network policy, identity and access permissions, and resource configurations. They need to put practices into place that provide comprehensive visibility into all three to better manage the threat.

5. Machine Identities Will be an Achilles Heel

Organizations have become wiser about improving security among their users by leveraging multi factor authentication (MFA) and single sign-on (SSO) to prevent credential abuse and mishandling. As more organizations improve security on the human front, cybercriminals will look to open new fronts by targeting machine and service identities. These have already been exploited in almost every cloud breach to help attackers access data or move laterally within systems. This is primarily because machine and service identities are created by developers to enable other functions and that are outside the wheelhouse of cybersecurity.

Tip: This needs to change. Faced with a shortage of cyber security talent, organizations need to leverage tools that can serve as a force multiplier for security staff. Automation can help with cybersecurity by identifying and prioritizing the risks connected to machine identities and their entitlements, as well as automating the remediation of those risks.

The remote work genie is refusing to go back in the bottle. Even after the pandemic becomes a memory, employers expect a number of their staff will remain remote in some manner. Meanwhile, cloud usage will continue to accelerate and so will attacks against service provider platforms and the data they contain. The year 2022 needs to be the year for getting cloud security basics right.

About the Author: 

Shai Morag is the CEO of Ermetic

The post 5 Cloud Security Trends in 2022 appeared first on eWEEK.

I spoke with Mark Porter, CTO, MongoDB, about trends in the database market, and how the so-called ‘Innovation Tax’ hinders the ability to produce outstanding tech solutions.

Among the topics we covered: 

• Let’s take a look at the database market as we enter 2022. There’s a lot going on in the data and data warehouse market: Snowflake, Amazon competing aggressively, etc. What do you see as a couple of trends defining the database market in 2022?
• The Innovation Tax: The idea that clunky processes and outdated technologies make it harder for engineering teams to produce excellent tech that delights customers. How can companies strategize for this?
• You’ve written an interesting blog post about engineering teams, and how there’s a “Goldilocks Gauge” of just the right size. Please explain.
• The future of databases over the next several years? What do you see?

Listen to the podcast:

Watch the video:

The post MongoDB’s Mark Porter on Database Trends and the ‘Innovation Tax’ appeared first on eWEEK.

Cisco is bringing its Webex technology to the final frontier: space. Webex will be a part of the Callisto payload on the Orion spacecraft, which launches later this year on a mission called Artemis I.

Formerly known as Exploration Mission-1, this launch will be the first in a series of several complex missions focused on human exploration to the Moon and Mars.

Also see: Cisco Launches New Hardware and Software at WebexOne

Orion Will be Voice and Video Enabled

The mission is a partnership between NASA and three key vendors: Lockheed Martin, Cisco, and Amazon.

Lockheed Martin is the main contractor for NASA; it built the Orion crew module, crew module adaptor, and launch abort system. The Callisto payload aboard the Orion will contain an iPad equipped with Cisco’s Webex to enable voice and video in deep space. It will also include Amazon’s Alexa cloud-based voice service to provide astronauts with companionship and information.

The project has been in the works since 2018, and is expected to finally make its debut this spring or early summer. This effort will allow NASA and its tech partners to test technology already used on Earth in space, to improve the lives of astronauts, said Aruna Ravichandran, VP and CMO for Cisco Webex Collaboration, in an interview with ZK Research.

Artemis I Will Test the Limits of Collaboration in Space

Artemis I will be a 40-day space mission, focused on simulation. The Orion won’t have humans onboard during the first mission, but rather a simulation of astronauts using Webex to communicate with the Mission Control Center at NASA’s Johnson Space Center in Houston.

A VIP room at the center will house technology for this mission, including WebEx Board Pro and the WebEx DeskPro. Cisco’s goal is to test two uses cases for Webex in space: videoconferencing and whiteboarding.

“Testing these technologies will ensure that when real astronauts go to the Moon, this will make it easier for them and it will be a successful mission,” said Ravichandran.

First, Cisco will facilitate communication between DeskPro and an iPad on Callisto, running Webex video meetings. Cisco expects latency to be a major challenge during the mission, given that the spacecraft will be more than 240,000 miles away from Earth. To address this challenge, Cisco built a proprietary blend of next-generation adaptation and resilience technology, which builds in buffering to deal with delays in deep space.

Additionally, Cisco will demonstrate how astronauts can create, collaborate, and brainstorm when they’re in space using WebEx Board Pro. Astronauts will be able to whiteboard on the Orion and relay it back to the Mission Control Center.

Also see: Compliance vs. Security in Enterprise Networking: the Cisco Approach

Video Collaboration Pushed to the Next Level

“Collaboration on Earth has become table stakes. We couldn’t have survived without collaboration technologies during the pandemic,” said Ravichandran. “We’re taking it to the next level with NASA’s Artemis I mission. The first mission is all about testing the technology and mastering latency.”

In addition to helping astronauts perform their jobs, Cisco wants to enhance their quality of life. Astronauts on missions often experience loneliness and isolation. Equipping astronauts with videoconferencing tools will allow them to stay connected to family and friends.

Video communication in space has always been problematic due to distance and the challenging nature of the environment. It’s common for communication systems to lose data after just milliseconds of delay. That’s why testing buffering technology developed by Webex engineers will be an integral part of the mission.

A Custom AV1 Codec: The Ultimate Video Transmission Technology

Cisco will also address constrained bandwidth in space using a customized version of the AV1 codec to greatly improve video quality. This will ensure transmission of the highest resolution video at 128 kilobits per second (kbps). While AV1 is not widely available in Cisco products yet, the vendor is customizing its technology to implement AV1 on the Orion.

This might be one small step for collaboration but it has the potential to be one giant leap for videoconferencing. Traditional video systems do not deal well with latency. They either drop packets, causing parts of the picture to go missing, or drop the connection completely. Video transmission was never designed for an environment like space.

Cisco is using AI technology to reconstruct video and audio to make decisions around what to send or not send. For example, instead of sending the full image, use lower quality for the background and maintain higher quality for the person’s face.

Lessons learned during the mission could be applied to use cases here on Earth, such as boosting bandwidth utilized by Internet of Things (IoT) devices in the field across industries. Ultimately, these advancements could remove the boundaries for hybrid work, said Ravichandran.

AV1 has been in the works since 2015 but is nearing standardization and could see deployments in areas where bandwidth isn’t plentiful. An example use case is what Cisco is currently working on: bringing video to low bandwidth, high latency environments. And this effort would also allow HD video to be delivered to places where it’s not possible with the use of standard codecs.

“This is a big area of focus for us at Cisco. We want to be the leaders in defining hybrid work,” Ravichandran concluded. “Now we can take it a step further to the final frontier by enabling video communication and collaboration in space.”

Also see: Digital Transformation Guide: Definition, Types & Strategy

The post Cisco, Lockheed Martin and Amazon Partner to Bring Collaboration to Space appeared first on eWEEK.

The new solutions Dell Technologies introduced at CES 2022, along with three new workplace Concepts announced in mid-December, are good examples of products that help employers and employees address business and marketplace shifts. That is especially important for products used in the workplace, including endpoint PCs and laptops.

Let’s take a look at what’s new and improved among these endpoint products.

Also see: Top Edge Computing Companies 

CES 2022: Dell Reimagines XPS 13 and Collaboration

Launched originally at CES 2012, Dell’s XPS 13 has since become one of the company’s most best selling laptops, and for good reason.

That first generation (L321X) was Dell’s initial Ultrabook, a class of laptops that utilized a host of new technologies and features to deliver significantly better compute, battery and display performance. The XPS 13 also fundamentally altered the company’s reputation for product design and aesthetics. The XPS 13 wasn’t just a new laptop line—it was a signpost indicating Dell Technologies’ future direction.

Since then, next gen XPS 13 solutions have steadily highlighted new features and technologies developed by Dell and strategic partners, like Intel, and the 10th anniversary XPS 13 Plus introduced this week at CES 2022 continues that progression. This is the first XPS 13 to incorporate 12th gen Intel Core processors.

However, new Dell fan technologies (increasing airflow by 55 percent without raising noise or temperature) enable the XPS 13 Plus to use 28W Intel Core chips (up from 15W in prior gen systems), thus substantially improving overall performance.

Along with streamlined design changes that reinforce the XPS 13 line as a solid choice for use in the office or home, the new XPS 13 Plus offers practical enhancements, including Express Charge 2.0 (which brings the laptop up to 80 percent battery life in under an hour), larger (zero-lattice) keycaps, a glass touchpad, improved InfinityEdge display and redesigned quad speaker system. The XPS 13 Plus also meets the social impact plans in Dell 2030 Goals.

Dell’s new UltraSharp 32 4K Conferencing Monitor also offers new and improved features, as well as a sophisticated design aesthetic that fits both home and workplace use cases. The monitor includes Dell’s UltraSharp Webcam (with SafeShutter), echo-cancelling dual array microphones and 14W speakers, enhancing both collaboration and visual experiences.

The UltraSharp’s IPS Black technology and VESA DisplayHDR 400 features enables content to be viewed in 4K resolution, contrast, and color, and ComfortView Plus reduces harmful blue-light emissions without affecting color accuracy. Like other Dell video conferencing monitors, the new UltraSharp is certified for Microsoft Teams and comes with privacy and productivity features.

Both the XPS 13 Plus and UltraSharp 32 4K Conferencing Monitor will be available for purchase worldwide in spring 2022.

Also see: 5 Edge Computing Trends in 2022

Dell Concepts for Seamless Work Experiences

While the new solutions launched at CES 2022 address current issues and needs, Dell’s vision of the workplace of the future is apparent in the Seamless Work Experience Concepts outlined last month in a blog by Glen Robson, CTO of Dell’s Client Solutions Group:

• Concept Flow – is designed to remove the productivity-sapping friction workers can experience as they transition from location to location. To address that, Dell engineers are marrying devices, industry standard wireless charging technology, intelligent software applications and Wi-Fi 6E docking technology to create a singular, seamless proximity-sensitive user experience. As a result, workers will be able to get up and running faster and with fewer steps and headaches than they currently do.
• Concept Pari – Teams and team members often face communication and collaboration barriers when using video conferencing tools. To improve that situation, Dell engineers studied the impact of cameras and camera placement on communication and developed a small (~ 1 ounce/30 grams), simple, moveable wireless webcam with built-in microphone that can be placed and positioned virtually anywhere. Using the webcam, users can more easily maintain direct eye contact with those they are addressing and share offscreen content and images. While designed mainly for people who regularly engage in video calls, Concept Pari could be a worthwhile asset for anyone who regularly transitions between various work locations.
• Concept Stanza – Finally, traditional work activities like notetaking can also be improved with new digital tools. Concept Stanza aims to do that by providing workers a thin, light, port-less, 11-inch companion device for PCs. Designed to reduce distractions, using Concept Stanza is similar to handwriting notes but double tapping a note instantly converts it to text that can be saved, retrieved, searched and shared across multiple devices. Designed to be used with either a stylus or microphone, Concept aims to blend analog and digital processes, thus enhancing overall workplace productivity

Also see: Digital Transformation Guide: Definition, Types & Strategy 

Addressing Business Needs, Planning Innovation

The new Dell XPS 13 Plus and UltraSharp 32 4K Conferencing Monitor launched at CES 2022, along with the Seamless Work Experience Concepts announced last month, highlight how the company is effectively addressing business customers’ current requirements while planning innovative future products and tools.

