Steve Staso - Computing Solutions Strategist

Being the C-suite officer in charge of security requires handling significant pressure.  Cybercriminals are thriving; in 2021, the average cost of a data breach rose 10% and there were 17% more data breaches than in 2020.

Whether you’re new to the role of Chief Information Security Officer (CISO) or a seasoned CISO at a new organization, it’s critical to make an impact in the first 90 days at the job. Your actions in the first 90 days will lay the groundwork for your tenure or failure.

It is easy to fall prey to Shiny Object Syndrome and tempting to knock out every high-visibility task on your to-do list so you look (and feel) as if you’re getting things done. But the surest way to pave a path to success is to methodically and thoughtfully make a 90-day plan and stick to it.

This nine-step, week-by-week roadmap will guide you through crafting a competent cybersecurity program, driving digital transformation at your organization and leveraging SaaS technology to accelerate business plans and reduce operating costs.

Also see: The Successful CISO: How to Build Stakeholder Trust

Weeks 1-3: Identify and Understand Business Risk

In the first three weeks of your employment, learn about the business – the whole business. Explore how it operates, where dispersed teams are located, how the company addresses its market and provides services and goods. This is your opportunity to develop a deep understanding of the organization’s go-to-market strategy and supply chain.

Set up as many meetings as you can with other C-suite executives, the board of directors, and other company leaders to gain intimate insights into their business functions and responsibilities. Meetings with other technology officers is the best way to get a grip on the greater organizational tech stack, too.

In these first three exploratory weeks, gauge leadership’s willingness to shift left with security during the development cycle; shifting security left in the development lifecycle reduces costs and increases reliability by baking in security from the get-go.

Weeks 4-5: Get a Feel For the Organization’s Tech Processes and Begin Developing Your Team

Well-defined processes have a greater impact on cybersecurity than the tech stack does. In the fourth and fifth weeks of your new CISO role, meet with your team to learn about the processes in place, especially around project, incident and account lifecycle management.

Find out what’s working and what is not. Ask for any documented standards available and create a list of which processes and technologies lack documentation. Next, meet with other technology teams to identify which tech and processes overlap with your scope. Repeat the same exercise you did with your own team.

It’s also time to start getting to know your team well. Take one-on-one time to ascertain their career goals, and explore how you can help them meet those goals. Find out what training and professional development they’re interested in, what types of training the company has provided in the past, and then follow up with human resources to learn about career paths for your team’s growth.

This is a perfect time to discuss automation with your team members – they probably have ideas about where automation could benefit the organization.

Also see: Secure Access Service Edge: Big Benefits, Big Challenges

Week 6: Build a Strategy

Now that you’ve gathered information, it’s time to plan. Build a strategy to:

• Meet the organization’s overarching business strategy, objectives and goals.
• Meet your staff’s career goals and objectives.
• Augment staff with automation by alleviating them of repetitive, tedious tasks.
• Assess cyber risks facing the organization as one critical, holistic gap.
• Shift security left in the development lifecycle.
• Encourage SaaS adoption.
• Move all IT to a zero-trust architecture.

Week 7: Finalize Your Strategy and Begin Plan Implementation

It’s your seventh week, and your strategy and plan are good to go. Your next step is to run your strategy by your peers. Get feedback, be receptive to it, make adjustments, then present it to your executive committee for approval.

After it’s been approved, collaborate with the appropriate team(s) to identify tactics that will drive success. Collaboration is key here – it will cultivate rapport and help your new colleagues build trust in you. Then, start implementing your strategy.

Also see: Top Digital Transformation Companies

Week 8: Get Agile

Transitioning your team to Agile project management methodology will ensure fast wins of functional elements.

If your team is small, scrums will be appropriate and effective. If your organization already works on sprints, align your team’s sprint cycle with the engineering team’s duration. If no one else uses sprints, set your cycle to three-week sprints.

