Steve Staso - Computing Solutions Strategist

“Legacy” systems don’t get a lot of love in the tech industry, mainly because of the way that some vendors derogate the term while hyping their own shiny new products as replacements. Yet any time that a new server or other data center solution is deployed it becomes, for all practical purposes, a legacy system. Most enterprises understand this and don’t abandon compute platforms without good reason.

Perhaps the most important point is how well vendors adapt well-established systems to support customers’ changing business needs and requirements. The recent announcement of new cloud-based programs and solutions designed to help developers modernize IBM Z applications is a good example of this dynamic and process.

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

Adapting or updating legacy platforms and business applications to take advantage of fresh approaches, including newer programming languages, frameworks and infrastructure platforms is central to hybrid cloud modernization. Some have compared it to remodeling or renovating an older building, and that is correct in terms of how modernization efforts can extend the lifespan and value of existing systems and applications.

However, an equally important if less discussed point is how organizations can ensure that crucial employees, including developers and teams, have access to the tools and solutions they need to transform existing applications and processes, or create entirely new modern solutions.

That issue is central to the new IBM Wazi as-a-Service (IBM Wazi aaS) on IBM Cloud. Available as closed experimental beta, it will for the first time bring z/OS capabilities from the IBM Z-focused Wazi Developer solution to IBM Cloud.

That 2020 offering, the IBM Wazi Developer for Red Hat CodeReady Workspaces (Wazi Developer) was designed to accelerate the modernization of IBM Z applications by helping new developers adapt to the mainframe ecosystem, use modern programming languages and familiar cloud native tools for hybrid development.

The offering accomplishes this in large part via personalized and dedicated z/OS sandboxes — Wazi Sandboxes — running on Red Hat OpenShift on x86 to enhance cloud-native development and testing processes.

The new offering takes this several steps further by delivering IBM Wazi as-a-Service (Wazi aaS) using IBM Z technology to deliver IBM z/OS development and test on IBM Cloud. Developers involved in IBM Z modernization will be able to access and self-provision z/OS Virtual Server instances on IBM Cloud with whatever combination of resources their projects require.

In addition, the company announced that a new IBM Z and Cloud Modernization Stack is scheduled to be available on March 15. The offering is a “software-based” solution optimized for Red Hat OpenShift that can run on-prem or on a public cloud. The new stack is the first set of capabilities in support of the recently announced IBM Z and Cloud Modernization Center, and is designed to help clients:

• Simplify access to applications and data through secure API creation and integration.
• Leverage agile enterprise DevOps for cloud native development via open tools and rapid application analysis.
• Standardize IT automation with access to open source environments, including Kubernetes.

Together with Wazi aaS, these offerings provide development flexibility and choice with each offering sharing the same automated CI/CD pipeline.

Critics might claim that offerings like Wazi aaS are short-term fixes for legacy systems that are declining and destined for obsolescence. However, that perception ignores the strength and security the mainframe platform offers for processing business critical transactions and the robust sales growth that IBM Z continues to enjoy.

Just as important, IBM’s new solutions are clearly focused on addressing a key concern for many enterprises—how to find, hire, train, empower and keep highly talented developers.

In short, IBM has designed Wazi aaS as a practical solution for enhancing developers’ speed and agility, accelerating DevOps practices and reducing the need for specialized skills. By doing so, the company is also helping Z mainframe customers achieve hoped-for business and application modernization goals, while at the same time substantially extending the value and life span of their legacy IBM Z mainframe investments.

Also see: Top Digital Transformation Companies

The post IBM Accelerates Application Modernization with Cloud-Based z/OS Offerings appeared first on eWEEK.

Welcome to the Third Wave of cloud computing. A wave that is centered on connectivity and engagement, that has been accelerated by a global pandemic and a rush into the digital world.

The cloud’s latest evolution is more technically advanced than its predecessors, larger and more complex. Now in 2022, the benefits of cloud computing have been proven and it’s become the foundation for most digital transformation efforts. By next year, spending on public cloud services is expected to reach $482 billion.

But “the cloud” isn’t a magical utopia that you just arrive at one day. You need people to design, implement, manage and evolve those systems – especially as the landscape becomes more complex and companies become more dependent on cloud providers.

Let’s take a look at how to select that advisory help for cloud growth. But first let’s fully understand today’s Third Wave.

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

The biggest difference in the Third Wave is the cloud’s maturity. Customers are no longer new to the cloud. They have seen the speed and agility it can offer, which the pandemic proved is key to survival. They no longer have to be convinced.

When cloud computing (or Software-as-a-Service) first came into our vernacular in the early 2000s, it was a complete paradigm shift. The biggest value driver then was productivity and ease-of-use, and most projects were at the departmental level or within small to medium businesses.

The Second Wave (roughly 2010-2020) was marked by the rise of Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS) technologies, and increasing enterprise adoption of the cloud. Larger companies saw these platforms as a way to lower costs and get out of the data center game, while new technology providers saw them as a way to get up and running fast with new solutions.

As we enter the Third Wave, the cloud is exceptionally diffuse and complex. There isn’t one winner. At its core, Third Wave means that multicloud and hybrid cloud environments are the norm. A 2021 survey of 380 IT leaders conducted by IBM found that 95 percent of respondents are looking to adopt public, hybrid, or private cloud strategies, and they’re doing so aggressively.

As customer demand increases and cloud vendors proliferate, so too does the number of strategy firms, integrators, digital consultancies and managed service providers vying to serve those markets. This is a double-edged sword for customers.

On one hand, more choice is a good thing. On the other hand, finding the right consultancy to guide a particular business, in a particular industry, which can help them get the most out of these platforms is becoming increasingly difficult.

Firms that can best serve customers in the Third Wave excel in three categories:

• Their team’s technical capabilities
• The way they deliver their services
• How they run a business

These areas have always been important when choosing a partner, but they look a little different in the Third Wave.

According to the World Economic Forum’s 2020 Future of Jobs report, demand will continue to increase for roles such as data scientists, AI/ML specialists, robotic engineers, software developers and digital transformation specialists. Service partners know their success lies in having team members with these capabilities and having a methodology to grow and expand them over time because of how fast technology is changing.

The best (and most in-demand) service firms will also have expertise across a growing array of disruptive technologies, from machine learning (ML) and artificial intelligence (AI), to Robotic Process Automation (RPA) and integration, to cybersecurity and blockchain – just to name a few.

While the best firms typically specialize in one of these areas, when it comes to digital transformation, it’s important to have expertise across many of these areas and to be able to apply them across existing hybrid environments.

While technical skills might make the headlines, it’s the firm’s ability to apply them in the business that really matters. This requires industry and business knowledge, as well as the soft skills and capacity to help clients through the journey.

It’s not just what a services firm delivers that has evolved, it’s also how those capabilities are brought to market and applied within customers. Gone are the days of big-bang projects that take three years. The world and customer expectations change too quickly.

Insight into specific business and regulatory requirements is now a must for most customers. While industry-specific expertise has always been important, most cloud consultancies of the past were horizontally focused. This mindset is changing.

Third Wave consultancies should be able to demonstrate deep vertical market expertise and the ability to customize “off-the-shelf” vendor cloud solutions to address critical industry-specific business processes.

Key point: Third wave cloud consultancies are expanding in two additional areas: tech-enabled and managed services.

The trend toward tech-enabled services started during the Second Wave, but recently software is driving a larger portion of revenue. The ability to automate recurring tasks through software and provide pre-packaged accelerators to get customers to value faster will become even more important as service providers struggle to recruit and retain technical talent.

Demand for managed services is also growing as organizations digitize and look to offload more of the “run” part of their business operations. In the past, managed services have been an add-on or focused primarily on customer support.

Today’s managed services offer customers guaranteed access to scarce talent so they can continue innovating, and peace of mind that their systems are secure, managed well and ready for what’s next.

Also see: Top Digital Transformation Companies

Last but certainly not least is the way Third Wave consultancies operate as a business, especially as it relates to how they recruit, manage and retain global talent. According to Korn Ferry, there could be a global shortage of more than 85 million tech workers by 2030, and we’re already seeing this play out in today’s talent wars.

Succeeding in this work environment means firms must rethink their recruitment and talent management processes, and appeal to the way their workforce and the next generation workforce wants to work. People-based firms must be far more mission-driven, diverse and socially responsible than they’ve been expected to be in the past. It’s no longer a “nice to have,” it’s a requirement.

Consultancies who build teams that are inclusive and diverse will be more capable of responding to the varying needs and expectations of customers spanning different cultures and geographies. A 2021 McKinsey & Company report found companies are 21 percent more likely to outperform their peers in EBIT (earnings before interest and taxes) margins. Furthermore, 27 percent are more likely to outperform peers in long-term value creation if they have higher diversity levels worldwide. Consultancies who have achieved full-team diversity and built a value-based corporate culture will be more likely to achieve long-term success.

Finally, the shift to a more globalized, remote workforce comes into play. Consultancies who invest in programs, tools and incentives to facilitate and reward collaboration, communication and connectivity across the organization, their partner ecosystem, and their customer base will be in a better position to deliver on their promises.

Also see: DevOps, Low-Code and RPA:  Pros and Cons 

In summary, if you are a customer looking for a cloud partner to help you navigate and succeed in the Third Wave, don’t settle for what has worked in previous cloud eras.

Seek out service organizations that focus on outcomes, not output. Firms that put people at the same level as profits. Firms that have domain expertise and disruptive technical skills, but also the ability to apply them for real world business impact. Look for firms that understand the world has changed and can prepare customers for what’s next.

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

About the Author:

Chris Barbin, Founder and CEO, Tercera

The post Navigating the Cloud’s Third Wave: How To Hire Help appeared first on eWEEK.

Modern IT is undergoing a massive transformation, particularly in the realm of data storage. Adding more cybersecurity features and upgrading performance are all important moves for enterprise storage vendors like IBM, especially for their current customer base.