What is clear from examining these new and potential offerings is Dell Technologies’ astuteness in balancing the market’s thirst for valuable new solutions and the need to continually evolve its portfolio of workplace endpoints.

The post Dell Technologies at CES: The Importance of Endpoint Evolution appeared first on eWEEK.

As companies increasingly move to cloud architectures, they’re looking for ways to increase security while also reducing complexity and operating costs. And of course the move to remote work – which boosts the need for hybrid cloud and Software as a Service – only accelerates the need for these requirements.

All of these requirements can be addressed by an emerging tech that’s high on the current hype cycle for enterprise security: Secure Access Service Edge, or SASE. The benefits are outstanding: SASE is an architecture that combines networking concepts like VPN and SD-WAN, along with security concepts like Zero Trust (another compelling but often over-hyped concept) and contextual access.

But at this point, SASE represents more of a goal or concept for most organizations than a product. Nevertheless nearly all the major vendors have seized on this concept (e.g., Cisco, Palo Alto Networks, Zscaler, Akamai, McAfee, etc.) and labeled many of their components as critical to achieving SASE.

But as attractive as the concept sounds, in practice there are many hurdles companies need to overcome in order to turn SASE hopes into reality.

Challenges of SASE Deployment

In broad terms, SASE is the integration of network services with security services to make user access, SaaS and multicloud functions fully secure. It includes capabilities inherent in SDWAN deployments, like path resiliency and redundancy. It also offers app routing, visibility and reporting, vendor-specific software defined capabilities, and VPN.

SD-WAN, which helps virtualize networks and their operations, is deployed widely, but does require that enterprises replace older single function switches and routers with new equipment. This virtualization of networking has been going on for several years, and the majority of enterprises have virtualized most of their networks, or at least a major portion.

This is a critical step, as SASE requires a virtualized network in order to be implemented. Further, the public cloud networks are all virtualized so heavy users of these services have a built-in advantage.

The more difficult part of realizing SASE is the need for a security architecture that can be fully integrated and managed, much like software-defined networks are. But the majority of enterprises currently have a hodge-podge of security products in place that are nearly always stand-alone.

In fact, some organizations have dozens, if not hundreds, of unique security apps running on everything from their data centers or cloud instances, to networks, to hardware endpoints, and to individual apps. Even the ubiquitous VPN necessary to securely connect over networks may not be cross-compatible with all the devices and servers within the organization. And with an increasing number of network options available (e.g., 5G, WiFi 6, broadband), this represents its own compatibility challenges.

SASE requires a uniform method of policy management, secure access, threat protection and device management in order to be fully implemented. And with so many security components in place that generally do not play well tougher, this is a daunting task.

One final obstacle to overcome is organizational. In most enterprises, networking and security operations are unique groups, not necessarily always in close contact. For SASE to be fully implemented, both network ops and security ops must be on the same page and working together. Without this interaction, implementing SASE isn’t possible.

Implementing SASE Requires Major Integration

Despite what many vendors promote when selling their products, achieving SASE is really a major integration challenge. And many smaller organizations do not have the resources to make this happen, even if they do have some components already in place (e.g., SD-WAN, cloud access gateways, etc.).

Even larger firms will have difficulty making sure their entire network and security tools and cloud management products are able to share information and be managed through a single interface. While it’s possible to have a SASE implementation without a universal control plane or single pane of glass console, the cost of such a solution is much higher in skills required, people required, and time spend compared to a single management interface. It’s also problematic in that it’s much easier for problems to arise due to a lack of visibility when non-compatible systems are manually managed.

For many companies, the best path forward to SASE is to find a systems integrator that can not only integrate the necessary tools but also can manage the day-to-day operations of their SASE architecture implementation. However, enterprises should be aware that SASE is a “moving target,” in that few companies have all the components in place, and even ones that do will likely face upgrades and changes to infrastructure over time as capabilities mature.

Network operators (e.g., Verizon) have created SASE practices that can provide “SASE as a Service” on top of their connectivity and security service operations, but enterprises will still need to manage the relationship and devote resources to ensure the latest updates in network and security are fully implemented as business needs and infrastructure changes take place.

Further, service providers will have their own set of preferred partners that may not be compatible with the vendor products the enterprise currently has in place. Still, this may be an advantageous path, as these operators can leverage both their ability to influence vendor apps, as well as their learned experience in making SASE work effectively.

Market Consolidation Needed before SASE Maturity

SASE as a concept has a lot of merit in that it can significantly improve the security posture of organizations, particularly in a cloud-heavy world in which we now reside. But achieving the realization of SASE is not as straight forward as many vendors’ hype makes it sound.

Indeed, we expect it will take at least 3-5 years before the SASE market settles on what the architecture really looks like and which vendors will be at the forefront (with market consolidation removing many players from the field in the interim).

Enterprises should definitely be evaluating the SASE architecture as a way to increase security, but must also be aware that current products may not be the end products in place in the future, and therefore deployment flexibility will be required.

The post Secure Access Service Edge: Big Benefits, Big Challenges appeared first on eWEEK.

Cloud security professionals are already in short supply. Combine that with alert fatigue, a surge in demand, and the “always-on” nature of the modern workplace, and it is clear why employee burnout is increasing.

In 2019, Gartner projected that the cloud market would grow to $355B by 2022. Reaching that level depends on the development and advancement of cloud-native security solutions, which puts tremendous pressure on cybersecurity organizations and the employees developing them.

Understaffed Security Operation Centers (SOCs) eventually reach a breaking point, which typically leads to additional exits or oversight – and can lead to costly breaches.

These factors create a negative feedback loop within SOCs. Workloads increase for those who stay on, and the quality of work suffers, which yields further burnout and resignations and the increased potential for more customer breaches. 

 So, how does a cybersecurity organization get out of this negative feedback loop?

6 Ways to Avoid Cyber Burnout Strong Security Leadership

As a security leader, particularly those who oversee Infosec teams and functions, you have a fiduciary duty to fight internal battles and deliver a message that resonates with internal stakeholders. You must persuade employees to think long-term and understand the consequences of slowed productivity or inadequate quality of work.

Many security professionals have achieved recognized technical expertise, but the leader of the security group must also be able to effectively communicate the risk the current security staffing level poses to the organization. They must ensure that this risk is within the risk tolerance of the company.

Addressing these problems might require building relationships outside the technical groups because, ultimately, securing additional resources means the company may not fund something else. This also might require focus on other skills such as communication. It’s easy to de-prioritize these soft skills when the technical skill might be more fulfilling.

Effective Hiring and Mentoring

Leaders should focus on effective hiring and mentoring. Some security teams have a haphazard approach to hiring, but to attract and retain the necessary talent, leaders must inhabit the role of a manager entirely. If the transition from being a “doer” to a hiring manager is not complete, teams may suffer because leaders select the wrong person or don’t satisfy a new employee’s particular aspirations, which results in the new hire not being fully productive. 

Many security teams are small and because of this, there may be less focus on being an effective hiring manager and an effective manager overall – the technical demands are simply too pressing. 

Treat talent acquisition as a team sport. Everyone on a security team should know about skill and resource gaps and tap their networks for the best possible hires.

Prioritize Ability

Hiring managers, talent acquisition, and people operations professionals should actively make tradeoffs wherever possible. Ability can—and should—trump pedigree and years of experience.

Open your criteria and seek talent that can demonstrate results versus the ideal education, previous employment, or years of experience. This strategy widens the candidate pool and levels the playing field for candidates from all walks of life who are passionate about cloud security and want to make it their life’s work.

Enable Great Work

Enable the best work possible from your staff. Hiring new cybersecurity and cloud computing professionals is just the beginning. It is critical to have clear deliverables and a metric-based understanding of what success looks like for a SOC role during the onboarding phase.

Communicate this to the new hire and the incumbent team. After all, the team cannot help a new hire succeed—and improve their own circumstances—if the goalposts are always moving.

Support Continued Learning

CISOs and people operations professionals should encourage and build into the employee guidelines an infrastructure that supports continued learning and development. Cloud security evolves daily, and skill stagnation could be the poison pill that kills a team’s productivity and effectiveness.

Consider Vendors or Partners

Security teams are inclined to action and, therefore, have a tendency to build a feature that solves a problem on their own. That’s because it’s often easier (and faster) to tackle the problem rather than to buy a product or a service.

But by accepting help from a vendor or partner, leaders can become comfortable evaluating a product or service, negotiating the right terms, and then effectively monitoring the deployment of the product or service. This frees up time for teams to focus on other projects and avoid burnout.

Conclusion: Be Willing to Invest

Invest in your team and you will reap the rewards. Perks like lunches or gym memberships are great, but if companies don’t do the hard work of meaningful investment in employee development, retention will continue to be an issue. Free burritos will never make up for an employee who feels that their job satisfaction and career development are stagnant or, worse, in jeopardy.

Also see: Digital Transformation Guide: Definition, Types, and Strategy

About the Author: 

Ani Soghomonyan, Head of People & Talent, Britive

The post Avoiding Cybersecurity Staff Burnout: Keeping Employees Productive and Engaged appeared first on eWEEK.

Discussions about enterprise systems often focus on traditional RAS (resiliency, availability, scalability) performance features, with nods toward developing issues like protecting businesses against rapidly evolving security threats.

But a vital point that doesn’t get nearly as much attention is adaptability—that is, how best and effectively businesses can integrate existing systems with new, forward-thinking strategies and efforts, like hybrid cloud and application modernization. 

Adaptability is especially crucial when it comes to enterprise solutions, like IBM’s Z mainframe offerings that support core, business-critical applications and processes. As a result, the company’s new IBM Z and Cloud Modernization Center announcement is worth close attention. 

Also see: Top Cloud Service Providers & Companies

Inside the IBM Z

Exactly what is the IBM Z and Cloud Modernization Center? As the company notes in its announcement, the Center is a one-stop digital interface where clients can schedule briefings, connect with partners, seek and join workshops, speak with domain experts, and find information and resources related to application modernization and hybrid cloud. 