Week 9: Start Measuring and Reporting

You may or may not have access to historic reports when you start as a CISO. Either way, week nine is the right time to kickstart new benchmarks and a regular cycle of measuring and reporting back to your peers and to the executive committee.

Make sure to give credit to your staff and the other departments you work with! By nurturing the good will you established in your first several weeks, you’ll have stronger relationships with your colleagues – and that’s not a bad thing when you have to point out problems and gaps.

As your reporting becomes regular, start educating and communicating about cybersecurity to the whole organization. Encourage partnership, engagement, and celebrate successes rather than focusing on problems. Create a “security champions” program across departments in which your champions are encouraged to report when things go wrong and rewarded for engaging.

Also see: Best Website Scanners 

Week 10: Conduct a Thorough Pen Test

Penetration testing is how you will get some data on how bad things really are. You should plan for, schedule, and execute a thorough pen test (or red team exercise) of the infrastructure and applications.

Find a pen-test partner that follows either the PTES or OSSTMM 3 methodology for infrastructure testing and that uses the OWASP testing framework for each application.

Week 11: Get Moving on a Zero Trust Authentication Framework

Transitioning to a zero-trust authentication (ZTA) framework is a crucial step in your first 90 days as a CISO.

In a ZTA, users are not given access by default, but they’re given access once they’re authenticated. A ZTA will enhance the security posture of your organization. The first step of your ZTA should be to begin sunsetting passwords wherever possible and transitioning to secure multi-factor authentication (MFA).

Also see: Secure Access Service Edge: Big Benefits, Big Challenges

Week 12: Evaluate SaaS Providers

Starting your new CISO role by diving into buying guides and SaaS-vendor comparisons is tempting, but it makes much more sense to do this once you have a grasp of the company, your strategy, the existing tech stack and budgets.

When you begin evaluating SaaS providers, certify prospective vendors’ compliance with the CSA CCM, registration in the CSA STAR Alliance, or at the minimum, SOC 2 type 2 attestation.

If you evaluate vendors that do not meet these criteria, you will need to develop a thorough program to evaluate their security. It’s critical to evaluate SaaS vendors against objective, third-party assessments and not simply the vendor’s shiniest marketing efforts.

90 Days Down

Following this roadmap will help you reach your 90th day with a solid foundation: a functioning cybersecurity team, data baselines for repeatable reporting, trust and rapport with new colleagues and teams, a list of opportunities for digital transformation, and an intimate understanding of most facets of the organization.

Congratulations on your first 90 days!

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

About the author:

Eyal Gruner is the Co-founder and CEO of Cynet

The post Improve Your Cybersecurity in 90 Days: Guide for Security Pros appeared first on eWEEK.

Data mining tools are enjoying a dramatic increase in interest, due to data trends driving today’s businesses. Clearly, data analytics is now firmly embraced by businesses of all shapes and sizes, and use of data mining tools is a core practice of digital transformation.

Also see: Best Data Analytics Tools 

Success in using data mining tools is all about two factors:

First, it’s about which data mining techniques you use to extract meaningful insights from a vast ocean of data. This is accomplished by gathering and prepping raw data from innumerable sources and subjecting them to algorithms and analysis to find patterns and common elements. Additionally, it’s about which data mining tools you use. To be sure, there’s an enormous amount of variety in data mining tools. So let’s dive in.

• What is Data Mining?
• What are Data Mining Tools?
• Best Data Mining Tools and Software
• SAS Visual Data Mining and Machine Learning
• Oracle Machine Learning on Autonomous Database
• Talend Data Fabric
• RapidMiner
• IBM SPSS Modeler
• Knime
• Orange
• Qlik

Also see: Top Data Visualization Tools 

What is Data Mining?

Data mining is classified as an advanced data analysis technique. It finds the hidden relationships and patterns that other types of analysis might miss. It incorporates artificial intelligence (AI) and machine learning to spot customer needs, find ways to boost revenue and profitability, and engage more effectively with audiences. Using data mining tools often requires data visualization and business intelligence techniques.