The recent additions IBM announced to its storage portfolio should address top of mind issues for many in IT. Let’s take a look.

Also see: How Database Virtualization Helps Migrate a Data Warehouse to the Cloud

IBM Cyber Vault is a new offering that uses IBM FlashSystem Safeguarded Copies to provide validation and verification of copy data so IT can know it’s good. Safeguarded copies are logically air-gapped snapshots of FlashSystem primary storage, providing immutable, incorruptible data copies.

IBM has a number of offerings in the cyber resilience market, including their Cyber Resilience Assessment professional service, QRadar and Guardian software solutions to monitor for data threats from systems and humans. Cyber Vault rounds out their portfolio with validation/verification of data.

Cyber Vault is a blue-printed solution from IBM Labs that takes FlashSystem Safeguarded copies and uses them in a secure VM to provide analysis, scanning, and test/validation, as well as potentially forensic and diagnostic services for Safeguard data.

FlashSystem Safeguarded copies are first copied to a secure Cyber Vault virtual machine environment. Once there, IT can verify and validate that data with whatever tests seem pertinent. Once done, IT knows whether their primary storage (at the time of Safeguarded copy) is good to use to recover from cyber-attack.

Cyber Vault could be used also at a remote disaster recovery site with replicated FlashSystem storage. And because IBM supports Spectrum Virtualize targets on Azure, this whole process could be done on the Microsoft Azure Cloud.

Cyber Vault was already offered on mainframe systems but now this service is available for the open environment using FlashSystem storage Safeguarded copies.

Also see: What is Data Visualization

IBM has also released new FlashSystem 9500 and 7300 storage systems. These include:

• Faster processors – 4 Intel Ice Lake 24-core, CPUs for 9500 and 4 Cascade Lake 10-core, CPUs for the 7300 system.
• New PCIe support – Gen 4 for 9500 and Gen 3 for the 7300 system.
• Larger capacities – 4.5PBe (PBe is effective capacity after data reduction) in 4U for 9500 and 2.2PBe in 4U for the 7300 system.
• New Gen3 FlashCore Module (FCM) – from 4.8TB to 38.4TB in a single module and ~70msec latency.

All this means lower latency storage access, more storage bandwidth and overall, 25-50% faster storage performance over prior generation storage. The FlashSystem 9500 also offers up to 48 32GFC and is 64GFC ready, with new cards. The new FlashSystems mean up to 2X faster read throughput for AI and in-memory DB workloads, up to 50% more transaction per second for Oracle processing, and 4X better performance on VMware Horizon activity.

IBM also updated the SAN Volume Controller (SVC) appliance with two 24-core Intel Ice Lake CPUs to add more storage virtualization performance to SVC clusters.

Also see: IBM Extends “Tailored Fit” Pricing to Z Hardware

One can see how IBM’s announcements incrementally improve and build upon past success, at least for cyber security. And performance is a major competitive arena among all storage vendors, which no business can afford to ignore for long. Again, FlashSystem 7300 and 9500 take all this to the next level.

Despite recent quarterly progress, IBM’s storage business has struggled over the past few years. FlashSystem and SVC are not the only solutions in IBM’s storage business, and all have a role to play in altering business trajectory. And the recent news is just the first of four quarterly announcements for IBM’s storage business.

We’d very much like to see how IBM can do more to address some of the other enterprise concerns. For example, the multi-cloud and how to get there. To many, this means Kubernetes, containerization and apps that run anywhere, wherever it makes the most sense, in the cloud, on-prem, or on the other side of the world.

Furthermore, on the horizon are all the new AI and applied data solutions moving into the enterprise. How to become the major storage supplier for these new applications needs to be on every storage vendor’s mind.

We look forward to Q2 and beyond to see what IBM will announce to raise the playing field on these and the other major issues facing IT today.

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

About the Author: 

Ray Lucchesi, President, Silverton Consulting

The post IBM Storage Announces Cyber Vault, FlashSystem and SVC Solutions appeared first on eWEEK.

Salesforce blends powerful built-in capabilities with the flexibility to create your own low-code capabilities. But as any seasoned developer will tell you, building applications is the easy part. The real challenge is ensuring they don’t break over time and having robust protections in place to allow you to continue to build – and experiment – without fear of failure.

This is why testing, especially automated testing, is so important. Testing is a key aspect of achieving digital transformation. No matter the size of the company, if teams aren’t emphasizing testing, they won’t achieve the speed and reliability of changes they need.

Interested in knowing more about how teams are managing their testing processes, Copado surveyed more than 275 Salesforce professionals, who shared details of their teams’ strengths and weaknesses on testing. Here are a few big takeaways:

• 41% of teams don’t have time to test every change before each release.
• 92% experience production issues due to inadequate testing.
• 84% still rely at least partially on manual testing.
• 51% of organizations have 25+ full-time QA resources (20% have 100+ resources).
• 95% have a higher total cost of ownership for manual testing versus commercial test automation.

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

Testing needs to happen at many levels, from unit tests to cross-system UI testing. Salesforce unit tests are helpful, but the unit testing culture among Salesforce developers is far weaker than in other languages like Ruby.

Salesforce mandates 75% unit test code coverage to deploy to production, but because continuous integration wasn’t common on Salesforce until recently, most Salesforce developers don’t fully appreciate the importance of these tests. So, it’s common to see sloppy tests that meet the 75% threshold but provide little or no actual protection.

UI testing is also tricky on Salesforce, especially on their newer UI, Lightning. Lightning uses a shadow DOM (document object model) for security and performance purposes, but that makes it hard for tools like Selenium that rely on the DOM.

The underlying DOM is also subject to change without warning, which can make tests fail even if the actual system is working properly. This means that UI testing tools for Salesforce need to have significant intelligence built-in.

Salesforce is also frequently integrated with other systems like ServiceNow, SAP, and Oracle Financials. This means that testing tools need to be able to test all of the connected systems to ensure end-to-end functionality.

As a low-code platform, Salesforce development can move quite quickly. The underlying Salesforce platform is also updated three times a year. These faster software cycles mean more changes and updates that cause unwelcome surprises down the road if you don’t have good tests in place.

System behavior can also depend on the underlying data in the system. So test data management is important, especially when working with complex apps built on top of Salesforce like Salesforce CPQ, Veeva, and nCino.

Lastly, Salesforce is designed to give low coders a boost. This means that ideally, testing tools should be easy for non-coders to use also.

Also see: DevOps, Low-Code and RPA:  Pros and Cons 

As Salesforce matures in scope and sophistication, organizations need to be ready for high-quality, cross-cloud customizations and verify third-party integrations across other technologies and platforms.

Teams in our survey (41%) said they don’t have enough time to sufficiently test all changes before a release. Our analysis shows that teams feel pressure to deliver features because of aggressive project timelines and that development takes longer than expected.

Testing, ultimately, gets thrown on the backburner as a result. Teams often fail to give testing the respect it deserves, and relegate it to the end of a sprint, rather than following test-driven development or other methods of shifting quality left.

Also see: What Does 2022 Hold for Intelligent Automation

Low-code platforms like Salesforce will continue to dominate the digital landscape as every business migrates its infrastructure to the cloud. The speed and ease of building on these platforms is unmatched, but they require robust testing to make sure changes don’t break existing systems.

Low-code platforms remain in the early stages of testing. Based on Salesforce’s research on its professionals, the majority of teams still mainly rely on manual efforts. But the rise of DevOps and the acceleration of change means that automated testing is the next frontier for today’s digital businesses.

About the Author: 

Andrew Davis, Salesforce DevOps Specialist and Senior Director of Research and Innovation, Copado.

The post What’s the State of Testing on Salesforce? appeared first on eWEEK.

Tableau and Spotfire are both leading data analytics and business intelligence platforms. Both were scored well by Gartner in its latest “Magic Quadrant (MQ) for Analytics and Business Intelligence Platforms.” Gartner graded Tableau as a Leader and Spotfire as a Visionary.

BI and analytics applications are a hot item as organizations seek to mine data from massive data sources like relational databases, enterprise applications, unstructured data, and social media. Instead of a small team of data scientists slicing and dicing data, now marketing, line of business heads, sales, and IT are all utilizing data analytics in their day-to-day activities.

As two popular data analytics platforms, users often are forced to choose between Spotfire and Tableau. There are arguments for and against each. It is hard to say that you could go wrong with either selection. But which is the better data analytics tool?

Spotfire is a mature product offering strong analytics, dashboard, visualization, data preparation, and workflow capabilities. It allows users to harness data science techniques, geoanalytics, and real-time streaming analysis in easily consumable forms, and can automatically suggest visualizations.

Tableau is very much focused on delivering 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. It differs sharply from traditional tools that require the user to analyze data in rows and columns, choose a subset of the data to present, organize that data into a table, and then create a chart from that table. VizQL skips those steps and creates a visual representation right away, providing visual feedback as you analyze.

How about the latest features and updates? Tableau, of late, has been adding features like Ask Data in Slack (ask questions using natural language and automatically get data visualizations), Einstein Discovery in Slack (predictions in the flow of work), and Model Builder (collaboratively build and consume predictive models using Einstein).

The latest updates for Spotfire include a better interface for administrators, data source name availability from server diagnostics, connection to TIBCO Cloud Data Streams from Spotfire on-premises installations, new connectors (such as PostgreSQL, MySQL, Oracle, Amazon Redshift, Apache Spark SQL, and SAP HANA), single sign-on (SSO) to data sources, and more security features.

Both Spotfire and Tableau offer comprehensive features, so the winner here comes down to which product offers the functionality the user specifically desires.

Also see: Data Analytics Trends 

Both platforms have a definite learning curve, but Spotfire appears to be a little easier to use than Tableau.

Users of Tableau should be well-versed in working with the R programming language, which is used heavily in statistical programming and data analysis. But Tableau has been working to become easier to use. Its AI-powered analytics features lower the barrier to data science techniques. Tableau works really well when Excel and statistical data is being used as the raw material. For other formats, ease of use can suffer.