In essence, the Center is designed to help customers fully realize and extend the value of their IBM Z environments with new workloads. It achieves this by working with clients to co-create and execute roadmaps to modernize existing apps and data, extend those assets to hybrid clouds, develop new cloud native apps, and fully integrate them with existing apps and data already supported on IBM Z.

Is this important to the company’s customers? Apparently so. According to a July 2021 IBM Institute for Business Value survey, 71% of respondents said mainframe applications were central to their business strategies. Plus, four of five survey participants said rapid adoption of transformational efforts, including mainframe application modernization and adoption of open technologies, were vital to remaining competitive. 

Similarly, another IBM Institute for Business Value survey found that hybrid multi-cloud technologies and operating models delivered significantly higher value than single cloud vendor/platform or public cloud-only approaches. 

Considering the key roles that IBM Z mainframes play in many of the world’s largest finance companies, banks, retailers, airlines, and other enterprises, the IBM Z and Cloud Modernization Center could not arrive at a better time. 

The Importance of IBM Partners

While IBM consulting professionals and teams will lead many of the services and offerings provided, the practical and strategic value that IBM’s partners add to the effort cannot be overemphasized. The ecosystem of IBM partners involved in the Center’s efforts include:

• Services: Systems integrators with offerings and competencies in in-place modernization include Capgemini and Deloitte Consulting LLP, with more expected to be announced in the coming months.
• Technology partners: Software vendors and managed service partners including Episode Six, Fiorano Software,Fujitsu Limited, Software Business Unit, HEXANIKA, Illumio, Luxoft, a DXC Technology Company, MuleSoft, Pennant Technologies, Software AG, Suntec Business Solutions, and Zafin.

IBM itself will provide domain-specific expertise, including IBM Systems Elite methodologies, demos, trials, minimum viable product (MVPs), workshops, and other point services to accelerate digital transformation. Accelerators include system reference architectures and patterns, code snips, sandboxes, and other technical solutions. 

The company will also offer links to IBM’s Skills Academy to help architects and developers with application modernization techniques and a reference center highlighting relevant client use cases, analyst papers, and other materials.

Also see: Why Cloud Means Cloud Native 

How Significant Is this News? 

Practically speaking, does the IBM Z and Cloud Modernization Center offer enterprise customers anything new or unique? Plus, how big a deal is it, anyway? In short, no and yes and pretty big overall. 

At one level, enterprises can and do develop and launch individual application modernization and hybrid cloud projects, sometimes alone and sometimes with the help of trusted vendors and service integrators. The problem is that while many of those efforts succeed, many others don’t. Partly that is due to the sheer complexity that such projects engender. But another point is the often confusing or contradictory narratives offered by some vendors and experts. 

In stark contrast, IBM’s deep expertise in Z mainframe systems and hybrid cloud, as well as its decades of experience in consulting services alone and with enterprise-focused strategic partners, places the company in an ideal position to vet, manage, and deliver the services and offerings provided through IBM Z and Cloud Modernization Center. 

Could large enterprises chart similar paths and develop similar solutions on their own? Maybe so. Can they do it as easily or with outcomes as predictable as those offered by IBM and its strategic partners? That seems less likely.

Overall, these new services and offerings should help IBM’s enterprise customers develop reliable new solutions and replicable processes for modernizing applications and utilizing hybrid cloud infrastructures. Just as important, the company and its partners will provide customers the means to adapt IBM Z for new forward-thinking business processes and use cases. That will aid customers in extending their often-substantial mainframe investments in both the short and long terms. 

The post IBM Z: Helping Enterprises Adapt to Cloud Modernization appeared first on eWEEK.

Every December, I spend time thinking about where the software ecosystem will go over the year ahead. For the past decade, that’s meant focusing on cloud computing, as it’s long been the dominant software trend. 

The past year, however, (and 2020 as well) has been rather different—driven, of course, by COVID-19. The pandemic and its effect on health, working practices, and consumption patterns has significantly changed the role of technology. And to my mind, permanently altered how technology will be used in our society and economy going forward.

Here are five trends to look for in the tech industry in 2022.

Also see: Top Cloud Service Providers & Companies 

1) Digital Transformation Drives Chip and Software Production

The cliché that COVID-19 accelerated digital transformation, compressing 10 years of growth into a single year, contains a profound truth—that our economy is shifting toward a software-centric operational model.

This was clearly demonstrated by all the measures companies took to respond to the shift of a large part of the workforce from office to home. The obvious beneficiaries of this were video conferencing and virtual event companies. Other beneficiaries were grocery chains, whose revenues skyrocketed as people stopped going to restaurants; many of these chains rolled out new applications to support huge online ordering volume, delivery, and curbside pickup. All of these examples show how software became the key enabler of business processes.

However, digital transformation is moving well beyond online interaction supporting analog transactions. Software is now being placed into physical products, transforming them into software-centric devices with functionality driven by digital interaction.

Software is now being placed into physical products, transforming them into software-centric devices with functionality driven by digital interaction.

Nothing symbolizes how physical products are becoming digital offerings with atoms attached than the automobile industry. After the shutdowns of 2020, auto buyers came out in force in 2021—and ran directly into low availability caused by shortages of critical computer chips.

So painful is this to Ford that it entered into a strategic partnership with Global Foundries to ensure future availability. This marks a significant shift for the company which, like most auto manufacturers, used to treat chip procurement as a low-value activity best pursued in an arms-length buyer/vendor fashion.

The chip shortage highlights that cars now depend on digital processing, from engine management to suspension response right through to user interaction—all of them and a hundred more auto features rely on digital processing, which means chips.

It also means software because, after all, the chips are only useful insofar as they enable applications controlling all those features to operate. In turn, this means that writing software has become a critical prerequisite for an automaker to compete in the next decade.

The auto industry is one vivid example of how software is moving into a central role in products, but this shift is occurring in every industry. 2021 represents the inflection point of the digital transformation S-curve, which heralds dramatic growth and impact of an emerging technology.

Whether software replaces, complements, or infuses its products, every company must come to grips with how it will pursue digital transformation. The importance of this issue cannot be overemphasized: This will soon be an existential question for companies, and failing to get this right means a dim future for those unable to succeed.

2) Applications Get Dynamic 

The new breed of digital-forward applications break all the assumptions underlying traditional applications: they were thought to require predictable load, limited user population, well-understood infrastructure scale requirements, and reliable hardware.

Digital-forward applications can experience widely varying loads driven by unpredictable usage patterns and even more unpredictable user populations. One COVID-driven example cited by McKinsey & Company includes a fast-casual-restaurant chain which saw its online orders jump from 50,000  to 400,000 per day.

Because of this, it’s difficult to predict just how much infrastructure will be required to maintain application availability and performance, which therefore requires the ability to add and shed computing resources quickly.

In turn, this means most of the legacy application development and operations processes are made obsolete. They were designed for a world of predictability and infrastructure rationing, and imposing heavyweight processes on infrastructure access was seen as a feature, not a bug.

Finally, these digital-first application requirements mean they will be deployed into infrastructure environments that can support them—which means public cloud environments. This destroys any assumption about infrastructure reliability because as one cloud provider’s CTO noted “everything fails, all the time.”

The net-net of this is that digital transformation developments in application characteristics forces cloud adoption, which implies adoption of cloud-native application practices.

For enterprises, this means they must adopt the application practices of the cloud-native companies, such as frequent application updates, automated processes, and resilience through redundancy and easy failover. The upskilling this will force into enterprise IT shops will be a key issue for them, as it will require changes to talent recruitment and retention well beyond what most companies have in place for what has traditionally been viewed as a low-impact cost center.

3) Hyperscalers’ Continued Revenue March – Up and to the Right

Given the enormous shift toward digital transformation, what is the likely consequence?

One obvious consequence is that this will boost the growth of hyperscale cloud providers because the de facto deployment location for cloud-native applications is a public cloud environment.

It is estimated that hyperscalers’ core growth total addressable market (TAM) is somewhere between three and five trillion dollars, which is well beyond what most people estimate.

It is estimated that hyperscalers’ core growth total addressable market (TAM) is somewhere between three and five trillion dollars, which is well beyond what most people estimate.

However, this TAM is based on the current spend of traditional IT practices, which deploy applications into on-premises data centers. Those practices are full of friction and require substantial up-front capital investment, both of which serve to dampen adoption. Most organizations find infrastructure procurement such a burden they pursue only the most obvious, highest-priority use cases. Every other use case falls by the wayside because it’s too much work to justify them.

The kinds of applications that typify digital transformation are those that, in the past, would not have passed the “most obvious, highest-priority” screen. They would have been those with unproven potential, which meant, in most IT organizations, their advocates would have found the justification process too onerous and given up pushing them.

Today, changes in cloud computing have increased digital transformation priorities and will increase the overall demand for computing resources. This will increase cloud infrastructure demand well beyond the TAM of displacing traditional infrastructure. It’s estimated that this additional demand could double overall cloud revenue, toward the order of $10 trillion dollars.

4) A Changing Role for IT: Running the Business

As described above, traditional IT has been lumped into the corporate cost center bucket—expenditures that are necessary but not especially connected with marketplace success. In other words, that bucket holds everything not focused on building and selling a company’s products or services. Every company’s approach to cost centers is the same: spend as little as possible.

However, this will change significantly as more of every IT organization’s efforts focus on digital-first applications. This is because these applications directly interact with customers or improve products to make them more attractive to the market. They are directly tied to revenue and, because of that, are subject to very different spend filters.

The question asked of digital-first applications is not “How much will it cost?” but “How much will it make?” For those applications that show a positive contribution to revenue or profit, the issue will be how much can be invested and how quickly.

This changes the role of IT, which can be summarized in the phrase “IT’s job changes from supporting the business to running the business.”

For senior IT leadership, this imposes a range of necessary actions: 

• Closer collaboration with product teams to ensure the right digital functionality is built into the company’s offerings.
• Better analysis of how users actually use the product, so it can be modified to increase customer engagement and thereby revenues.
• Increased emphasis on application resilience to reduce interruptions to revenue flow.

Essentially, IT must change from an order-taking organization to a collaboration partner organization. Some leaders and organizations will make this transition, and their parent companies will thrive; others will find the change too challenging, and their failure will affect not just them but will damage their parent company’s future.

5) The Talent War Goes from Lukewarm to Scalding

The technical talent war has been an evergreen topic for years. IT organizations have had problems in recruiting technical talent, with accompanying challenges to project delivery timescales.

Notwithstanding this constant theme, 2022 will supercharge the issue of hiring and keeping talent.