These days, data mining is more powerful than ever. It can certainly perform text mining, but it is capable of far more sophisticated knowledge discovery techniques. Data mining can now take advantage of abundant compute power, and memory to crunch numbers and data rapidly and with more accuracy.

Also see: Data Mining Techniques 

What are Data Mining Tools?

Data mining tools can be deployed on-premises on in the cloud. Some are offered as traditional software, some are open source, and many exist as software as a service (SaaS) solutions.

Data mining tools use machine learning algorithms and statistical models to make sense of massive data sets. Whether it is social media platforms, CRM systems, website analytic tools, mobile applications, organizational databases, or other enterprise systems, data mining software helps make decisions smarter, and provides better data on which to base strategy.

Not all tools use the same approach. Some of the data mining techniques used are descriptive analytics, cluster analysis, rule learning, classification, predictive analytics, regression analysis, forecasting, and risk assessment. Some tools favor one approach. Others combine several. In many data mining techniques, data visualization plays a core role. Text mining might be employed.

Also see: Top Business Intelligence Software 

Best Data Mining Tools and Software

eWeek evaluated many different data mining tools. Here are our top picks, in no particular order:

SAS Visual Data Mining and Machine Learning

SAS Visual Data Mining and Machine Learning (VDMML) is a comprehensive visual – and programming – interface that supports the end-to-end data mining and machine learning process. SAS VDMML, which runs in SAS Viya, combines data wrangling, exploration, feature engineering, and modern statistical, data mining, and machine learning techniques in a single, scalable in-memory processing environment.

Key Features

• Access, profile, cleanse and transform data with self-service data preparation capabilities with embedded AI. Can combine unstructured and structured data in integrated machine learning programs.
• Best practices templates enable a consistent start to building models. Analytical capabilities include clustering, regression, random forest, gradient boosting models, support vector machines, natural language processing, topic detection.
• Users can visually explore data and create and share visualizations and interactive reports.
• Network algorithms explore the structure of networks – social, financial, telco and others.
• Modelers and data scientists can access SAS capabilities from their preferred coding environment – Python, R, Java or Lua.
• Includes access to a public API for automated modeling; or use an API to build and deploy custom predictive modeling applications.

Pros

• Automatically generate insights, including summary reports about a project and champion and challenger models. Simple language from embedded natural language generation facilitates report interpretation and reduces the learning curve.
• Automated feature engineering selects the best set of features for modeling by ranking them to indicate their importance in transforming data.
• Generative adversarial networks (GANs) generates synthetic data, both image and tabular, for deep learning models.
• Scalable in-memory analytical processing provides concurrent access to data in memory in a secure, multiuser environment and distributes data and analytical workload operations across nodes – in parallel – multithreaded on each node for very fast speeds.

Cons

• As the big name in analytics, SAS is typically more expensive than other tools.
• There are a great many tools and sub-tools within the SAS ecosystem. Great for data scientists and analytics experts, but it can sometimes be challenging for the less skilled.

Oracle Machine Learning on Autonomous Database

Oracle Machine Learning on Autonomous Database uses more than 30 in-database scalable machine learning algorithms accessible from SQL and Python APIs (including OML4SQL and OML4Py). It supports classification, regression, clustering, association rules, feature extraction, time series, anomaly detection, among other machine learning techniques.

Key Features

• Integrated notebook environment supports SQL, PL/SQL, Python, and markdown interpreters, where the same notebook can contain SQL and Python paragraphs – allowing users to choose the most effective language for the task– and users can version notebooks and schedule notebooks to run.
• Automated machine learning (AutoML) from a Python API (OML4Py) and no-code user interface (OML AutoML UI).
• Python API (OML4Py) for scalable data preparation and exploration, and model building, evaluation, and scoring.
• Store Python scripts and objects in the database for unified security, backup, and recovery, and use with embedded Python execution.
• Run user-defined Python functions in database spawned and controlled Python engines (embedded Python execution), with built-in data-parallel and task-parallel features.
• Deploy in-database and third-party ONNX format models for real-time scoring via a RESTful service for model management and deployment.
• Deploy models from AutoML UI directly to OML Services.