Spotfire is said to be easier in terms of enabling more users to access, customize, and use sophisticated dashboards and visualizations. Some users complain about a lack of offline data modeling capabilities. But Spotfire gets high marks for data visualization.

The conclusion: both require some technical knowledge, but Spotfire is probably a little easier for a non-technical audience. Both are excellent for more technical and advanced users.

Also see: Top Data Mining Tools 

Tableau tries to differentiate itself with what it describes as an intuitive analytics experience with richer capabilities based on its patented VizQL engine. It 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 users to control performance, cost, and data freshness.

Tableau also scores very well on live query capabilities and extracts, helping analysts to query faster. Its use of the R language makes it the winner on statistical capabilities.

Spotfire has excellent analytics qualities, too. It harnesses machine learning to transform data and provide insights. It also offers top notch visualization, dashboard, and customization features.

Indeed, there is little to differentiate between Spotfire and Tableau on analytics. Perhaps the deciding factor is handling mass amounts of data. Some users say Spotfire has big data limitations – though reports are mixed. Tableau doesn’t suffer from that. So, for smaller data sets, Spotfire might come out ahead whereas for very large ones, Tableau leads.

Also see: Data Mining Techniques 

Spotfire is ahead of Tableau when it comes to the cloud. Both platforms originated in the on-premises world. But Spotfire has made more progress on providing cloud and hybrid functionality.

Tableau does offer cloud-hosted solutions such as Tableau Online and Tableau CRM, but its strength lies in on-premises deployments and this is where much of its massive installed base resides. Thus, it can be challenging to scale out Tableau workloads in the cloud.

CRM and BI often go together. With Tableau now being owned by Salesforce, it offers excellent marketing and enterprise product capabilities. It is in the process of being integrating with Salesforce Einstein Analytics (known as Tableau CRM). An Einstein Discovery dashboard extension, for example, brings predictive modeling capabilities from Einstein to Tableau.

Spotfire offers sales and marketing teams plenty of great visualizations and dashboards. But it stops short of CRM. But it is set up to integrate well with third-party CRM systems. Whether you use Salesforce, Microsoft Dynamics CRM, or another CRM systems, Spotfire can integrate it.

Overall, in this category, Tableau wins as it can provide CRM in one package. But for those not part of the Salesforce universe, Spotfire offers a good alternative.

Also see: Top Data Visualization Tools 

Tableau has its hands full in integrating with Salesforce. This creates a somewhat fragmented experience between Einstein Analytics and Tableau but steady progress is being made in bringing both together. It won’t be long until those issues are resolved. As a result, Salesforce customers will be upsold to Tableau and vice versa.

Spotfire, similarly, is in the midst of integrating the Information Builders BI platform with its own. This will take time but will bring additional functionality in the long run. Already it is bearing fruit as seen in the recent updates to the platform.

Further, Spotfire has a hyperconverged analytics vision that is ongoing. Some users noted that the technical capabilities of this have yet to match the hype. But the company is working hard to close the gap via tighter integration.

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

Tableau has a reputation for being expensive. By some estimates, it works out about $75 per month per user for decent analytics functionality. But those who only want to interact with some basic dashboards can get it much cheaper. That said, the addition of Tableau CRM for a list price of up to $150 per user per month means newer functionality and Salesforce integration doesn’t come cheap.

Spotfire also lacks a good reputation on pricing and contract negotiating. The company doesn’t publish prices online. But it looks like there are different prices for analysts, business authors, and consumers of analytics data. These range from $25 a month per user to $125 per month. In this category, users should check pricing as it applies to their specific environments. In some cases, Tableau will win; in others Spotfire will be cheaper. Overall, licensing on Spotfire looks more complex. The company also charges for integration with other platforms.

Also see: Real Time Data Management Trends

Tableau boasts a fanatical user base and a very loyal user community. Its user conferences attract large crowds. Tableau’s popularity is growing, too, partially through the distribution of a free platform known as Tableau Public. This is where people can share and explore data visualizations online. It contains over 3 million interactive visualizations. Yet it is in the data scientist, analytics specialist, and power user markets where its feature set wins the most plaudits.

TIBCO Spotfire also has a loyal following among data scientists and power users. It is particularly strong in life sciences, high-tech manufacturing, transport and logistics, and energy. But outside of its installed base, it lacks market momentum. Gartner said it rarely comes up in surveys of who is bidding for different projects. For those verticals in Spotfire’s areas of strength, it probably it just ahead of Tableau. But for other verticals, Tableau might be a better choice.

Also see: Top Business Intelligence Software 

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

Businesses across the globe, even as they continue to grapple with pandemic-related changes, are doubling down on scaling automation. The goals is to streamline existing business processes while continuing to deliver value to their customers.

But IT leaders need to ensure they select the right technologies that will perform the functions they wish to optimize, and that’s where implementing a hyperautomation framework comes in. 

Also see: DevOps, Low-Code and RPA:  Pros and Cons 

Hyperautomation, according to Gartner, is “a business-driven, disciplined approach that organizations use to rapidly identify, vet, and automate as many business and IT processes as possible.”

It involves the choreographed use of several tools, technologies, or platforms, including:

• Robotic process automation (RPA)
• Low-code/no-code tools
• Artificial intelligence (AI)
• Other types of decision, process, and task automation tools

Many organizations have already adopted hyperautomation tools, which is changing the way work gets done—56% of organizations are using between four and 10 initiatives at once.

An increased adoption of the hyperautomation framework will be an important part of the technology landscape in the coming years. The following building blocks that enable hyperautomation are the foundational trends to watch out for in 2022.

Also see: What Does 2022 Hold for Intelligent Automation

RPA software first gained popularity in the early 2000s, notably in the banking and financial sectors, with proponents heralding it as the best solution for implementing automation at scale. However, RPA only performed well when being fed high-quality, structured data—something that many enterprises unfortunately lacked.

That’s because paper-based processes or legacy systems don’t provide usable data for RPA solutions to function properly. With the increased desire for end-to-end automation, the value of RPA has really been put into perspective, and it’s only one component of a larger story. Still, RPA is a critical component and worth investing in as long as proper integrations, solid data, and defined processes are in place.

Artificial intelligence has almost taken on a life of its own these days, with practically every organization touting their tremendous capabilities as being “enhanced by AI.” However, this overused buzzword can actually be another indispensable building block of scaling automation, if implemented properly.

Just as with RPA, AI can only achieve peak optimization when supplied with a huge amount of good data. Most enterprises have this data, although much of it exists in unstructured form or is of poor quality, since it was gathered via error-prone, paper-based forms.

But in order to effectively leverage AI technologies, access to structured data is a critical requirement. For example, with structured and machine-readable data, an insurance company can test AI platforms in a handful of areas across its organization to see if it’s valuable first, and then implement it and roll it out in more venues.

Also see: Top AI Software 

Given that both RPA and AI technologies are only as good as the data they are supplied with, how can enterprises tap into machine-readable, structured data when it’s often locked away in unusable formats or shielded by customer privacy laws? The answer lies with a trend that is relatively new on the scene—synthetic data.

Synthetic data is information that’s artificially manufactured rather than generated by real-world events. It isn’t traceable to real customer information, and it can be used to train AI and machine learning models.

For example, in the healthcare industry, synthetic data can be used to overcome the challenges associated with protecting patient data and the privacy laws, like HIPAA. By feeding synthetic data into AI algorithms, the healthcare industry can better improve drug discovery, diagnosis and medical imaging, and accelerate its overall digital transformation.

However, in order to easily access data that synthetic data can be created upon, enterprises need to ensure they’re digitizing their data collection in the first place.

Also see: Best Data Analytics Tools 

If an enterprise has some of the initial hyperautomation building blocks in place, low-code process automation platforms can tie everything together by unlocking and capturing structured data.

Sitting on top of an existing tech stack, some of these low-code platforms can streamline data collection workflows. For example, paper-based processes can be rapidly transformed into digital experiences, often with citizen developers (with no formal IT training) at the helm.

Through the digital experience, organizations can ensure that error-free data is captured and passed on to systems down the line, while minimizing the time and cost investment of executing a hyperautomation framework at scale.

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

Finally, a system and its processes are only as good as the people overseeing them; this is where fusion teams come in. Fusion teams are multidisciplinary teams that blend technology or analytics and business domain expertise and share accountability for business and technology outcomes. They’re another building block for enterprises to ensure the success of project deployment.

Similar to agile teams, fusion teams are made up of employees from a variety of departments with a wide breadth of knowledge. This allows the team to be more flexible and autonomous, and to drive faster digital delivery.

For this reason, having a fusion team in place can be the difference between the success and failure of scaling automation within an organization—and why it’s a trend to watch out for this year and years to come.

Hyperautomation is driving significant business changes in 2022, with no signs of slowing down. With 80% of CEOs increasing spend on digital initiatives this year and 72% expecting to shorten the implementation timelines, adopting some of these building blocks is a critical step in making better, fast-paced business decisions backed by optimal data.

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

About the Author: 

Art Harrison, Co-Founder, Daylight

The post Hyperautomation Trends 2022: RPA, Low-Code, AI appeared first on eWEEK.

I spoke with Grace Liu, Sr. Vice President of Information Technology at Seagate Technology, about four mistakes that companies should avoid when working to simplify the many complexities of cloud computing.

The mistakes to avoid are: 

1) Increasingly, organizations are seeking simplification of architectures without the unpredictability of costs, services, and complex layers of tech at scale. It’s a tricky endeavor because the fixes themselves can invite more complexity and disruption. Adapting the simplifying solutions at the wrong time.

Follow up: Do you see this particularly with multi-cloud? Seems like the potential is enormous but the complexity even more so.

2) Only thinking about components, not solutions.

Follow up: Instead, companies should focus on an over-arching solution; this is, a plan?

3) Assuming that a perfect solution exists.