Obviously, one cause is that IT staffing requirements are growing due to digital transformation. As software replaces, complements, or infuses market offerings, more software needs to be written, deployed, and managed. So, one reason the talent war temperature is moving up is just general demand for technology personnel.

However, there are additional reasons the war for talent is going to get blistering hot.

Digital-first applications require specific skills even scarcer than general IT talent. Writing microservice apps designed for elasticity and failure resilience calls for skills present in only a small percentage of the overall technical talent pool.

Writing microservice apps designed for elasticity and failure resilience calls for skills present in only a small percentage of the overall technical talent pool.

As demand for digital-first applications grows against a small percentage of the pool, it will be harder and harder to successfully recruit staff to a given technology organization. Enterprise IT shops are at an additional disadvantage given their historical reluctance to bid up wages for this category of employee. 

It used to be that old guard IT organizations had some geographic protection in this competition. For example, if you were a regional retailer located in Grand Rapids, the competition to hire someone with technical skills was relatively lower than in a large tech hub. That has changed, though.

One of the unanticipated results of COVID-19 has been the growth of remote work; suddenly, one could be employed by a top cloud-native company and happily reside in Grand Rapids. While some large technology firms like Google have put forward policies designed to induce/urge employees back into their offices, remote work appears to be here to stay.

For those formerly geo-protected companies, this means the competitive pool for desirable employees has grown to include a much broader range of companies. And many of those companies treat employees like a competitive advantage and have no hesitation at bidding up their salaries.

Consequently, one can easily predict that talent access will be a critical issue for technology organizations across the land, and companies will have to adjust to the new reality of attracting candidates who have more employment options than ever.

The Future in a Nutshell: Faster, Ever Faster

The coming year will see more change in IT than we saw over the pre-COVID decade. Driven by the torrid growth in technology adoption by businesses seeking to respond to changing customer demographics and preferred interaction modes, software will be a core competence for every company.

The consequences of these changes will be massive. Every company will need to decide how willing it is to restructure its assumptions and plans in light of these changes. There’s no hiding—it’s only a question of whether a company chooses to accept or resist the changes.

The post 5 Tech Trends for 2022: Digital Transformation, Cloud and Talent Wars appeared first on eWEEK.

You might think it foolhardy to make predictions about something that changes as furiously fast as emerging technology. Driven by massive investments, competing for a market that appears limitless, thousands of companies innovate constantly, sometimes at cross purposes.

Yet we are human, after all, so we can’t resist peering into the unknowable future and proclaiming, “Yes, this is what’s ahead.” Plus it’s that time of year: in January we are (we hope) imbued with a fresh energy that allows us to accurately assess the year ahead.

Not that all annual predictions are mere guesses. Clearly, the tech forecasters below are top practitioners who are close to their respective sectors. They’re the experts who have long labored in cloud, AI, security, edge, data analytics, digital transformation, accruing years of deep market experience. If anyone can foresee the future, it’s these seasoned thought leaders.

So here you go – a compendium of insightful forecasts across the major IT sectors. We hope you enjoy perusing the predictions; at the least, they’re great food for thought. And please revisit the pages of eWeek as the year unfolds – we’ll be tracking tech as it happens, offering a real time record of how these forecasts turn out. All best to you, and have a great 2022!

• Cloud Computing
• Cybersecurity
• Edge Computing
• Artificial Intelligence and Automation
• Data Analytics
• Digital Transformation
• IT Infrastructure

Cloud Computing Revenge of the Rushed Cloud Migration

“The pressure of the business imperative to adopt cloud at rapid speed during the pandemic will begin to unravel as it becomes apparent that security slipped through the cracks in rushed migration. As a result, we will witness the rise of huge breaches due to simple cloud security misconfigurations and permissions errors. This will fuel the mushrooming of startups based on automation of cloud configuration, permission analysis and remediation platforms.”

Archie Agarwal, Founder and CEO, ThreatModeler

Major Growth in Hybrid and Multi-Cloud

“Expect to see dramatic growth in hybrid and multi-clouds in the new year. Hybrid and multi-cloud solutions allow the business to choose the best fit for their workload without compromising the fidelity to their service management and integrations. Efficient and effective enterprises will want to lean into this natural affinity to get the most value, resulting in multiple cloud environments.”

John Annand, Director of the Infrastructure Team, Info-Tech Research Group

Hybrid Cloud Conversation Driven by Public Cloud Vendors 

“For the last few years, hybrid cloud was championed by technology vendors who sold on-premises technologies, but now public cloud vendors are offering cloud-like experiences on premises. This is not a good or bad thing, but as companies decide how they will approach their hybrid cloud strategy they need to consider how much control they want to maintain. By handing their private cloud to a public cloud vendor, companies may lose some control and ability to customize, but they will gain a unified, consistent private cloud experience. Companies need to decide what will be best for their business, but overall the conversation has shifted with public cloud vendors taking the wheel.”

Jesse Stockall, Chief Architect, Cloud Management, Snow Software

Service Mesh Will Solidify its Role as a Critical Component of the Cloud Native Stack

“In 2022, service mesh adoption will continue to cross the chasm from early adopters to majority adoption within the cloud native ecosystem. There will be a widespread realization that “just throw it on Kubernetes” is not enough for a functioning cloud native application, and that the addition of a service mesh solves fundamental concerns about security, observability, and reliability. In 2022, the service mesh will become the norm, not the exception, for Kubernetes applications.”

William Morgan, CEO, Buoyant

Database Migrations and Cloud

“Database migrations will become central to any cloud strategy. Databases are the foundations of every IT architecture, and data access as well as processing capabilities make for a critical competitive advantage. Migrations will probably be the single most important IT project of 2022 — and beyond.”

Mike Waas, founder and CEO, Datometry

Also see: Top Cloud Companies and Service Providers 

Cybersecurity Infrastructure: New Roads to Theft and Destruction

“Cyberattacks have generally sought either disruption or extortion, but soon both state and non-state actors will more frequently target physical infrastructure like electrical grids, dams, or transportation networks. Hackers may target funds disbursed by Congress that are intended to rebuild U.S. infrastructure.”

Michael Bruemmer, Vice President of Data Breach Resolution and Consumer Protection, Experian

The Biggest Barrier to Cloud Migration will be Security Misconceptions

“With large-scale cyberattacks on the minds of C-suites across the globe, the biggest hurdle to adoption in 2022 will be to rectify any misconceptions of the public cloud surrounding safety and security. The pandemic has taught us that cloud-based infrastructure is an important factor in moving to a remote workforce, due to this we will continue to see more companies make the switch. However, opinions that public cloud may not be as secure as existing infrastructure will slow the adoption process. CSPs will need to work to combat these misconceptions and ensure that potential customers understand the cloud is safe, effective and is an essential component of digital transformation.”

John Schmottlach, Senior Vice President of Delivery, Apps Associates

A Holistic Approach to DevSecOps

“2022 is the year we’ll take a holistic, process-based approach to implementing security into DevOps. We’ve been doing the same DevSecOps dance for the last five years, and while there has been some cultural transformation, organizations keep adding to the DevSecOps hairball that results from too many tools and no real process. The adoption of cloud and hybrid environments at the onset of the pandemic introduced efficiency and the ability to answer questions quickly, but security is still a lacking part of the conversation and is a necessity to reduce overall business risk. In the coming year, we’ll see organizations focus more on finding a process-based way to continue increasing this efficiency and pull real-time insights from the DevSecOps hairball in a way that truly benefits the business.”

Brittany Greenfield, Co-Founder, Wabbi

IP Theft Will Increase 

“While external cybersecurity attacks are always a real threat for businesses, often it’s the employees themselves inadvertently or purposefully leaking sensitive data at their company through collaboration tools like Slack and Teams. In fact, confidential information sharing on average takes place in 1 in 135 private messages and 1 in 118 public messages. Over the next few years, employers must take steps to monitor this type of internal data sharing to protect all parties involved.”

Jeff Schumann, Co-Founder and CEO, Aware

The Privacy Paradox in a Hyper-Personalized World 

“In 2022 and beyond, companies will be challenged to strike a balance between the countervailing pressures of hyper-personalization and privacy within the context of regionally-evolving regulations and customer expectations. This will signal a fundamental shift in product development, with guardrails built in by design from the very beginning, and flexibly evolving throughout the product life cycle. And accountability for responsible AI and data stewardship practices will be ingrained in a company’s culture, at all levels in the organization. While grappling with the “privacy paradox” will be no easy feat, the industry is fast at work with enabling technologies. For example, we anticipate breakthroughs in consent management platforms as they continue to keep pace with evolving global privacy laws.”

Atticus Tysen, CISO, Intuit

Deception Technology will Experience Rapid Mid-Market Growth

“Cyber-attacks are steadily becoming the biggest threat to businesses of all sizes. While larger companies more commonly have the resources to implement in-depth security measures, mid and small-size companies remain ill-equipped to defend against advanced attack techniques, forcing them to decide where and what to protect. As a result, in 2022, many of these companies will turn to deception technology as an efficient, cost-effective method to respond to internal and external threats. A high signal-to-noise alert ratio and innovations in machine learning will make managing decoys and concealment attractive for under-resourced businesses. Additionally, with an increase in SaaS-based offerings, mid-level companies will be able to take advantage of benefits of deception technology in greater numbers.”

Carolyn Crandall, Chief Security Advocate, Attivo Networks

A Major Cryptocurrency will be Hacked Within the Next Few Years

“The value of cryptocurrency has dramatically increased over the past few years. And as the value continues to rise, so too does its value to adversaries. But this currency lives on a network with no “armed guards” protecting its vault. It’s not a matter of if, but when, vulnerabilities will be found. And the result could cause a mass evaporation of funds. Bug bounty programs are a start – but there is still much more to do in the way of shoring up the security posture.”

Thanassis Avgerinos, Co-Founder and VP Engineering, ForAllSecure

Data Debt Will Be a Primary Cause of Breeches

“Organizations have data stored everywhere, from their latest SaaS application to their oldest desktop and everything in between. And while organizations have worked tirelessly to secure their perimeters and lock down rights and access, sensitive data remains unfound and unprotected. Minimizing this data debt’s security impact begins by viewing data as a threat surface and methodically mitigating that threat based on its relative value, volume and vulnerability. In 2022, there will be many organizations with millions of undiscovered and undetected risks across their data landscape, exposing their enterprises and their partners to significant damage.”