Pros

• Minimize or eliminate data movement for Oracle Autonomous Database data.
• Score data using in-database models with integrated SQL prediction operators in SQL queries.
• Data and model governance via Oracle Autonomous Database security models in development and production.
• On-premises and cloud availability for ML capabilities.
• Oracle tools integration, including Oracle Analytics Cloud, Oracle Streaming Analytics, and Oracle APEX.

Cons

• Use cases requiring GPU compute, such as deep learning image CNNs, are not supported.
• OML Notebooks, OML AutoML UI, and OML Services are available on Oracle Autonomous Database – Shared only.
• Solution is optimized for data residing in Oracle Autonomous Database so it is best for this platform.

Talend Data Fabric

Talend Data Fabric is a single, unified platform that centralizes data integration, quality, governance and delivery. It is unique in that it is designed to consolidate data activities, providing intelligence and collaboration capabilities to meet data workers at their technical level, in a cloud-based platform.

Key Features

• 1,000+ built in connectors and components to leading SaaS and on-prem applications, including: Marketo, Workday, Salesforce.com, SAP, ServiceNow.
• Data quality, preparation, and governance in a unified platform.
• Application and API integration for microservices.
• Supports most databases and storage including: AWS, Azure, Google Cloud, Snowflake, Microsoft SQL Server, Oracle, Greenplum, SAS, Sybase, Teradata; and big data platforms including: Cloudera, Databricks, Google Dataproc, AWS EMR, Azure HDInsight.
• Native Spark streaming to support real-time big data messaging systems.

Pros

• Talend Data Quality Service scales the use of healthy data using automated frameworks to establish a data quality framework.
• Ready-to-use dashboards, ongoing monitoring and reporting.
• Trust Score for Snowflake: the only solution that profiles entire datasets inside Snowflake Data Cloud using native Snowflake processing to ensure data professionals can assess quality at scale for healthy, analytics-ready data.
• Self-service data APIs make creating and operationalizing compliant, no-code APIs happen fast.

Cons

• Those without Java expertise may find it challenging.
• The learning curve can be steep.

RapidMiner

RapidMiner is a business analytics workbench with a focus on data mining, text mining, and predictive analytics. It uses a wide variety of descriptive and predictive techniques to give the insight to make profitable decisions. RapidMiner, together with its analytical server RapidAnalytics, also offers full reporting and dashboard capabilities.

Key Features

• Instead of holding complete data sets in the memory, only parts of the data are taken through an analysis process and the results are aggregated in a suitable location later on.
• Fast performance as it takes the algorithms to the data instead of the other way around.
• Graphical connection of Hadoop for the handling of big data analytics.
• Meta data propagation to eliminate trial and error.
• RapidMiner can continually observe the storage and runtime behavior of analysis processes in the background and identify possible bottlenecks.

Pros

• No software license fees.
• Flexible/affordable support options.
• Fast development of complex data mining processes.
• Installation takes less than 5 min.

Cons

• Can be a steep learning curve.

IBM SPSS Modeler

IBM SPSS Modeler is a visual data science and machine learning solution designed to speed up operational tasks for data scientists. Organizations use it for data preparation and discovery, predictive analytics, model management and deployment, and machine learning to monetize data assets.

SPSS Modeler is also available within IBM Cloud Pak for Data, which is a containerized data and AI platform that lets you build and run predictive models on cloud and on-premises.

Key Features

• Finds patterns in text, flat files, databases, data warehouses, and Hadoop distributions in a multi-cloud environment.
• 40+ out-of-box machine learning algorithms.
• Integrate with Apache Spark for fast in-memory computing.
• Speed data analysis within-database performance and minimized data movement.

Pros

• Takes advantage of open source-based tools such as R and Python.
• Empowers data scientists of all skills, programmatic and visual.
• Facilitates a hybrid approach — on-premises and in the public or private cloud.
• Start small and scale to an enterprise-wide, governed approach.