Follow up: Of course today’s rate of change in the IT landscape makes perfection even more elusive.

4) Overextending a solution that works for one scenario to another application without proper testing.

Follow up: Interesting. What’s an example of this?

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Choosing the technology that has evolved most over the past two decades would be a daunting task, but it is likely that high performance computing (HPC) would be among the top contenders.

In the early 2000s, HPC installations and performance were largely dominated by systems leveraging proprietary technologies, such as processors and interconnects. But by 2008, eight of the top ten supercomputers ranked by Top500.org (including the top three) were based on x86 silicon from Intel and AMD.

These developments sparked a commercial renaissance for HPC. However, while considerably less expensive than it once was, HPC still requires significant upfront investments, and tends to become redundant more quickly than other commercial systems. Is there any way that HPC vendors can help customers enjoy greater flexibility and achieve a better return on their investments?

Lenovo’s recent addition of a new HPC-as-a-Service (HPCaaS) to its TruScale everything-as-a service portfolio offers an intriguing approach.

Why has effective HPC remained out of reach for many organizations? After all, since x86 CPUs and other industry standard components were cheaper and easier to obtain than proprietary tech, powerful HPC solutions became increasingly affordable. That trend accelerated as GPUs and other industry standard components came to play key roles in HPC.

However, by definition and design, HPC delivers optimum value through optimal functionality. Meaning that businesses hoping to achieve market differentiation or success with HPC need to continually look to and push the edge of system performance. Companies that can afford and acquire the newest, fastest, most powerful solutions can automatically put competitors at a disadvantage.

As a result, “keeping up with the neighbors” in HPC can be painfully expensive, with many businesses purchasing new and larger systems than they might need to address uncertain compute requirements or market shifts. Add in the remarkable events of the past two years, as the Covid pandemic has shaken global businesses and supply chains, and it should come as no surprise that organizations are reining-in capital investments wherever possible, including those that could benefit from leading-edge HPC solutions.

What is Lenovo’s new TruScale HPCaaS and how might it address these issues? To the first point, the company has designed its TruScale offerings so that customers can acquire, use and pay for services as easily as they would through public cloud platforms.

Like other TruScale offerings, the new HPCaaS solution allows Lenovo customers to acquire on-premises systems (in this case, HPC clusters) with no up-front investment and then pay for the compute, storage and other resources they consume with transparent and predictable payment options.

They can also effectively manage their budgets and visualize consumption and payment requirements through Lenovo’s TruScale Portal. In other words, Lenovo TruScale HPCaaS enables businesses to turn traditionally substantial capital-intensive investments into more manageable and affordable operational expenditures.

Just as importantly for HPC-focused groups and divisions, TruScale HPCaaS allows additional compute, storage and acceleration resources to be provisioned via what Lenovo calls “push button” access, and are turned-off after requirements are met. That enables customers to flexibly access and use whatever resources they need or can afford. In other words, Lenovo’s TurScale HPCaaS helps ensure that the days of organizations having too few or buying too many HPC resources are fading fast.

Also see: Top Cloud Companies

Overall, Lenovo’s TruScale HPCaaS is intriguing both commercially and strategically. As noted above, the new solution offers the company’s customers options for acquiring, using and paying for HPC that are simpler and more transparent and affordable than traditional solutions.

That should interest organizations in HPC-intensive sectors, like academia, healthcare, manufacturing and pharmaceutical research. At the same time, the flexibility of Lenovo’s offerings could also help expand and accelerate the use of commercial HPC applications in other sectors and businesses.

Finally, it is worth noting that developing and delivering TruScale HPCaaS is clearly within Lenovo’s wheelhouse. The company has long been a force in both high end supercomputing (it has been the leading vendor in Top500.org since June 2018) and commercial HPC. Overall, Lenovo has the assets, knowledge and expertise to make TruScale HPCaaS work for itself and its customers.

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

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There is perhaps no hotter current topic in networking than private 5G (P5G). Finally, a wireless technology that’s as fast as wired networks but without the flakiness of Wi-Fi.

I’m certainly not suggesting that Wi-Fi is going away, as the low-cost relative to P5G and its near ubiquity make it ideal for general purpose connectivity. But if ultra-reliable, wireless connectivity is required, P5G is the only way.

Cisco, the de facto standard in networking, had yet to announce its plans in this area but it did so recently. To get an idea of the industry impact of having Cisco endorse P5G with its own solution, I recently talked to Ozer Dondurmacioglu, vice president of marketing at Celona. I tapped Celona as they were the pioneer in P5G and currently partners with the likes of Aruba Networks, now a part of HPE Enterprise, NTT with others coming.

We discussed Cisco’s approach versus Celona, how private 5G and Wi-Fi complement each other, their different use cases, and why a company like Cisco would be interested in private 5G.

Highlights of my ZKast interview, done in conjunction with eWEEK eSPEAKS, are below.

Also see: NTT Addresses the Why and When of Private 5G 

• Cisco recently announced its own private 5G managed service and high-end Wi-Fi 6E access points, targeting businesses with hybrid work environments. Cisco’s entry into private 5G shows there is a growing ecosystem of 5G networking services and devices to support critical infrastructure for hybrid work. The Cisco launch validates the need for this technology.
• Cisco’s approach is to leverage its service provider channel and offer P5G as a managed service. Celona chose to go the do-it-yourself (DIY) route with a solution that network pros can deploy and manage themselves. Celona is also developing certifications to ensures engineers have the proper skills to deploy.
• Managed services create an “as a service” option for business that want to offload operations of the P5G network. Neither is “better” per se but are complementary and create deployment options for customers. NTT will use Celona as part of their managed service. It’s likely many organizations will choose to start with a DIY solution and build a managed service layer on top as they scale.
• Cisco’s simultaneous release of private 5G and Wi-Fi 6E makes sense because they are designed to coexist as separate, yet complementary solutions. Wi-Fi and 5G can be deployed side-by-side in various settings, such as hospitals. Devices and guest users can connect to a Wi-Fi network, while patient bedside systems can go wireless and connect to private 5G. Both would be managed by the same networking team.
• Wi-Fi itself is going through an evolutionary process, with Wi-Fi 6E promising faster speeds, lower latency, and less congestion. Wi-Fi 6E is based on the same standard as WiFi 6, but it uses an extended spectrum. Wi-Fi 6E is not a substitute for private 5G since they are two distinct technologies that use different spectrums.
• The use cases for Wi-Fi 6E and private 5G will likely be different. Ad hoc network connectivity can be provided by Wi-Fi, whereas mission-critical connectivity is better suited for private 5G. There is more control over the quality of service (QoS) with private 5G because it’s clean spectrum and very infrastructure dependent.
• One Celona customer, California State University Stanislaus, deployed a private 5G as a backup wireless network to support remote learning and temporary research facilities on campus. Additionally, Wi-Fi traffic is backhauled over the network, so the university doesn’t have to invest in costly fiber when it installs surveillance cameras in the future. The Wi-Fi is reserved for students, while everything else runs on a different spectrum.
• Organizations considering private 5G should start by evaluating the performance of their applications over Wi-Fi—what works well and what doesn’t. This should be a joint effort between the networking teams and the application delivery teams. When both teams focus on the networking layer, they’ll get more out of their private 5G deployment.

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

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Companies need to evolve their manufacturing systems to support what can be called data-driven, end-to-end adaptive operations, where systems respond automatically to both known and new scenarios.

Yet this is easier said than done. As manufacturers strive to progress toward adaptive operations, they are hampered by the traditional and siloed architecture of their manufacturing systems. This architecture limits use cases that go beyond individual assets and one single function, and doesn’t allow for the acquisition, integration, and analysis of vast amounts of data.

Also see: Best Data Analytics Tools 

This is why the concept of digital twins is receiving more attention from manufacturing leaders.

At the core of the digital twin is the ability to bring data together from multiple sources, unify, and contextualize it. It offers a single solution for people and applications that can utilize this contextualized data repository for a variety of use cases.

As manufacturing leaders explore digital twins as a solution, there remain uncertainties around implementation. Here are the most frequently asked questions and what we’ve learned:

Digital twins provide relevant context, not just data. They allow information to be captured and relationships to be mapped throughout the organization. This supports a progressive evolution toward fully autonomous operations.

Traditional siloed manufacturing systems’ architectures are challenged by highly heterogeneous information. When combined with the lack of context surrounding the data, engineers working with these systems struggle to pull valuable insights that can assist in optimizing business operations.

Digital twin technology enables progressive learning and the ability to capture tacit knowledge, a key enabler of reaching autonomous operations. In fact, this technology stores and structures information in a way engineers and operators can understand, which ultimately reduces reliance on data analysts for day-to-day issues and leads to greater organizational efficiency.

Digital twins give engineers and operators control and accountability for the manufacturing data and related solutions. By combining the power of the digital twin and modern low-code/no-code tools, manufacturing leaders can provide data engineers a safe domain in which they can collaborate to develop new ways of optimizing operations.

Also see: Top Data Visualization Tools 

Yes. An inclusive and unified manufacturing architecture remains the foundation of manufacturing operations across industries. Manufacturing leaders can implement a digital twin in parallel with existing systems.

Leveraging data from existing systems, the digital twin allows manufacturers to extract greater value from years of investment without the need to “rip and replace.” By harnessing the power and flexibility of cloud platforms and technologies, digital twin technology allows manufacturers to capture data from both information technology (IT) and operational technology (OT) systems—from enterprise resource planning (ERP) to programmable logic controllers (PLCs) to supply chain to distribution—and contextualize it quickly and effectively.

Although a complete redesign of traditional systems is not required to take advantage of digital twin technology, previously integrated systems should not remain completely untouched. Manufacturing leaders should work to streamline their existing manufacturing architecture to:

• Create a data-driven templated approach and abstraction layer, which allows for simplification and standardization without having to replace expensive equipment at the site.
• Move high-value specific developments (e.g., what conditions upstream may be causing failures downstream), most of which are about data and AI, to the twin environment.
• Reinforce the “vertical” integration between ERP and shop floor systems, which establishes a robust and efficient execution engine.
• Integrate the twin domains—execution and optimization—progressively over time.