Kevin Coppins, President and CEO, Spirion

The Rise in Cybersecurity Compliance Mandates Will Lead to a False Sense of Security

“There are two key issues with a compliance-driven approach to security. The first is that compliance standards are just a bare minimum and tend to lag the threat environment by years, if not more. Additionally, being fully compliant can lead to a false sense of security. Similar to building codes, security mandates establish a minimum, not a ceiling. People tend to view compliance regulations as aspirational instead of as a minimal acceptable level. A building “built to code” simply means that if a contractor did anything less it would be illegal to inhabit, “built to compliance” is no different. A compliance-focused mentality also encourages a homogenous approach, which benefits adversaries. The more organizations look the same, the better attackers are able to understand the defenses they will encounter.”

Mike Wiacek, CEO, Stairwell

We’ll See a Boom in Companies Expanding their Offerings to Include Cybersecurity Services

“End-point solutions like EDR and XDR are becoming a mandatory requirement for organizations, as these systems provide more than just antivirus protection – they provide complete, AI-based insights to multiple levels of threats based on the behavioral data these systems detect, across all platforms the organization has (cloud, on premise, remote access). I believe we’ll see an increase in these types of offerings, especially as the office and home remain merged, and innovations like the Metaverse, IoT and cloud adoption continues to grow, bringing new potential threats along with it.”

Avishai Sharlin, Division President, Amdocs Technology

Ransomware Hits the IoT

“With ever growing dependencies on IoT technology, I expect we will start seeing ransomware attacks targeting IoT technologies in 2022. These attacks will lead to widespread outages of consumer products, and we may even see these attacks target smart vehicle technologies. This could lead to critical disruptions and loss of confidence by consumers.”

Deral Heiland, Principal Security Researcher, Rapid7

Ransomware Will Continue to Evolve

“We are now seeing ransomware converging with hacktivism, where companies are being hit with ransomware just due to the hacker’s perceptions of a businesses’ values, industry, or actions. In these situations, the hackers are not even requesting a ransom or offering to decrypt the data. We also see that ransomware gangs now have the funds to purchase zero-day vulnerabilities that used only to be accessible to nation-states. Ransomware-as-a-Service will continue to make ransomware more accessible to a wider range of attackers while also paying company insiders to deploy ransomware at their place of employment. Nation States are going to continue to invest heavily in compromising identities and using “live off the land” attacks that are very difficult to detect because they do not use malware but instead use native operating system features to carry out their attacks.”

Heather Gantt-Evans, Chief Information Security Officer, SailPoint

Also see: Better Proactive Security Strategies that Cost Less

Edge Computing Edge Discussion Will Evolve to Separate into Two Focus Areas

“Edge platforms provide a stable pool of secure capacity for the diverse edge ecosystems and software-defined edge workloads/software stacks that extend our application and data systems into real world environments. We are already seeing most edge workloads and even most public cloud edge architectures shift to software-defined architectures using containerization and assuming standard availably of capacities such as Kubernetes as dial tone. This combination of modern edge platforms and software-defined edge systems will become the dominant way to build and deploy edge systems in the multi-cloud world.”

John Roese, Global Chief Technology Officer, Dell Technologies

IoT Connected to Key Business Outcomes

“In 2022, I expect a shift on enterprise IoT, where new projects are tightly integrated to key business outcomes instead of just being innovation for innovation’s sake. With the continuing pandemic and the focus on hybrid work, there is a pressing need to realize the business outcomes that IoT, together with supporting technologies like Cloud and AI, can enable. Organizations that cannot build their own IoT solutions will partner with experts – in software or services – to start realizing predictable, repeatable and measurable outcomes. If they don’t, they risk falling further behind.”

Vishal Gupta, Chief Information and Technology Officer, Lexmark International

Also see: Top Edge Computing Companies 

Artificial Intelligence and Automation Ethical, Responsible, and Explainable AI Will Become a Top Priority 

The extreme quantity of data being generated has already exceeded human scale but still needs to be processed “intelligently” and, in some cases, in near real-time. This scenario is where machine learning (ML) and artificial intelligence (AI) will come into their own.  If instant insights are the future, then ML and AI will help us produce these insights. The challenge is that data has ownership, sovereignty, privacy, and compliance issues associated with it that need to be taken into account as it is used to produce insights. And if the AI being used to produce instant insights has inherent biases built-in, then these insights are inherently flawed. This leads to the need for ethical, responsible, and explainable AI. The AI needs to be transparent, so everyone using the system understands how the insights have been produced and that transparency must be present in all aspects of the AI lifecycle – its design, development, and deployment. Transparency, inclusive design, and diverse teams are required to build a future state that more accurately represents everyone.

Liz Centoni, Chief Strategy Officer and GM of Applications, Cisco

Predicting Intelligent Information Will Gain Momentum

As we know, the shift to hybrid work has caused a massive increase in the amount of data being generated across numerous sources, and it is essential for today’s businesses to be able to capture, archive, and discover this rapidly growing volume of data. However, this process can be quite expensive based on the amount of data being generated and, the problem is, a lot of this data is classified as ‘dark data’ – meaning information that is collected, processed and stored, but isn’t used for any other purposes.

In the new year, organizations will start proactively predicting intelligent content right at the edge to get a better sense of what data really matters. In doing so, technology can leverage a combination of AI data patterns and policies to make an intelligent prediction of what content actually needs to be captured and analyzed, which in turn, will significantly lower costs and improve efficiencies. This is the next wave of managing not just data, but information, at its source.

Ajay Bhatia, GM, Digital Compliance, Veritas

Mainstream AI and Deep Learning

“As the toolset for AI applications continues to evolve, machine learning and deep learning platforms have entered the mainstream and will attain the same level of maturity as specialized data analytics. Just like we currently see a plethora of fully integrated managed services based on Apache Spark and Presto, in 2022 we will see vertical integrations emerging based on the likes of PyTorch and Tensorflow. MLOps for pipeline automation and management will become essential, further lowering the barriers and accelerating the adoption of AI and ML.”

Haoyuan Li, Founder and CEO, Alluxio

Automation over Outsourcing

“By 2025 over 50% of the revenue of the major consultancies will be derived from service delivery that leverages automation and AI instead of just human resources. 2020 will see large consultancies, VARs, Integrators, and Outsourcers strategically moving to build, acquire, or adopt automation technologies or risk becoming non-competitive. This will represent a major shift in how IT infrastructure programs are delivered over the next several years.”

Andrew Sweeney, Co-CEO, ReadyWorks

Startups Focusing on AI-Driven Software Development Will Continue to See Increased Investments

“While ML and AI today is largely siloed inside various pieces of the supply chain, more connected analysis across the toolchain will see an increase. Perhaps the biggest driver for this will be VSM, and its goal to collect and correlate data and metadata from across the supply chain. Generally ML and AI will start playing a bigger role in all aspects of the software delivery supply chain.”

Shawn Ahmed, CMO, CloudBees

Robotic Process Automation (RPA) Will Ease Resource Challenges

“A global shortage of talent and labor has become the main constraint for growth among major consulting firms. Nearly two in three consulting firms say they’re short staffed, and one in five are turning down new business as a result. Leveraging emerging technology like RPA linked with AI will help to address staffing shortages, streamline processes, create new efficiencies, and reduce costs. In 2022 and beyond, consulting will be more knowledge-based and insight-based. The ability to get insight from data will be a critical skill that is going to be more in demand for consulting firms.”

Vivek Bharti, VP of Product Management, Icertis 

Businesses Will Finally Derive Value from AI Through Contextual Experiences 

“Though the benefits of AI have been lauded for years, it’s challenging to find use cases of AI providing true organizational value. Non-contextual AI/ML can only go so far. For content creators, contextualized intelligence will be a game-changer – especially when it comes to managing and searching for images. The CMS will eventually learn business lexicon to add context, provide warnings about sensitive content and provide guidance in terms of any content that needs to be trimmed down.”

Nishant Patel, CTO, Contentstack

Automation Gets Easier to Implement

“Organizations will begin to see that automation isn’t that difficult once you get going. In today’s organizations, processes are everywhere, and most of them can be automated to increase productivity, and quality of efficiency. However, automation as a business initiative can be very overwhelming for organizations looking to get the ball rolling. In 2022, IT leaders should start small and begin by selecting a less complex process to implement. A successful lighthouse project will allow organizations to scale automation efforts as the next steps.”

Bernd Rücker, Chief Technologist, Camunda

In Contact Centers, Consumers and Agents Will No Longer Fear AI, They Will Become Reliant On It

“AI has quickly gained widespread acceptance in the business world and has proven to be an important element in business processes. As the labor shortage continues to persist, businesses can’t risk burning out their agents so they will look to AI technology to help offload mundane tasks agents dislike while augmenting their capabilities to solve customer issues. Without a doubt, in 2022 consumers will also embrace artificial intelligence to help make their lives easier while preserving their ability to speak to humans.”

Patrick Ehlen, VP of AI, Uniphore

Healthcare will See the Biggest Impact from AI and ML

“I predict that in 2022, the healthcare industry will see the biggest impact from AI and ML. With advances in technology and data collection, AI and ML will be able to assist with diagnosis and treatment, and can dramatically accelerate the development of medicines and vaccines. The potential to extend human life is a very real possibility while also improving the quality of life, thanks to advances made during the pandemic. Additionally, this will have the most direct impact on the average consumer and will bring AI/ML to the forefront on consumer confidence.”

Myles Gilsenan, Vice President of Data, Analytics and AI, Apps Associates

Also see: Top AI Software 

Data Analytics Business Users Will Be Empowered to Become Data Analysts 

“Enterprises will empower business users to become data analysts by applying well-trained natural language processing (NLP) and machine learning technologies, and implementing richly curated data catalogs to unleash the power of complex analytics. Organizations with integrated data strategies will provide their employees with the tools that allow them to gain data analyst “superpowers” by tapping into vast amounts of data and drive business results. This improves the productivity of business users’ and eliminates bottlenecks caused by the reliance on data analysts to find and analyze trusted data within their organizations, making the process more prolonged and arduous than necessary.”

Raj Gossain, Chief Product Officer, Alation

Greater Focus on Quality of Data 

“As first-party data becomes more regulated and the dependence on this data increases, more care must be taken to cultivate a proper business-led culture around the ongoing quality and improvement of data. For too long, this has been the exclusive realm of IT, where the business imperatives and urgency are merely hearsay. Business leaders that rely on the underlying quality of the first-party data will take ownership of driving this cultivation or curation function around this data. With the proper insights and transparency into the metrics and measures of data quality, they will seek ways to drive these data quality metrics up, resulting in improved outcomes. Leaders in this area will develop small expert teams, on the business side, whose sole responsibility is to track data quality, research and resolve data quality complaints or errors and plan programs for systemic data quality improvement.”