Cons

• Can be expensive.
• Customization can be challenging.

Knime

The Konstanz Information Miner or KNIME  is an open-source data analytics, reporting, and integration platform. It integrates various components for machine learning and data mining through modular data pipelining based on a building-block approach.

Key Features

• KNIME Analytics Platform is open source software for data science and data mining.
• An active community is continuously integrating new developments.
• KNIME attempts to make understanding data and designing data science workflows and reusable components accessible to everyone.
• KNIME Server is for team-based collaboration, automation, management, and deployment of data science workflows as analytical applications and services.

Pros

• Non experts are given access to data science via KNIME WebPortal or can use REST APIs.
• Drag and drop style interface without the need for coding.
• Models each step of a data analysis, controls the flow of data, and ensures work is current.
• Blend tools from different domains with KNIME native nodes in a single workflow, including scripting in R and Python, ML, and connectors to Spark.

Cons

• Interface is a little clunky.
• Can hog memory resources.

Orange

Orange is an open-source machine learning and data visualization tool. It helps to build data analysis workflows visually, and comes with large toolbox. 

Key Features

• Perform simple data analysis with data visualization.
• Explore statistical distributions, box plots and scatter plots, or dive deeper with decision trees, hierarchical clustering, heatmaps, and linear projections.
• Interactive data exploration for rapid qualitative analysis.

Pros

• Focus on exploratory data analysis instead of coding.
• Defaults make fast prototyping of a data analysis workflow easy.
• Easy to learn so is used at schools, universities and in professional training courses.

Cons

• Advanced analysis can be challenging for some users.
• Graphics could be improved.

Qlik

Qlik Sense is a data analytics and data mining platform that includes an associative analytics engine, AI capabilities, and operates in a high-performance cloud platform. It empowers executives, decision-makers, analysts, and anyone else with BI that users can freely search and explore to uncover insights.

Key Features

• Create a data literate workforce with AI-powered analytics.
• Insight Advisor, an AI assistant in Qlik Sense, offers insight generation, task automation, and search & natural-language interaction.
• Available as SaaS or a choice of multicloud or on-premises.
• Associative Engine allows people to explore in any direction.
• Combine and load data, create smart visualizations, and drag and drop to build analytics apps.

Pros

• Insight Advisor gives suggested insights and analyses, automation of tasks, search and natural language interaction, and real-time advanced analytics.
• Interactive mobile analytics.
• Embedded Analytics.

Cons

• Basic users may struggle to learn it at first.

The post Best Data Mining Tools & Software 2022 appeared first on eWEEK.

Data analytics tools and software deliver deep insights into wide-ranging business events. What data analytics and big data are used effectively, they can fuel faster and better decision-making. This offers significant competitive advantage and boosts digital transformation.

Clearly, data mining using data analytics software is at the center of business success; and of course, using optimal data mining techniques makes all the difference. Yet the volume, variety and velocity of big data keeps on growing—making the task more challenging than ever. Some companies hire data analysts and business intelligence professionals; other have a team of data scientists helping them decode various data sources. In sum, having the right data analytics tools is part of the solution; having skilled big data experts is just as important.

Also see: Top Business Intelligence Software 

Like most software, selecting the right data analytics platform is critical. Ensuring that a data analytics solution connects and interacts with other data sources is essential. It’s critical for monitoring both your edge computing deployment and cloud provider. Likewise, monitoring data in motion across an enterprise and out to a supply chain is increasingly important.

Best-in-class big data analytics solutions offer numerous features and capabilities for making sense of data at all levels. These include real-time visualizations, machine learning and AI capabilities and, in some cases, digital twins. They are powerful tools used by data scientists, data analysts, and other business intelligence pros. Understanding what precisely a data solution delivers is vital as organizations look to build out more expansive and complex data frameworks.