Also see: Top Business Intelligence Software 

MES are the backbone of the current manufacturing IT/OT stack. They will continue to be important as more manufacturers transition toward next-generation manufacturing operations management (MOM) architectures.

MES vendors are evolving their solutions to increase their footprint, leveraging the flexibility of the cloud, while at the same time, facilitating their integration into future-ready manufacturing architectures. This stems from the increased competition they face from IT players, including both specialized startups and cloud vendors.

As vendors continue to broaden their offerings and evolve their portfolios, they are focusing on platforms that include data historians, quality management systems, asset performance management, and warehouse management solutions. Since leaders today are offering a much-improved range of core capabilities, packed with industry-specific best practices, this is enabling faster rollouts with limited customization. 

Another major trend throughout the industry is cloud deployment. This significantly eases integration into the overall MOM architecture, while reducing the cost of MES deployments, all while enabling easier rollout across sites.

Additionally, MES vendors are increasingly providing more flexible offerings that can help manufacturers under budget and help capacity constraints pivot from large, CAPEX investments to incremental subscriptions. When combined with accelerated deployment, this makes MES deployments easier to justify financially. These developments have put MES back on decision-makers’ radars as they look to design their Industry 4.0 roadmaps.

Also see: Top Cloud Companies

About the Author:

Pascal Brosset, Global Production and Operations Lead, Accenture Industry X

The post Digital Twins Guide: Answering 3 Most Frequently Asked Questions appeared first on eWEEK.

Datadog and Prometheus both offer application performance monitoring (APM) platforms. Datadog is better established, and is graded as a Leader in the latest Gartner APM Magic Quadrant (MQ). Prometheus is open source and doesn’t meet the criteria for MQ entry demanded by the analyst firm – but it clearly has a number of strengths.

So there are some similarities in terms of features and functionalities, as well as quite a few difference between Datadog and Prometheus. How do users go about determining which one is best for their specific environment?

Here’s a look at both Datadog and Prometheus, how they compare, and their ideal use cases.

Also see: Best Data Analytics Tools 

Datadog is focused 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, server monitoring, and database monitoring fall within its feature set.

Datadog is particularly astute at dealing with the performance and visibility of multiple clouds operating on the network and in managing cloud services. The platform helps IT to drill down into performance data. It generates alerts about potential problems and helps IT to discover any underlying issues. It can assemble data from logs and other metrics to provide context that is helpful in minimizing incident response time.

Datadog’s user interface centralizes performance monitoring, alert management, and data analysis in one place. Recent additions to its platforms include network monitoring, security analysis, AIOps, business analytics, a mobile app, and an incident management interface.

Prometheus is free and is used for primarily for event and application monitoring. It records metrics in real time a database built using a HTTP. It also includes flexible queries and alerting. It allows slicing and dicing of collected time series data to generate ad-hoc graphs, tables, and alerts. It offers multiple ways of visualizing information. The software is part of the open-source Apache universe and supported by its own community.

Prometheus outdoes Datadog in terms of its ability to implement highly dimensional data models. Time series are identified by a metric name and a set of key-value pairs. And Prometheus has multiple modes for visualizing data: a built-in expression browser, Grafana integration, and a console template language.

Prometheus also stores time series in memory and on local disk in an efficient custom format. Scaling is achieved by functional sharding and federation. Alerts are defined based on Prometheus’s flexible PromQL and maintain dimensional information. An alert manager handles notifications and silencing.

Overall, both Datadog and Prometheus can perform general APM functions. Datadog is a Software-as-a-Service (SaaS)-based application. It has much broader applicability both in terms of APM capabilities as well as monitoring other areas such as security, networking, and infrastructure, as well as log management.

Prometheus’ strength lies mainly in event monitoring and metrics monitoring. But Prometheus offers something that Datadog does not – real-time monitoring. In terms of raw features, though, Datadog comes out ahead.

Also see: Top Business Intelligence Software 

Datadog installation is straightforward via the deployment of agents, though some command line scripting is required. It is relatively easy to customize dashboards and interfaces to the way you want them. The main interface offers extensive functionality. It’s great for experienced users, but it might be tough for new users who may be overwhelmed by the number of options.

Prometheus is said to be easy to use. But that likely only applies to people who live and breathe in the open source world. For those familiar with Apache, installation is considered simple. Each server remains independent for reliability and relies only on local storage. Written in Go, all binaries are statically linked and easy to deploy. Client libraries also allow relatively easy instrumentation of services.

Overall, Datadog gets the nod on implementation and ease of use. Without skilled open-source administrators as hand, the Prometheus interface can be difficult to master as well as being a little basic. And some find it difficult to set up and scale.

Datadog can work with a wide array of data formats and sources, but it is not a platform that is set up deal with a large number of information sources. Data formats like.xml, .csv, and .json are not supported, for example. That said, it can integrate well with other security and IT management tools. Datadog supports community APIs and extensions to integrate it into existing IT infrastructure. It is available for all major operating systems.

Prometheus supports over ten languages. Community resources are available for learning the application, training of users, and troubleshooting issues. Blogs, chatrooms, Slack channels, and mailing lists are available as part of support. On the integration front, existing exporters allow bridging of third-party data into Prometheus. Examples include system statistics, as well as Docker, HAProxy, StatsD, and JMX metrics.

Overall, Datadog wins on its integration options and community. Prometheus performs well in this regard, too.

Also see: Top Data Visualization Tools 

In years past, you could provide application performance management tools and software without having to worry a great deal about security. Today’s market is far more challenging in this regard. Most vendors now have to take care of security as a vital aspect of application development or face serious repercussions. Similarly, in APM, vendors now have to ensure they are providing a safe environment for users.

Being a SaaS application, Datadog has had to boost its game on security. It has been steadily adding security features in recent years. Prometheus relies more on other open source tools for security. 

Datadog prices out at around $15 per user, roughly (and it is $23 for the Enterprise version). Datadog has an open pricing policy with published prices, and generally low prices. Its pricing per-month options include per-host, per million events, and per GB of analyzed log files. But Gartner noted that some large deals entail large upfront spending. According to the analyst firm, this can lead to over- and under-provisioning.

Prometheus is free. But full functionality demands personnel well-versed in open source. That can come at a salary premium. If existing personnel or outside contractors already working for your firm are competent in Apache-based applications, though, Prometheus certainly wins on pricing.

There is no doubt that Datadog and Prometheus are both excellent tools. They both can solve a great many challenges related to application performance monitoring and beyond. Datadog is certainly the one to choose based solely on APM functions. Prometheus enters that arena to some degree, but its strengths lie elsewhere. For those specifically in need of event monitoring and metric monitoring tools, Prometheus comes out ahead of Datadog.

Datadog takes an infrastructure monitoring approach geared toward analytics and application performance. It is focused on performance measurement for cloud services and is particularly adept at measuring the performance of databases and servers, as well as measuring performance in a multicloud world. Since Datadog is aimed at monitoring infrastructure at scale, it’s used primarily by mid-sized companies and large enterprises. It is also favored by DevOps and IT to address cloud and infrastructure performance.

Prometheus is an open source tool and is likely to be favored by those familiar with other Apache-based platforms. It is unlikely that such users would think of using Datadog for event monitoring or metric monitoring. If your APM needs are basic, you can probably do just fine with Prometheus too. And any users wanting real-time monitoring should move Prometheus onto the shortlist.

Also see: Real Time Data Management Trends

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

Akamai recently acquired cloud company Linode. I spoke with Blair Lyon, VP of Cloud Experience at Linode, about the acquisition, and the benefits of using a so-called ‘alternative’ cloud provider.

• Big news: Linode has been acquired by Akamai.
• What is the ‘alternative’ cloud provider market, and what kinds of customers and users are drawn to this sector?
• I know that the word “Linode” is a combination of “Linux” and “node.” Is an open platform important to Linode’s users?
• What are the challenges these customers face?
• From where you sit, how do you approach or integrate with the hyperscalers?
• What’s the future of cloud and multicloud, as you see it?

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As soon as employees could work from anywhere, companies could start hiring from anywhere. Suddenly organizations that were once concentrated in geographic areas began considering how to support a global workforce.

As a result, over 70% of white collar workers are now fully remote, and in some areas as much as 52% of hybrid workers recently suggested they would quit their jobs if required to go back into the office full time. The extraordinary challenge of rethinking business operations on the fly for this new global movement has required IT to take a more proactive role.

I see three emerging areas where this new paradigm of work is already having a profound influence. Here are a few ideas on how we can confidently move toward a more global workforce:

Also see: Making the Shift to a Hybrid Work Environment

For years, one of the biggest challenges of remote work has been building and maintaining employee engagement. As health-driven lockdowns stretched into month after month of working from home, that engagement dropped to historic lows.

Employee engagement rates have sunk to between 20-30%, according to recent reporting by Gallup. Waves of people are leaving their jobs due to the appeal of new opportunities, burnout and frustration – a mass phenomenon news outlets have been calling The Great Resignation.

Companies have seized upon this disappearing engagement as a mandate to prioritize employee experience. Normally, that would be a problem for HR to solve, but in 2021, it also now also falls upon the IT team.

This counterintuitive reliance on IT came about because companies are trying to improve their employee experience by offering flexible hours, collaborative software tools, and the ability to work from anywhere – all initiatives that require IT to build on behalf of the organization. On an even more fundamental level, employees don’t have in-person meetings, watercooler chats, and personal facetime with co-workers anymore, so companies have to rely on technology to replace them.

IT leaders can take up this charge by proactively recommending new technology systems that will help build a strong employee experience, like virtual events, improved tools, and gifting platforms.