George Corugedo, Chief Technology Officer, Redpoint Global

Also see: Top Data Analytics Tools

Digital Transformation Company Culture Will Be More Important Than Ever

“With many companies choosing to move forward with a hybrid workforce, company culture will become more important than ever in 2022. The pandemic brought on an entirely remote workforce (whether companies were prepared or not) and many employees faced challenges with creating genuine connections in a virtual environment. It will be pivotal for companies to realize the importance that if you’re no longer going to require an in-office environment, they will need to cultivate ample opportunities for co-workers to connect and collaborate in a supportive and fun way. Whether that be through company events, team events, personal interest groups or mentorship opportunities, company culture will be increasingly important in the next year.”

Lela McKenna, Principal Manager of Change Management & Training, Apps Associates

LowCode/NoCode Gains 

“LowCode/NoCode development environments will start to get more attention as they grow their footprint and face similar challenges that the general software industry has faced over the last 10 years. The focus in 2022 will be on building resilient evidence-based systems, focusing on recovery vs. prevention.”

Daniel Riedel, SVP strategic services, Copado

ESG Will Shift from Talk to Action and Unlock Contract Performance

“2022 will be the year of action around ESG and the purpose economy. While 2021 was the year of talking about ESG, 2022 will see more employees and consumers wanting to align themselves with companies that are doing good. Decisions will no longer be made based solely on cost, but on purpose and how a company’s purpose resonates with a customer’s own social values.”

Bernadette Bulacan, Vice President, Lead Global Evangelist, Icertis

Sustainability Will Hit an Inflection Point as Businesses Prioritize ESG

“More businesses will realize that digital infrastructure puts a strain on the environment and will start setting goals to reduce their carbon footprint. Mission-driven companies will work to get to carbon neutrality and others will experience increased pressure from shareholders to prioritize ESG to ensure ethical growth.”

Asim Razzaq, CEO, Yotascale

Also see: Top Digital Transformation Companies

IT Infrastructure Standalone 5G will Herald Real Innovations 

“Service providers will be able to better enrich their offerings for both enterprise and consumers alike. It’s the ability to offer network slicing, guaranteed quality of service and exposure of the network through Open API’s and network functions like NEF (Network Exposure function) or NWDAF (Network Data Analytics Function) that will enable this.  Thus, we’ll finally move beyond the traditional “faster streaming” scenario many lean on today. On the consumer side, expect entertainment, learning and working to be advanced by virtual reality and augmented reality overlays. On the business side, expect private enterprise networks to be offered by communication service providers to their enterprise customers. These businesses will be able to design their own experiences on the 5G networks and monetize these unique experiences to create and control themselves.”

Niall Norton, General Manager of Amdocs Open Network and CEO, Openet

Virtual Desktops and Virtual Apps

“As organizations face the long-term reality of remote and hybrid work, they are seeking secure ways to enable users with access to all of their business-critical applications on any device, regardless of their location. According to the recently-released “VDI Like a Pro” report, adoption of Virtual Application Delivery grew 10% Y0Y and 17% of survey respondents who currently utilize Virtual Desktops indicated they plan to shift to Virtual App Delivery in the coming year. That trend would make 2022 a milestone year for a changing of the guard in remote work technology, where Virtual App Delivery will overtake decades-old Virtual Desktop technologies as the solution of choice for enabling remote and hybrid workers.”

Robb Henshaw, Co-founder and CMO, Cameyo

Kubernetes Will Develop a Greater Position of Dominance

“Kubernetes will gather mainstream acceptance to support serverless workloads and virtual machines. As such, hosting and edge platforms built to support Kubernetes will have a competitive advantage in being able to flexibly support modern DevOps teams’ requirements. Edge platform providers who can ease integration with Kubernetes-aware environments will attract attention from the growing cloud-native community. For example, leveraging Helm charts-as-a service (the emerging standard in application development language), where application builders can hand over their application manifest and rely on an intelligent edge orchestration system that just works.”

Daniel Bartholomew, CTO, Section

Within the Decade, Data Centers Will be Powered by 100% Renewable Energy

“To participate in the emerging circular energy economy, businesses must proactively forge municipality and community partnerships with energy and utility leaders and deploy more sustainable business models that leverage cloud and colocation partners. Over the next decade, all data centers will be 100% renewable, creating a sea change that gives rise to green supply chains and new economies built around low-carbon materials.”

David Hall, Equinix Fellow, Technology and Architecture, with the Office of the CTO

Also see: What is Digital Transformation? 

The post Tech Predictions for 2022: Cloud, Data, Cybersecurity, AI, and More appeared first on eWEEK.

In 2022, the burdens of enterprise data management will continue to expand, driven largely by rising data growth, as well as emerging trends ranging from DataOps and CloudOps to the increasing need to protect data.

The following are a few of the more significant steps we’ll see in the evolution of data management in 2022.

Storage Pros Will Focus on DataOps and CloudOps

As enterprises store more data in the cloud, the job of managing on-prem storage has been reduced significantly. Automation and advancements in storage technologies have simplified some of the basic tasks. This will result in a greater focus on collaborative IT technologies like DataOps and CloudOps. 

Indeed, traditional storage roles are evolving quickly. Instead of focusing on the detailed technical configurations of provisioning storage and resolving issues, the storage admin job will morph into a proactive role requiring a broader understanding of the full hybrid cloud infrastructure, including compute, AI and automation, DevOps, and containers.

Storage professionals will need to learn how to become trusted advisors to other IT and business roles and evangelize the future direction of data. This will require a collaborative mindset and approach to understand business requirements and user needs. 

Additionally, storage admins will be working more closely with data scientists, project teams, and DevOps. The bulk of the storage pro’s time will be spent identifying, segmenting and defining data types, and managing data granularly according to business and user needs. They’ll need new tools for collaboration, analysis, and planning to make the shift to DataOps and CloudOps.

IT Leaders Must Address Shortages of Data Staff

Using data to improve business outcomes is becoming a key competitive advantage. Enterprises are realizing that data science needs to become “citizen science.” Meaning that to address the constant shortage of data professionals, analytics must be made easy enough for more staff to handle these tasks. 

Companies must figure out: How can you enable every employee to easily analyze data and use it to improve results without requiring specialized data science skills? 

Citizen science technology revolves around data analysis. The workflows need to be intuitive, visual, clear and even inviting. Business intelligence and analytics vendors building tools for data warehouses, data lakes, and data management solutions are working on automated cloud-based solutions and simple intuitive interfaces – this work is essential for addressing the talent shortage. 

Among the professional cohort, data scientists must evolve from doing all the work themselves to engaging business users in data collection and culling. Those limited high end staff must learn to delegate if a company is to gain competitive advantage from data. 

Data Management Security Will Gain in Importance

Security and particularly ransomware are already popular data management topics, and they will gain an even higher profile in the year ahead. To be sure, the cost of protecting data from ransomware can be prohibitively high because of the cost of backing up so much data – cost is a key reasons data management security will become more important. 

Given that, “right-sized” ransomware protection, rather than the “one size fits all” approach, will draw increasing interest. This approach will require segmenting colder, less-used data to a lower cost location, such as the cloud, to reduce backup licensing costs and backup cycles while also leveraging capabilities that make data immutable, so it cannot be attacked by ransomware.

Another data management area that will gain visibility is cross-platform, portable tag management. This will enable data managers and data scientists to move files into new clouds or applications yet retain the tags that are critical for rapidly searching and segmenting data to feed data analytics pipelines. 

The End of Storage Overload: Time to Delete

Explosive, uncontrolled, unstructured data growth has created the need to segment and store it on different storage classes, or tiers, according to its usage, value, and need. But it also means that keeping data forever is no longer necessary or viable. 

This is a major shift from current practices, which tends to store – even hoard – all data, “just in case.” This is clearly becoming cost prohibitive.

In response, data storage professionals will create full lifecycle policies, so when data has reached its end of life, when it is no longer required for compliance or analytics, it will be purged altogether. Zombie data or dead data will garner proper attention as enterprises aim to better segment, classify, organize, cleanse, manage, and justify spending on storage, backup, and disaster recovery. Data hoarding will come to an end as part of successful digital transformation initiatives.

The sheer volume of data has become a challenge for enterprises in recent years. Throw in the impact of the cloud and the edge, the rise of unstructured data, and the growing demand for greater security around the data, and change is needed. What organizations are dealing with is a rapidly evolving data management environment that will force enterprises to find ways to quickly adapt to those demands. The industry will face that as a priority in the next year.

About the Author:

Darren Cunningham is the VP of Marketing at Komprise.

The post Data Management in 2022: DataOps, Talent Shortages, Storage Overload appeared first on eWEEK.

I spoke with Brian Keare, CIO of Incorta, about why companies are struggling to handle their data analytics, and how this relates to the supply chain bottleneck.

Among the tech issues we discussed:

• Data analytics – plenty of companies struggle in this area. Why? What’s the core challenge?
• What advice would you give to companies to optimize their analytics practice?
• The current global supply chains snarl is pretty backlogged. How can analytics help?
• What do you see as the future of cloud-based analytics?

Listen to the podcast:

Watch the video:

The post Incorta’s Brian Keare on Using Data to Improve Supply Chains appeared first on eWEEK.

I spoke with Adam Burden, Chief Software Engineer at Accenture, about his top forecasts for 2022, including how low code/no code will drive the democratization of technology.

Among the forecasts we discussed:

• At the edge of the cloud: Cloud computing will continue its dominance, which means that managing and optimizing hybrid cloud environments will be front and center, as well as charting the right strategy around using cloud to accelerate 5G and Edge deployments.
• Across the business: The democratization of technology is encouraging the decentralization of the IT workforce. As non-technical employees gain the tools build their own solutions, some of the burden is removed from IT. This means that IT can work more hand-in-hand with business departments to build solutions that are aligned with specific needs and goals.
• In the classroom: With a constant shortage of tech talent, more emphasis is needed on worker upskilling and collaboration. For instance, Accenture invested $900M for employees to complete 31 million hours of training in tech and business topics. Organizations will need to first define the roles of the future, and then prepare their IT workforce.
• On a platform: Companies are growing and converging by building platforms that other companies can use to provide complementary services. These “network-effect” platforms often create new business models and strengthen strategic partnerships, all with considerably less investment.

Listen to the podcast:

Watch the video:

The post Accenture’s Adam Burden on Tech in 2022, from Cloud to Digital Transformation appeared first on eWEEK.

To manage the complexities of today’s cloud native infrastructure, there’s an increasing need for IT monitoring technology, often referred to as observability. As core building blocks of this trend, two sophisticated technologies are rapidly developing: Monitoring-as-Code and Infrastructure-as-Code.

Let’s dig into these emerging technologies and look at how they help monitor and manage complex cloud native systems.