• How to Choose Data Analytics Tools
• Top Data Analytics Tools & Software
• IBM
• Microsoft
• MicroStrategy
• Qlik
• SAP
• SAS
• Sisense
• Tableau
• ThoughtSpot
• TIBCO
• Additional Market Leaders: Data Analytics Tools
• Vendor Comparison Chart: Top Data Analytics Software

Also see: Top Data Mining Tools 

How to Choose Data Analytics Software

Selecting a platform for data analysis isn’t a job for the faint of heart. There are numerous factors, features and frameworks to consider. Here are three critical steps that will guide you to the right decision:

• Analyze your needs – with an eye toward your particular staff. The process starts with an evaluation of what  your organization’s data requirements are and what objectives it has. It’s important to look at who will be using the data – line of business users versus data scientists, for example; how widely used do you expect the platform to be? Does it need to be robust enough for a team of data analysts? Additionally, understand what data sources are required to build models and insights, and what type of analytics is required: data visualizations, statistical analysis, predictive analytics or other specialized needs.
• Review vendors – think scalability. It’s essential to know whether a big data analytics solution can manage data effectively and consistently deliver the desired results. There’s also the issue of scalability. As supply chains and business partnerships expand, data analytics tools and business intelligence applications must be equipped to ingest data from new sources, process this data effectively and produce actionable information and results. Do you need a data warehouse? A date warehouse helps organize, prepare and data mine your data sources – it’s an integral part of an advanced business intelligence solution.
• Select a solution – with the understanding that switching is hard. Changing platforms is expensive and extraordinarily complicated. Consequently, it’s important to match your organization’s needs with the right solution provider on the first try; it’s worth more homework upfront. Key factors in selecting a vendor include speed, performance, user interface (UI) and usability (UX), flexibility, scalability, security, the vendor’s roadmap, and the vendor’s commitment to support. Pay particular attention to the vendor’s service level agreement (SLA). In the end, the upfront price isn’t as important at total cost of ownership (TCO).

Also see: Top Data Visualization Tools 

Top Data Analytics Tools & Software

Here are 10 of the top big data analytics solution providers:

IBM

Key Insight: Big Blue offers a wide array of analytics solutions and tools. However, Cognos Analytics with Watson is a leader in delivering insights through data visualizations. It taps the Watson AI and machine learning engine to blend data and deliver broad and deep insights. The platform offers natural language processing and contextual forecasting, including predictive analytics. It also includes integration with social media platforms.

Pros

• Powerful ad hoc reporting tools.
• Advanced AI through the Watson platform.
• Suitable for line of business users as well as data scientists.
• Strong compliance and security features, including single sign on and object level security.
• On premises and cloud options available.

Cons

• Better suited to existing IBM customers; the platform can be difficult to integrate with outside data tools.
• The analytics dashboard and reporting functions is geared for pro users.
• Large footprint that consumes significant resources.

Microsoft

Key Insight: Power BI is an analytics platform optimized for Azure cloud. It delivers rich data visualizations through a highly scalable self-service model. The platform supports end-to-end business solutions by connecting Power BI with other Microsoft Power Platform Products—and to Microsoft 365, Dynamics 365, Azure, and hundreds of other apps. It is ranked as a Leader by both Gartner and Forrester.

Pros

• A top performing platform for AI and ML.
• Strong data ingestion engine and data management functions.
• Superior data visualizations.
• Enormous user base translates into frequent updates and strong community support.

Cons

• Difficult to use with non-Microsoft tools and applications.
• Can have a steep learning curve.
• Not a good fit for some mobile platforms and devices.
• Premium tier is expensive.

MicroStrategy

Key Insight: The vendor bills its BI and Analytics platform as a way to embed “intelligence everywhere.” It connects more than 200 data sources—including top platforms like Snowflake—en route to real-time visualizations for both PCs and Macs. It supports location-based analysis and delivers self-service dashboards that can be used for sophisticated drill-down analysis.