It’s also essential for companies to regularly collect feedback from this newly remote workforce that allows them to understand how to improve the employment landscape and monitor sensitive programs like diversity, equity and inclusion initiatives.

A major consequence of telling employees they can work anytime and anywhere is that people actually will work anytime and anywhere. For many companies – in particular, newly global companies – this means the organization now has to offer a work environment that embraces conflicting time zones.

This challenge multiplies when we consider how overloaded most employees are with meetings. Recent research from Harvard Business Review pointed out that, on average, 15% of an organization’s time is spent in meetings each week. There’s a reason the clarion call of the modern worker is: “this meeting could have been an email.” (In fact, our own research shows that 74% of workers have had that exact thought about meetings at some point.)

This challenge multiplies when we consider how overloaded most employees are with meetings.

A meeting-centric way of doing business isn’t sustainable and it isn’t practical for distributed workforces. Moving toward an asynchronous work environment is another area where IT teams need to lead the way.

Giving people the ability to collaborate on projects according to their own schedules is a much more effective and employee-friendly way to empower people. For example, if the purpose of a meeting is for one person to share information to attendees and collaboration is not required, the host could simply record a video of themselves discussing the information.

By sharing that recorded video, meeting attendees can watch at a time that suits them best. Other possibilities include an internal database for new resources or a virtual blackboard to share ideas, all deployed across departments or teams.

We are still in the early days of innovation with asynchronous work environments, but development is moving quickly. IT has an opportunity to evaluate needs, find solutions and make recommendations that work alongside company goals. The teams that do it well will help build a much more effective way of conducting business with a distributed workforce.

The next big thing for virtually all industries is automation. Companies that adopt automation will be able to act with much more agility and at far greater scale.

For example, IT teams can delegate answering frequent questions to a bot, such as “How do I reset my password?” – freeing up their time to spend on more complex work. Automation will become an irreplaceable element as more companies expand to a global reach.

But automation done wrong can significantly hinder a company. Replacing human judgement and experience with algorithms can create blind spots and increase biases.

Judicious use of automation will offer extraordinary benefits, including triggering next steps in projects, communicating with customers in any time zone, and connecting employees. Automation will also increase a company’s need for rock-solid IT teams that can stay ahead of trends and keep everything running.

Replacing human judgement and experience with algorithms can create blind spots and increase biases.

The next steps for virtually every IT leader will be understanding how best to adopt and deploy automation tools in their organizations. Along with evaluating options, IT will need to understand the company’s needs and bring in the “good” automation (which helps people work) instead of the “bad” (which subverts human judgment).

Now is the time for IT to shine. As companies and their workforces continue to evolve and expand across the globe, managing their technology has become an essential element of their success.

As with everything within IT’s purview, privacy and security are essential to these new experiences. Building your stack with a strong security foundation will help ensure all of these programs and services help move company goals forward.

Also see: Top Digital Transformation Companies 

About the Author:

Eric Johnson, CIO, Momentive

The post 3 Ways IT Can Support Your New Global Workforce appeared first on eWEEK.

Data flow has been stuck in the past, slowing down decision making in key areas such as supply chain, retail and finance. The data agility required during the pandemic was a baptism of fire for many organizations.

Companies needed to change operations and business models more quickly than their well-worn legacy applications would allow. Trying to rearchitect a business model so quickly ultimately exposed some significant gaps between IT systems – gaps that limited organizations’ ability to quickly move essential data.

Going forward, there is a clear and pressing need to get data “in motion” and at speed, to deliver the real-time insights craved by business leaders, employees and, most of all, the customers they serve.

As a result, businesses are looking at how they can best stream data events across their organization, whether for customer requests, inventory updates, or sensor readings.

It’s from this view that I base my predictions for three emerging trends in streaming data:

Also see: Real Time Data Management Trends

In 2022 and beyond, moving data in real-time between increasingly distributed application architectures will be a high priority.

In response, Forrester sees event-driven architecture (EDA) as the first key trend impacting software development this year. Specifically, “the growth of distributed application architecture hits a wall when only using synchronous APIs for integration due to fragility and scalability limitations. Over the years, EDA has gained more interest as it addresses this wall through APIs, microservices, and integration. We predict that in 2022, that interest will expand, with 35% of enterprises putting a major focus on EDA.”

EDA enables data to be moved in real-time event streams via an event mesh – helping link together previously unconnected processes across business running multiple siloed systems. A global study of C-Suite and IT architecture professionals has found that 85% of organizations are looking to incorporate real-time data and event-driven architecture (EDA) into their operations.

Leading edge industries looking to exploit real-time movement of data include financial services, retail and manufacturing – all industries where automation, APIs and IoT technologies are converging. As we expand out from this event-driven approach to move data in real-time, we see its mainstream impact on some more macro-level trends set to define the next twelve months.

Also see: Best Data Analytics Tools 

We’ve seen during the pandemic the need for smart inventory management, product tracking and supply chain optimization, all of which rely heavily on the essential flow of data. The data underpinning these processes is wide and all-encompassing – think location, weather, order status, the whole lead to cash, and source to pay process. All of this is becoming real-time and connected as event streams.

Enter 5G. If we combine this event-driven infrastructure with quicker connectivity advancements such as 5G, we see the potential for richer data to move quickly over an event mesh, between processes such as eCommerce, CX, warehouses, plants, transport and logistics, and business insights and reporting – all in real-time.

Statistics indicate that 5G increases data rates from 1GB/second to 20GB/second, increases data traffic from 7.2 exabytes/month to 50 exabytes/month and reduces latency from 10ms to <1ms.

Everyone involved in a supply chain, from finance to manufacturing and distribution and critically, the end-customer, will become more connected via real-time streams.

Also see: Top Data Visualization Tools 

In the next few years, how do you think we will buy a new pair of sunglasses, or the latest dress or shoe online? Your avatar will roam in a virtual mall in 3D, try items on in 3D and pay, all with the click of a button or a tap on a smart device. Welcome to the Metaverse, where research corroborates 66% of consumers say they are particularly interested in using AR.

Granted, we are still in the Nokia 3310 era of virtual reality at the moment; the smartphone form factor is probably around five years away. But the common theme will be moving data at speed to support such next-gen Metaverse interactions – and the key enabler in making this happen is event-driven data architecture.

Consider the potential impact of the Metaverse on another data-heavy industry, financial services, where transaction numbers are huge and systems have to run 24x7x365. Decentralized Finance (DeFi) is on the rise, with predictions that the market will reach $800 billion within the next year.

The convenience of a decentralized approach is clear to see, with financial institutions bypassing intermediaries to deal directly with each other in a secure, rapid manner. But to make Decentralized Finance a reality, financial institutions must be able to interface their core systems with a distributed ledger, and this is where event brokers – and in turn the event data mesh – come into play.

McKinsey believes there is more to be done to address the digital divide in customer experiences, and data will be at the core of this change: “With the rise of big data and predictive analytics, companies can now build a customer-management capability that is holistic, predictive, prioritized, and value-focused.”

The Metaverse (or Omniverse) by definition cannot be static and non-reactive or disconnected from the real physical world. It needs to be a 3D digital twin of the real world. Most important, it needs to be connected in real-time with the real world; it needs to be in motion. The Metaverse needs an event driven architecture and a real-time event mesh to support it.

Also see: Top Business Intelligence Software 

About the Author:

Sumeet Puri, Chief Technology Solutions Officer, Solace

The post 3 Emerging Trends in Streaming Data appeared first on eWEEK.

Observability is a hot tech topic yet has also become one of the industry’s most overused buzzwords. The term means understanding the behavior, performance, and other aspects of cloud infrastructure and cloud apps based on the data they generate, such as metrics, events, logs and traces (MELT). Observability relies on telemetry that comes from endpoints and services in multicloud environments.

The reason observability has grown in importance is that managing user experience has become increasingly difficult with the shift to the cloud. IT no longer has the tight control it once did over the infrastructure, as it now runs in public clouds, people’s homes, and other complex environments like edge computing.

As the old axiom goes, “you can’t manage or secure what you can’t see,” and observability lets you see a lot more.

The challenge for IT buyers is that as infrastructure converges, the ability to a have single source of truth becomes critical to IT operations, especially network and security operations. That’s why observability has gained so much traction among IT teams, which are seeking greater observability into increasingly complex computing environments.

In my latest ZKast interview, I spoke with Shehzad Merchant, Chief Technology Officer at Gigamon, a provider of network visibility solutions that can monitor usage all the way up to the infrastructure stack, including containers, virtual machines, and the cloud.

Merchant discussed the importance of observability and using network intelligence to address today’s challenges. Highlights of the ZKast interview, done in conjunction with eWEEK eSPEAKS, are below.

• The personas in observability are primarily DevOps (software development and IT operations) and CloudOps (cloud operations). These personas are scripting, automation programming, and dealing with very large data sets and complex infrastructure by querying data to extract meaningful intelligence.
• Observability is associated with infrastructure monitoring and application performance monitoring, but not security. Although observability can be applied to security, it’s not sufficient on its own. One of the first things bad actors do is to turn off telemetry, which creates gaps in observability.
• Observability typically looks at things from the inside out, while the outside in perspective is lacking in observability. That’s a challenge many organizations are facing across their hybrid cloud and multi-cloud environments.
• There needs to be consistency across all different cloud environments in terms of observability, telemetry, troubleshooting, and security. Vulnerabilities transcend boundaries and bad actors take advantage of inconsistencies.
• Deep observability—looking at network intelligence from the outside in—can be successfully applied to security. A key benefit is real-time, immutable telemetry that can’t be turned off or changed by bad actors. Deep observability offers a comprehensive view of hybrid networks and closes the gaps in observability.
• Network telemetry has a footprint that can go all the way up the stack. It can be used to identity apps, their behaviors, and dependencies. It’s relevant to both NetOps (network operations) and SecOps (security operations) teams. DevOps teams are good at scripting automation and working with infrastructure as code, but not at securing up the stack.
• Currently there is a shift where DevOps is intersecting with SecOps. A new DevSecOps persona is beginning to evolve. As long as the two teams have access to the right telemetry data, the best of both worlds can come together. This can help security teams move faster toward cloud observability.
• In conclusion, observability is here to stay. It’s evolving beyond MELT, thanks to deep observability. Gigamon’s vision is to provide a solution for deep observability and fill the gaps that exist today in infrastructure and performance monitoring, as well as security.