Also see: Why Cloud Means Cloud Native 

Infrastructure-as-Code: a Software-Defined IT Foundation

In the Infrastructure-as-Code model, there is still physical infrastructure, but it comes in the form of a descriptive model for software-defined compute, storage and networking resources that have been provisioned and subsequently delivered by cloud providers.

In IaC, there are no wires, plugs, hubs and routers; instead, there are ‘connection topologies’ defined by source code files. So of course there are cables and wires, but they live in the cloud data center and code defines which ones we use, on what day, for what workload and at what level of impedance and so on.

As we now traverse into the cloud-native environment – driven by Infrastructure-as-Code – with all the containerized Kubernetes granularity that comes with it, we still need to keep our eyes on the central challenge and look out for unexpected turns and speed bumps. Which brings us to Monitoring-as-Code.

Monitoring-as-Code: Moving From 1.0 to 2.0

The “always-on dashcam” that is Monitoring-as-Code (MaC) plays a key role in managing complex systems. A close cousin to (but not wholly synonymous with) Observability-as-Code (OaC), MaC is now rapidly evolving from a ‘simple’ data collection function to form part of the higher-tier observability strata that typify modern multi-cloud deployments.

In a world where DevOps, DevSecOps, and AIOps are all helping to create the comparatively new roles in Site Reliability Engineering (SRE), a new and more evolved use of monitoring will surely be needed as we move from static IT infrastructures to the dynamic models of IaC.

At its most basic level, Monitoring-as-Code (MaC) is an exercise in data collection via software agents, plugins and discrete elements of source code that work as ‘exporters’ to track system-level data and direct it to a repository. This is MaC level 1.0, if you will.

To get to MaC 2.0 and beyond, we need to think about building a wider observability lifecycle and enabling it with intelligence, diagnosis and extended abilities to achieve levels of automated remediation with what we might call codified reactive logic. In simple terms, the foundation blocks of IT and cloud software are shifting.

“One of the major tech trends for 2022 is going to be the integration of ‘observability and pipelines’ as adjacent markets,” said James Governor, co-founder of technology analyst house RedMonk. “Testing is shifting left, but it’s also shifting right, into production. Software delivery pipelines are now a mission critical application. We’re seeing observability players work more closely with companies providing CI/CD and software delivery tooling – to make life easier for customers.”

Governor says that so adjacent are the two work disciplines (or markets) of observability and software delivery that they are converging to enable what we can call “progressive delivery.”

Also see: AIOps Trends 

Use Cases for ‘As Code’ Observability Tech  

“As we move to progressive delivery, we can start focusing on use cases such as blue/green deployments, canarying, feature flags and dark launches, with progressive rollouts to specific user cohorts to limit the blast radius. Production then becomes part of the software delivery lifecycle,” explained Governor.

He says that the ‘as code’ revolution is driving all of this forward, enabling more sophisticated pipelines and extensions as organizations seek to modernize their app delivery, with better testing, more automation and observability baked in.

Current players in the Monitoring-as-Code space include Sumo Logic with its open source Sensu project. Described as an ‘observability pipeline’ built to deliver Monitoring-as-Code on any cloud, Sensu promises to consolidate monitoring tools and fill gaps in observability across data silos with automated diagnosis and self-healing. In sum, it supports the full transept of modern IT from bare metal to Kubernetes.

Sumo Logic acquired the Sensu project back in June of 2021 and CEO Caleb Hailey and co-founder Sean Porter joined the new parent company in roles designed to further their work. The pair conceived Sensu while working on an internal project to replace an out-of-date Nagios infrastructure monitoring installation for a cloud information archiving company.

Without necessarily deriding their worth, Porter himself called out MaC 1.0 tools for what they were: software agents designed to transit system file information to configuration management tools that might include Puppet, Chef, Helm, Terraform, Ansible or other. He described this as an action akin to retrofitting traditional monitoring tools to the modern DevOps paradigm.

This all brings us full circle into another modern application platform implementation paradigm. The world of CI/CD can take MaC deep into its structure if we make sure we adopt the evolved MaC 2.0 at its core.

MaC 1.0 in CI/CD means using monitoring tools to test application and related services operational health and wellbeing. MaC 2.0 in CI/CD means using monitoring tools with codified reactive logic to build and test applications. And to automate their operational wellbeing in live production environments.

As the use of MaC 2.0 grows in contemporary CI/CD environments that make significant use of containerization through technologies like Kubernetes, along with artificial intelligence, the ‘common packaging’ of containers can enable us to extend CI/CD into an even more formalized pipeline.

Sumo Logic’s Porter refers to these developments as the move to new new Infrastructure-as-Code, a term that clearly needs reinvention (“neo-IaC,” perhaps). Additionally, this also clearly applies to how we build CI/CD pipelines with more operational functions – including monitoring and observability – baked in.

More Closely Monitoring the CI/CD Pipeline

It’s important to create a more operationally intelligent, reactive and functional Continuous Integration/Continuous Delivery (CI/CD) pipeline that doesn’t just “do and view.” It also “sees, understands and reacts” so this new IaC substrate can help build a system to serve highly demanding cloud services needed by so-called high-velocity, cloud native enterprises.

“Monitoring-as-Code (MaC) solves a bane for many projects whereby unexpected issues during pre-production testing or deployments go undetected. We lose hours allowing failing tests to continue, then more time troubleshooting the problem, and of course, we miss the opportunity to investigate root cause at the point of failure,” explained Seng Phung-Lu, AVP of site reliability engineering at TD Bank.

Phung-Lu says that with MaC tooling deployed alongside an application via a single unified pipeline, he and his team are able to catch any issues early and avoid (as he puts it himself) having to “manually babysit” the testing and CI/CD process.

The Ultimate Goal: Continuous and Complete End-to-End Monitoring

The real value of these emerging technologies may be a new definition of what we mean by end-to-end monitoring.

Back in the day, end-to-end monitoring used to mean observability applied to everything from the device Operating System (OS) and application User Interface (UI) to the compute, storage, analytics functions and back again to the user. The new end-to-end monitoring is all of that, plus an ability to deliver monitoring across the entire breadth of infrastructure that all of the above runs on.

In the virtuous circle of MaC 2.0 inside an end-to-end neo-IaC environment, a new symbiotic relationship starts to develop; application metrics are collected and detected automatically in response to code changes and new deployments. Onward extension of the IT stack benefits from the “learned mistakes” (code execution failures) that have already been experienced and the stack itself draws upon its own evolutionary DNA.

About the Author:

Adrian Bridgwater is a leading journalist covering emerging technologies. 

The post Why Cloud Native Needs Monitoring-as-Code and Infrastructure-as-Code appeared first on eWEEK.

Social media is appealing because humans are inherently social creatures. We like to connect, share and discuss. But it’s clear that sometimes, these impulses take a wrong turn in the remote, detached online settings where social media takes place.

A key issue with social media use – one that’s often not understood or even recognized – is the cybersecurity risk it introduces. This risk doesn’t only affect individuals; as an employer, your employees’ social media behaviors may create significant risk to your organization.

It’s necessary to understand how malicious actors gather information from social media platforms about your employees, particularly with digital artifacts, relationships and personally identifiable information aggregation across platforms.

Also see: Digital Transformation Guide: Definition, Types & Strategy

1) Everyone’s Information Has Value

Once you create a digital profile, your information is available for everyone to see. That makes it available to be harvested by malicious actors. People tend to repeatedly use similar images, usernames and email addresses for different platforms. They also use those email addresses for other, more sensitive online activities, like banking. Every time you put a piece of information online, it puts you at greater risk.

It’s surprising how many people believe their online information isn’t a target for bad actors. They assume that since they aren’t rich or famous, no one would be interested in it. But the money in your bank account is of interest to criminals; your computer can be compromised and used as a resource to target other people of interest to criminals. That means every person is of some interest to a criminal.

2) Oversharing Creates Risks for Curated Phishing

Everyone knows someone within their social media circle who overshares – that virtual social butterfly with way too many “friends” who offer a running commentary on everything they are doing, everywhere they are going, every personal problem they’re having.

What most people don’t know is that this kind of oversharing can open a person up to the risk of spear phishing and similar attacks. Why? The more information an attacker can string together about you, the more likely they’ll be able to create a realistic-seeming email or text that you will engage with.

For employers evaluating a prospective employee, a habitual over-sharer is at a higher risk for phishing attempts, which then puts the company at risk. And that leads into the next point.

Also see: Phishing Campaign Used Morse Code to Evade Detection

3) Bad Actors Can Aggregate Data Across Forums

Every bit of information you put on your social media accounts is a potential data point, but it’s not only about the content you share. Bad actors can gather information from the memes and quizzes you fill out. It’s a seemingly harmless diversion to respond to memes like “Your secret agent name is your mother’s maiden name plus your favorite color.”

What else do those answers remind you of? How about the security questions that sites ask to recover passwords? First pet, first car, favorite color and so on. It’s been shown that some of these quizzes are created by malicious actors to gain access to your online accounts.

One or two bits of information aren’t likely to be dangerous, but if you become the target of an attack, the bad actor will look for more information across your social accounts. Any content that’s publicly available is of potential use. For instance, if bad actors can find your business email address easily, they can use that info to attempt a Business Email Compromise attack.

4) Relationships Can be Leveraged for Information

In addition to what you share and what others share with you, your social media connections also pose potential risk. A clear example comes from the Cambridge Analytica scandal. When you post something and your friends like it, comment on it or reshare it, that’s now a relationship that’s exposed if that post is publicly available.

Even if your accounts are private, there’s still risk. For instance, your Facebook profile image is public, and some of your connections can comment on your profile image if you’ve recently changed it.

Even if you don’t share your interests, location or education history, an attacker can conduct pattern analysis across relationships or life pattern analysis. They can derive information about you just from your relationships, whether you share it or not. In fact, this is one of the most valuable tactic attackers use.

5) Disinformation on Social Media Leads to Business Risk

Attackers are taking advantage of disinformation and misinformation. For instance, malicious actors could take advantage of a recent data breach by sending an email like, “Your account has been compromised” or “You’re locked out of your account. Click here to change your password.”

These attackers are taking advantage of the individual’s decision cycle to get access to corporate computer systems, sensitive information, bank accounts and more.

Social Safety: Strong Policy is Needed

These days, social media is about more than being social. It’s become a business necessity, but it’s also now a potential security liability. Attackers have adapted their methods so that they can glean important facts about your employees from social media to develop stealthy phishing campaigns and other social engineering-based attacks.

Make sure you have a strong social media policy in place and that you enforce it. Also, consider monitoring the social media of your employees, as well as your corporate social presence. Such steps will help you stay ahead of social cyber risk.