Also see: Top Cloud Service Providers & Companies 

The post The Future of Observability is Beyond MELT appeared first on eWEEK.

I spoke with Danny Fields, CTO of Avalara, about understanding the benefits, challenges, and contrasting strategies involved with deploying cloud native technology.

• Let’s look at the current environment for cloud native. Are companies really embracing it? Is it seen as too challenging?
• What’s a typical challenge for companies as they deploy with cloud native? Any advice to address this?
• How is Avalara building the next cloud, designed for compliance?
• The future of cloud native? What do you see over the next few years?

Listen to the podcast:

Watch the video:

The post Avalara CTO Danny Fields on Optimizing for Cloud Native appeared first on eWEEK.

Domo and Tableau are both well-respected business intelligence (BI) and data analytics platforms. Both were scored well by Gartner in its latest “Magic Quadrant (MQ) for Analytics and Business Intelligence Platforms.” Tableau came out ahead; Gartner graded Tableau as a Leader and Domo is a Challenger.

Data analytics applications are in heavy demand these days, as organizations seek to boost the quality of their data mining. Whether from relational databases, enterprise applications, unstructured data, social media, or other sources, there is more information than ever that is subject to analysis. Instead of a small team of data scientists slicing and dicing data, today data seekers come from from management, marketing, line of business heads, sales, and IT – all utilize analytics in their day-to-day activities.

As two popular analytics platforms, users often are forced to choose between Domo and Tableau. There are arguments for and against each solution. It is hard to say that you could go wrong with either selection. But which analytics platform is best?

Also see: Best Data Analytics Tools 

Tableau is very much focused on delivering 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. It differs sharply from traditional tools that require the user to analyze data in rows and columns, choose a subset of the data to present, organize that data into a table, and then create a chart from that table. VizQL skips those steps and creates a visual representation right away, providing visual feedback as you analyze.

Domo is a cloud-based platform that offers productive, efficient visualizations and dashboards, as well as a low/no-code environment for BI application development – this greatly expands the platform’s functionality. More recently, it has been improving its back-end to boost its data preparation and manageability features in an effort to win more of the power user market.

Tableau, of late, has been adding features like Ask Data in Slack (ask questions using natural language and automatically get data visualizations), Einstein Discovery in Slack (predictions in the flow of work), and Model Builder (collaboratively build and consume predictive models using Einstein).

Which has the best features? Domo’s latest update is Sandbox, a new development and testing environment built on its own platform. This helps customers more easily create and promote content into production across the enterprise. Its aim is to strengthen the enterprise capabilities of Domo and move into larger-scale BI and analytics. Tableau wins in terms of depth of analysis and the kind of features that data scientists, analysts and power users look for.

However, Domo wins when it comes to ease of use for business leaders who don’t fall into the power user category – a huge and important user cohort.

Newer users appear to find Domo a little easier to use than Tableau. Some complain that Tableau has a steep learning curve. Users should be well-versed in working with the R programming language, which is used heavily in statistical programming and data analysis.

But Tableau has been working to become easier to use. Its AI-powered analytics features lower the barrier to data science techniques. Tableau works really well when Excel and statistical data is being used as the raw material. For other formats, ease of use can suffer.

Domo is better at dealing with diverse data sets. Additionally, it doesn’t require knowledge of programming languages due to its no-code/low code approach. Everything needed is integrated within the tool.

The conclusion: Domo wins on broad usage by a non-technical audience whereas Tableau wins with more technical and advanced users. Key point: Domo is working to close the gap in that regard, and similarly, Tableau has also been attempting to make itself easier to use by inexperienced users.

Also see: Top Business Intelligence Software 

Tableau tries to differentiate itself with what it describes as an intuitive analytics experience with richer capabilities based on its patented VizQL engine. It 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 users to control performance, cost, and data freshness. Tableau also scores very well on live query capabilities and extracts, helping analysts to query faster. Its use of the R language makes it the winner on statistical capabilities.

Domo has solid analytics qualities, too. It does not match Tableau in that regard. But many users don’t need all the bells and analytics whistles baked into Tableau. Yet the vibrant Tableau user community swears by them.

Domo wins hands down when it comes to the cloud. It is completely cloud-based, whereas Tableau evolved from the on-premises world. Tableau does offer cloud-hosted solutions such as Tableau Online and Tableau CRM, but its strength lies in on-premises deployments and this is where much of its massive installed base resides. Thus, it can be challenging to scale out Tableau workloads in the cloud.

Some users noted that loading and savings data into Domo can take time. Tableau is better on this point, but it still takes a long time to load massive data sets. Therefore, Domo the gets the nod for cloud analytics, and Tableau for on-prem.

Also see: Top Data Modeling Tools 

CRM and business intelligence often go together. With Tableau now being owned by Salesforce, this comparison category goes beyond David vs. Goliath. Tableau has excellent marketing and enterprise product capabilities and is being integrating with Salesforce Einstein Analytics (known as Tableau CRM). An Einstein Discovery dashboard extension, for example, brings predictive modeling capabilities from Einstein to Tableau.

Domo offers plenty of great visualizations and dashboards for sales and marketing teams. But it stops short of CRM. Thus, for those wanting integrated CRM, Tableau wins. For those not part of the Salesforce universe, however, Domo might be a better option.

Tableau has its hands full in integrating with Salesforce. This creates a somewhat fragmented experience between Einstein Analytics and Tableau, though steady progress is being made in bringing both together. It won’t be long until those issues are resolved. As a result, Salesforce customers will be upsold to Tableau and vice versa.

Domo is largely free of integration headaches. Its cloud-based approach simplifies its deployment and use. Domo wins here.

Also see: Top Data Visualization Tools 

Tableau has a reputation for being expensive. By some estimates, it works out to about $75 per month per user for decent analytics functionality. But those who only want to interact with some basic dashboards can get it much cheaper. That said, the addition of Tableau CRM for a list price of up to $150 per user per month means newer functionality and Salesforce integration doesn’t come cheap.

Domo has also run into some pricing challenges. Its rates have dropped in recent times. But it works out to around $80 per user. In this category, Tableau will sometimes come out ahead for some bids, and Domo in others.

Tableau boasts a fanatical user base and a very loyal user community. Its user conferences attract large crowds. Its popularity is growing, too, partially through the distribution of a free platform known as Tableau Public. This is where people can share and explore data visualizations online. It contains over 3 million interactive visualizations. But it is in the data scientist, analytics specialist, and power user markets where its feature set wins the most plaudits. In these markets, it is likely to be favored over Domo.

Domo, on the other hand, has a reputation for being intuitive and easy to use. Housed wholly in the cloud, it does very well in markets that lack in-house data science talent. It can be up and running in no time and providing sales managers and execs rapid feedback, visualizations, and dashboards – and much of what they need on the feature-set front. Further, Domo has been adding enterprise-grade features to enable it to move up the food chain. For those that want decent analytics capabilities without requiring heavy duty capabilities, Domo probably wins.

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

There’s no such thing as a perfect software product. No matter how stable your application is, there’s bound to be occasions where things go wrong in production. To make the most and learn from each incident, it’s crucial that engineering teams regularly commit to doing post-mortem investigations.

This is especially important as companies grow and teams increasingly transition to a remote working environment. Even something that seems small can be analyzed and learned from in order to prevent future, and potentially more serious, vulnerabilities.

Having best practices in place for how to conduct a post-mortem software investigation around an incident is something that cannot be overlooked by technology providers.

Also see: The Best Project Management Software 

While there’s no one-size-fits-all solution for every team, there are several fundamental steps that should be taken to make it an effective process and ensure that incidents remain rare.

• Collect data during the incident. It’s important to collect as much data as you can in a single location, as the incident goes on. This includes server graphs, snippets from logs, and screenshots showing what was going on at each point in the incident. It doesn’t all end up being useful, but it’s good to have everything collected when you start going through the investigation in detail.
• Start the investigation right away. Get one of the developers/managers involved to take on the role of lead investigator, which means they’re in charge of making sure the investigation gets done, the post-mortem document gets filled in, and the debrief gets held. Starting it right away makes sure nothing gets lost.
• Review the results within a week. While things are still fresh, hold a debrief to review the post-mortem document as a group, discuss the action items, and make any edits needed. This can be a 30-60 minute video session with the team involved in the incident, as well as representatives from other departments (primarily the customer support team, but any impacted department should attend).
• Share the results. As soon as the debrief is done, everyone should get a chance to learn from it. Post it where the whole company has access to it for transparency – incidents shouldn’t be hidden away.

Also see: Digital Transformation: Definition, Types & Strategies

These best practices will set up teams for success, but as the future of work evolves, there are new challenges in following them.

For instance, companies are now facing employees working in all sorts of time zones, and a mix of remote and hybrid teams makes scheduling and coordination much more complicated. There are several additional measures that can help ensure that a post-mortem investigation remains effective, regardless of the environment:

• Assume async. Scheduling the debrief more quickly means that it’s harder to find a spot in everyone’s calendars. Rather than pushing the meeting further and further out, do more of the work asynchronously. Make sure the document can stand on its own, and use the quickest communications channels to ask people for their contributions. Also consider recording the debrief (easy with Zoom) so that anyone who couldn’t attend is also able to watch it later, so nobody has to worry about missing out.
• Complete the investigation quickly. It’s important to shorten the timeline expectations on the investigation. Collecting the data early avoids having multiple ongoing investigations, and allows everyone involved to get back to their sprint work sooner.
• Simplify the incident document template. Consider simplifying the template so that there are less sections to worry about, and make each section as easy as possible to fill in. In order to still be complete, this document should include sections for:
• Impact and Scope
• Trigger (what started the incident)
• Resolution (what ended up fixing it)
• Timeline of events
• Root Cause
• What went well
• What didn’t go well
• Action items
• Data & Analysis (all the charts)
• Ask for input from customer-facing teams right away. A customer success team always has great input and is able to help fill in gaps in the timeline. Reach out to them early so there’s time for their input to be added into the post-mortem document before the debrief. Waiting for the debrief is too late!
• Track action items in backlogs. Why track action item progress in an incident document when there is already a standard tool for tracking work? As soon as you can, get all action items from post-mortems so they can be assigned to backlogs and don’t get lost. It’s also beneficial to have automated reports set up to view the list of outstanding post-mortem actions—driven by a post-mortem label on the items.
• Have a section for “things we should do if we have time.” Realistically, not all action items are actually actionable—some are more aspirational or something everyone should keep in mind. In order to keep the action items clearer, include this section as a spot to put the things you think are important but you couldn’t turn into assignable/trackable work. It’s better to have a smaller set of action items that you actually do than a giant list of things you would like to do given infinite time.
• Keep it Blameless. This one isn’t actually new, but it’s well worth repeating! Be interested in what happened and what you’re going to do to fix it going forward, not in pointing fingers.

Remote work and fast-paced development don’t have to make incidents complicated. By following these best practices, software engineers and team managers can make the most of an incident post-mortem and focus on what matters most: learning from it and making things better for the future.

Also see: 7 Digital Transformation Trends Shaping 2022

About the Author: 

Jesse van Herk, Senior Manager of Product Engineering, Jobber

The post Best Practices for Fixing Software Problems appeared first on eWEEK.

Any organization that’s invested in an analytics tool like Power BI or Tableau knows that they’re only as good as the data you feed them. Challenges such as disparate sources, inconsistent data formats, and slow legacy systems all block insights from your business intelligence software and data analytics tools.

A common solution to this challenge is a data warehouse that enables data management, analytics, and advanced data science. The data warehouse helps organizations to facilitate data-driven decision making, find cost savings, improve profitability.

No matter the industry, size of the organization, technology involved, or data savviness, companies often ask: how do the benefits of a data warehouse justify the cost?

Also see: Best Data Analytics Tools 

The first major investment involved with a data warehouse will be either hiring a consulting firm to develop your modern data warehouse or dedicating internal resources to the task.

By hiring data consultants, you introduce additional costs in consulting fees, but yield results much quicker and therefore save time. If you choose to create an internal task force for the job, you reduce upfront costs, but altering day-to-day functions and the inevitable learning curve lead to longer development timelines.

The second investment is almost always necessary: you will need a tech stack to support your modern data warehouse. This may simply involve expanding or repurposing current tools or it could require selecting new technology. It’s important to note that pricing is different for each technology and varies greatly based on your organization’s needs and goals.

This project typically involves paying for storage, computing time, or computing power, in addition to a base fee for using the technology. In total, this can typically incur a yearly starting cost of $25,000 and up. To make it easier, each of the major data warehouse technology stacks (Amazon Redshift, Snowflake, and Microsoft Azure SQL Database) offer cost estimating tools. With a clearer understanding of what your costs will look like, let’s jump into why they are worth it.

Also see: Top Data Visualization Tools 

While the figure above is a considerable sum to invest in data analytics, many of your current technology investments will be phased out, or the costs will be reduced using modern technology.

Plus, your IT team will handle fewer cumbersome maintenance tasks due to automatic clustering, self-managed infrastructure, and advanced data security options. This allows your IT team to focus on more important business needs and strategic analytics.

With the volume and variety in data organizations track, it’s easy to find yourself stuck with messy data held in siloed systems. Modern data warehouses automate processes to eliminate duplicate information, reduce unnecessary clutter, and combine various sources of data together. All of this enables you to save money by storing data efficiently.

Think of it this way: if your data experts struggle to find key information, so does your technology – the extra compute time and storage costs more than you would expect. In contrast, implementing a system that stores your data logically and in a streamlined manner greatly reduces these costs.

Also see: Top Data Modeling Tools 

While it is more difficult to quantify the ROI here, dashboards and advanced analytics greatly enhance your employee’s ability to perform well and save money. Regardless of your industry, using a modern data warehouse to drive analytics empowers employees to perform better in several ways:

• Dashboards dramatically decrease the time employees spend finding and organizing the data. For many of our clients, reports that once took analysts weeks of effort are now able to automatically aggregate in seconds.
• Accurate data empowers better decision-making and yields creative problem-solving. You have the right information quicker.
• Real-time analytics enables you to quickly respond to significant business events. This gives you a competitive edge since you can easily retain customers, spot inefficiencies, and respond to external influences.
• Predictive analytics save you money by finding opportunities before you would need to act.

Developing a full-scale data warehouse requires time and money that may not be available at the moment. Yet the benefits of a data warehouse are necessary to remain competitive, and there are ways to build a modern data warehouse quickly and without the large upfront investment.

A modular data warehouse contains key strategic data and ensures that you gain the advantages of analytics almost immediately. On top of that, it provides a scalable foundation that you can add data to overtime until you incorporate all the data necessary for your business functions.

Also see: Top Business Intelligence Software 

About the Author:

Fred Bliss, CTO, Insights Practice, 2nd Watch.

The post Do the Benefits of a Data Warehouse Outweigh the Cost? appeared first on eWEEK.

Despite the recent massive shift to digital commerce, many marketers are still dealing with obstacles to implementing real-time, data-driven marketing strategies, processes, and campaigns.

Those are among the key findings of a Capgemini Research Institute survey of 1,600 global marketers. Only 11% of the survey participants reported driving “significant value” through real-time marketing. However, 93% of those respondents reported being “highly satisfied” with those marketing efforts, compared to 46% of the participants who weren’t using real-time campaigns.

The CMO survey identified three major types of barriers to data-driven marketing implementations: data access and integration, technology investment, and skill development.

While 45% of marketers reported having a customer data platform (CPD) that gives them a unified and a single view of customers, only 38% of the survey participants said their customer segment and persona data is available in a format they can use for decision-making. Properly formatted and unified data is the foundation of real-time marketing.

Less than half (49%) of the marketers surveyed said that their organizations “leverage technologies like AI for automated customer segmentation and grouping.” Just 37% “use automation to set up triggers to send the right message at the right time.” Without AI to analyze customer data and automation to leverage it in real-time, these organizations are at a disadvantage.

As technology and analytics play a larger role in marketing, many organizations are realizing that the marketing skill set needs to expand. Only 44% of marketers said they have enough AI, machine-learning and data analytics skills on their teams. Forty-five percent said they have the social digital marketing skills required for data-driven marketing. These skills are increasingly necessary to evaluate data and strategize campaigns.

Also see: What is Data Mining? 

In our discussions with CMOs, we’ve identified several ways that marketing leaders can prepare for and execute their organization’s transformation to include a data-driven approach. As with any new initiative, this transition will have the most impact if there’s a commitment from the top to pursuing and managing the changes involved.

It’s important to clarify and map a digital transformation strategy that centers on data-driven capabilities while simultaneously building the organization’s brand.

In addition to investing in data unification and technology to leverage unified data in real-time, an effective data-driven marketing strategy will build an environment that includes the right talent with the right skills to enable processes that make the most of those investments. This journey may require cultural changes and skills shifts in addition to changes in the way data is collected, shared, and used.

Especially if your organization collects data from many channels and touch points, a framework is necessary to define what data is worth collecting, how it will be used in the data-driven marketing program, and how often it will be refreshed.

While building the framework, don’t overlook the data that you can gather, with proper permission, from smart devices, chatbot interactions, immersive experiences, and other emerging touch points that can deliver leading indicators of customer behavior and preferences. An effective framework will also outline customer-data unification processes to build a 360-degree view of the customer. That requires getting data out of silos across the organization and into a single customer data platform (CDP).

With the CDP pulling customer data from many touch points, it’s possible to remap the customer journey to support real-time messaging and offers based on the customer’s most recent actions.

Social listening in particular can surface data that shows customer intent — a critical piece of information for determining the next best offer. The scope and speed of operating in real-time also require an investment in automation to deliver those next best offers and real-time messages. When the automation software can pull content directly from a well-organized content management platform, it’s easier to display the right content at the right time.

Data-driven marketing requires recruiting and/or upskilling talent with creative, data, and digital skills.

It also requires a cultural shift in the marketing department; this needs to be driven by leadership’s commitment to an analytical approach to meeting customer needs. Also important: an understanding of digital and performance marketing goals and best practices, and an overall drive to keep learning as technology and customer data sources evolve.

Because data-driven marketing requires first-party data from across the organization, marketing leaders can gain insights and work more efficiently by collaborating with their counterparts in IT, sales, and finance.

Creating cross-functional teams with members from each of these departments can help clarify goals, set clear KPIs, and help to drive sales. Marketers can also benefit from collaborating more closely with external agency partners to leverage customer data for maximum impact.

Also see: Top Data Modeling Tools 

Long-term brand-building efforts are still important, even during a near-term transition to data-driven, real-time marketing strategies. By earmarking budgets for efforts in both timeframes and including some brand-building in short-term initiatives, marketing leaders can see quick improvements in performance and stronger customer loyalty over the long run.

Implementing a data-driven marketing strategy and processes is a major undertaking that requires careful planning, technology investments, and a commitment to working in new ways with new skills, across departments. The potential return on this effort is a deeper and more comprehensive understanding of the customer that leads to better business outcomes and sustained growth.

Also see: 7 Digital Transformation Trends Shaping 2022

About the Author: 

Lee Dempsey, Principal and Capabilities Leader, Digital Customer Experience, Capgemini Americas

The post 6 Best Practices to Clear Marketing Data Roadblocks appeared first on eWEEK.