About the Author: 

Aaron Barr is the CTO of PiiQ Media

The post 5 Ways Social Media Impacts Cybersecurity appeared first on eWEEK.

Datadog and Splunk both cover a lot of ground as application performance monitoring (APM) tools. Both offer broad monitoring and in-depth data analytics. Buyers looking for a high quality performance monitoring platform will likely find both on their list of strong candidates.

However, there are as many differences as similarities between these two solutions. In sum, they’re very different products that will appeal to buyers with different goals in mind. Here’s a look at both, how they compare, and their ideal use cases.

Datadog vs. Splunk: Key Feature Comparison

The Splunk platform enables searching, network monitoring, and analyzing a vast amount of IT data to identify data patterns, provide metrics, diagnose problems and aid in business and IT decision making.

To understand the scope of Splunk: Security Information & Event Management (SIEM) can be considered just one small part of its feature arsenal. Beyond security, it takes in APM, compliance, automation, orchestration, forensics, as well as plenty of features related to IT service management (ITSM) and IT operations management (ITOM)

Datadog is focused more on cloud monitoring and security. It offers the ability to see inside any stack or application at any scale and anywhere. Infrastructure monitoring, APM, log management, device monitoring, cloud workload monitoring, and database monitoring are all included within its feature set. As you can see, there are certainly big areas of overlap. But Datadog stops short of calling itself a complete SIEM, ITSM or ITOM platform.

Delving deeper into both tools, the best way to differentiate them is how they operate. The Splunk application takes more of a log management approach, which makes it ideal for managing and monitoring the large amount of data generated from the devices running on the network. Datadog, on the other hand, takes more of a monitoring approach geared toward analytics. Thus, Datadog tends to be favored by DevOps and IT teams to address cloud and infrastructure performance.

While Splunk presents itself as a complete platform to handle everything related to SIEM, security and ITOM, Datadog is happy to cater to a smaller subset of the market. In fact, it is very willing to integrate with other security and IT management tools, whereas Splunk wants to be king of the mountain.

The existing stack of security and management tools, therefore, should be considered before deciding between Splunk and Datadog. Those with outdated tools that are in need of a complete overhaul should probably gravitate to Splunk due to its much wider feature set. Those needing only APM and some analytics, should favor Datadog and upgrade other toolsets in parallel.

Another way to differentiate them is by looking at what you want to address. Speaking broadly, Datadog monitors the performance of servers, databases, and infrastructure. Splunk is more focused on monitoring and analyzing data generated from various machines, converting it so that it can be analyzed by developers. Thus, Splunk is great for analyzing the huge number of log files generated by enterprise systems. It eliminates the need for IT to spend hours trawling through all the logs looking for that performance needle in the IT haystack. It makes use of the search processing language to find terms present in log files. Datadog, in contrast, is probably better when it comes to dealing with the performance and visibility of multiple cloud providers operating on the network and in managing cloud services.

A big strength of Splunk and a key differentiator is its ability to integrate data streams from a huge number of sources. It supports a wide range of data formats, like.xml, .csv and .json file. Those with needs that require such data stream integration from multiple data formats should opt for Splunk, as Datadog offers little support in this regard.

That said, users of Datadog are going to get up and running far faster than Splunk. Due to the size and complexity of Splunk, it requires a higher level of skilled internal resources as well as vendor support to deploy and operate. Datadog installation and commissioning are straightforward. And it is far easier to customize dashboards and interfaces using Datadog than Splunk. Datadog seems to have the philosophy that we will adjust to whatever way you do things, whereas Splunk is more geared toward providing access to a large amount of monitoring data if you agree to follow its methodology.

Datadog vs. Splunk: Management

Splunk’s wide range of products and features are aggregated within the Splunk Observability Suite. The platform can be used to analyze, ingest, and store data for later use, as well as detect issues impacting customers. Overall, it offers a breadth of management that Datadog doesn’t attempt to rival.

Those wishing to manage all security information and events (SIEM) or all IT operations (ITOM) or all IT services (ITSM) will find Splunk far more complete than Datadog. Overall, Splunk encompasses far more of the IT landscape than Datadog. Splunk’s comprehensive approach certainly advantages for those companies that select it. For example, Splunk offers a wealth of real-time visualization and analysis features that Datadog cannot compete with. If real-time management and monitoring are vital, then this issues is a no contest.

However, while Splunk wins hands down on breadth of management, Datadog comes out ahead on depth – at least across a limited feature set. Purely within APM and cloud services, Datadog offers better drill down and general management capabilities. Further, it is better at managing itself. Whereas Splunk relies on IT to notice and troubleshoot issues related to Splunk, Datadog generates alerts about potential or actual problems within itself and helps IT to identify the underlying issues.

Datadog vs. Splunk: Pricing

It is well known that Splunk isn’t a low-cost option. Once it ascended to become the darling of SIEM and ITSM a few years ago, it set its prices accordingly. The various modules within Splunk also have a reputation for being expensive.

Further, upselling can send the budget much higher i.e., if you want the SIEM module. If you need performance monitoring, that adds in an APM module, and slowly other modules creep in and the price tag rises. This is normal enough in IT. But when you are already dealing with a pricey platform, it is important to determine what you really need and what you can dispense with.

For example, Splunk offers a wealth of real-time visualization and analysis features that Datadog does not. If real-time management and monitoring are vital, then Splunk is the clear choice. But it does come at a price.

Real-time monitoring sounds great, but not everyone needs it enough for to pay this price premium. Datadog skips real-time and is quite a bit cheaper than its big rival. As for deployment, and support, Datadog also comes out well ahead in terms of keeping costs down. Splunk implementation and support costs can escalate as the software is rolled out.

Datadog vs. Splunk: Conclusion

Splunk and Datadog are both excellent tools designed to solve a great many challenges related to security and performance monitoring. You can’t go wrong too far wrong with either one. Both are strong in APM. In fact, both are regarded as leaders in the latest Gartner APM Magic Quadrant. Both also offer a lot of advanced features for your money that go far beyond APM. And both are trailblazers when it comes to innovation and future roadmaps.

In reality, though, it isn’t a case of one versus the other so much as it is a case of determining what you really need. Datadog is all about performance measurement for cloud services and is particularly adept at measuring the performance of databases and servers and measuring performance in a multi-cloud world. It doesn’t attempt to embrace the entire SIEM, ITOM, ITSM spectrum. Rather it takes one slice and does that portion really well. Those that have already deployed plenty of tools for security and IT management, therefore, may gravitate more toward Datadog to supplement ongoing efforts.

Splunk, however, is a much broader platform and toolset geared for a heavy duty large enterprise. Its log management approach often proves invaluable in rapidly analyzing log files and making sense of mountains of data so that IT knows what is going on. Whether it’s a performance slowdown or a security incursion, Splunk is a good way to stay one step ahead of trouble. Those needing an all-encompassing security and IT management platform, therefore, will find Splunk closer to their needs. Additionally, those with aging applications that are ready for a major management makeover will find Splunk a good fit. It covers a large amount of ground – if you have the budget for it.

Also see: Application Security is Key to Stopping Ransomware

The post Datadog vs. Splunk: 2022 Software Comparison appeared first on eWEEK.

Recently, I was the keynote speaker at the AppViewX digital event, “Simplify Application Delivery 2021.”  While there were many sub-themes to the event, the one I focused on is automation, as I believe this is the single most important capability for application delivery moving forward.

The application delivery landscape is evolving rapidly and shifting from a vertically integrated hardware stack to a set of cloud native capabilities. While this significantly increases agility, it does raise the bar on complexity, driving the need for automation.

Distributed Clouds: Paradigm Shift in Cloud 

I used the first part of my keynote to describe how the rise of distributed computing is changing application delivery. It’s important to understand why distributed clouds are fundamentally different than every other compute model, including traditional cloud computing.

Looking back at on-premises, hosted and cloud computing, while the financial model for these shifted from CAPEX to OPEX, the operating model did not, as they were all based on a centralized compute function. In this case, IT pros would run workloads in a data center or cloud and front-end it with an application delivery controller (ADC). If the location was the business’s own data center, the product of choice was a physical ADC. With cloud, virtual ADCs were used.

With distributed computing, applications are created by accessing workloads or data from public clouds, private clouds, and edge location, giving rise to the concept of composability.

Applications are no longer vertically integrated stacks but rather lightweight, cloud native services that are “composed,” which increases business speed and agility. With distributed clouds, the primary unit of compute evolves from a virtual machine to a container, which is ephemeral in nature. Containers can be spun up, run for a few minutes, and then deprecated just as quickly.

Legacy Application Delivery: Too Slow for Distributed Clouds

The problem with traditional application delivery is that even a virtual ADC can take hours to load – far too long for cloud native systems. This is driving the evolution of ADCs into a number of new form factors, such as a set of containerized services or even API-level ADCs where the functions can be called by an application when needed. Now ADC functions can be spun up when a container might require it.

But how is this to be managed? With cloud native systems and distributed computing, events happen far too fast for people to manage application delivery. This is the role that automation plays, eventually leading to an AIOps model where artificial intelligence is used make decisions on what changes are needed and when.

For IT pros, it’s important to evolve their thinking around automation from being task oriented to intent-based. While it’s true, IT automation has existed for some time, the effectiveness of automation frameworks such as a Puppet, Chef and even Python are limited. In this case, these tools are used to automate specific tasks, and this works fine in static systems. Task automation won’t work in highly dynamic environments because the scripts would need to be upgraded constantly.

AI-Based Automation: Necessary for Cloud Native

Automation needs to evolve to an AI-based, closed loop model, where the intent of rules is continually being analyzed and applied. This enables true zero touch automation as the machines will run the network.

A couple years ago, IT pros often scoffed at the idea of fully autonomous IT operations, but that attitude has changed. I recently ran an AIOps study and found that 97% of respondents would trust AI to run their IT environments and 99% believe AI is important for managing cloud and application performance.

During the panel I did at the event, one of the topics we discussed was using automation to implement zero trust and this is a perfect use case for AIOPs. With zero trust, policies are created to allow specific devices or workloads to connect with others only when explicitly allowed.

Task-based automation would be sufficient in a static environment as the policies could be set up once and then applied. In dynamic environments, such as a distributed cloud, where workloads are constantly being created and then shut down, people could not update the zero trust policies fast enough to comply, but machines can.

IT is at an inflection point where we are evolving from centralized clouds to distributed clouds and this will enable businesses to digitally transform faster than ever. As this happens, IT pros need to embrace closed loop automation for application delivery. This will  ensure that the right ADC services are deployed as per business policy, without having to introduce the long lead times created by manual operations.

 

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