Pros

• Powerful engine integrates with most major data platforms through a robust set of APIs.
• Strong support for mobile devices.
• Solid security features are built into the platform.
• Specialized templates and tools for vertical industries such as finance, healthcare, retail, tech and government.

Cons

• Interface can be challenging.
• Unstructured data can be difficult to integrate.
• User base isn’t as large as other vendors.

Qlik

Key Insight: A longtime vendor in the BI and data analytics space, Qlik offers a moderately priced solution, Qlik Sense, that delivers robust functionality on-premises or in the cloud. It ties together existing databases and data sources and provides self-service visualizations and reporting that can be used across different groups, departments and functions. The platform incorporates AI and ML to deliver active intelligence.

Pros

• The platform offers a strong dashboard and easy-to-use tools.
• Highly scalable and flexible analytics capabilities.
• Capable of handling large volumes of data.
• Supports multi-cloud infrastructures; includes strong governance features.
• Integration with numerous other data tools, including Tableau and Power BI.

Cons

• May require customization and third-party extensions.
• Lacks some key reporting and exporting capabilities.
• Lower vendor profile and a smaller user base means less community-based support. 

SAP

Key Insight: SAP’s presence in the enterprise application space makes it a good choice for organizations already on the vendor’s platform. SAP Analytics Cloud delivers a streamlined solution with advanced predictive analytics and planning functions. It delivers powerful self-service visualization and simulation tools, real-time insights and integration with numerous outside data sources.

Pros

• Delivers a cloud native platform.
• Powerful dashboard delivers broad and deep insights into data.
• Supports numerous types of analysis, including visualizations, predictive analytics, augmented analytics and statistical analysis.
• Offers strong AI and ML capabilities.

Cons

• Can be complex and difficult to set up.
• No on-premises solution.
• Expensive, particularly for small- and medium-size organizations.
• Limited support for off-premises applications running on desktops and mobile devices.

SAS

Key Insight: A pioneer in the data analytics space, SAS offers a sophisticated framework for data analytics. This includes numerous applications that address different requirements. Its visual analytics solution is among the most advanced available, offering a sophisticated dashboard, a low code framework and AI/ML. It connects to numerous data sources, performs interactive data discovery and accommodates augmented analytics, chat-enabled analytics, location analytics and much more.

Pros

• Fast and efficient data processing, including strong AI and ML capabilities.
• Flexible low-code environment for building mobile apps.
• Powerful security, administration and governance features.
• Drag-and-drop interface is easy to use.
• Flexible and highly scalable.
• Large user base.

Cons

• Potentially expensive and difficult to learn.
• Some users desire expanded customization capabilities.
• Installation and initial setup can be difficult.

Sisense

Key Insight: The vendor’s data analytics capabilities are among the most sophisticated, and the solution is designed primarily for data scientists, analysts and power business users. The self-service cloud platform connects cloud and on-premises data and includes advanced functionality such as AI and ML. It incorporates low-code and no-code tools and supports numerous types of output, including predictive analytics and visualizations.

Pros

• Robust APIs and strong data discovery capabilities.
• Fast performance and intuitive interface with drag and drop capabilities.
• Highly customizable.
• Highly rated customer support.

Cons

• Better for power users. Can be difficult to set up, learn and use.
• Expensive

Tableau

Key Insight: The widely popular data analytics solution, now part of Salesforce, delivers excellent and highly interactive visual dashboards in real time. It connects to a wide range of data sources, handles discovery and data ingestion deftly, and taps AI and ML to deliver an easy-to-use solution that’s ideal for line of business users but sophisticated enough for data scientists. Not surprisingly, there’s a strong focus on CRM, though the solution is suitable for different tasks across a wide range of industries.

Pros

• A powerful and highly flexible framework produces outstanding dashboards and visualizations.
• Extremely intuitive UI.
• Large user base translates into strong community support.
• Excellent integration with Salesforce CRM.

Cons

• Expensive, particularly for smaller organizations.
• Some user complaints about customer service and support.
• Lack of direct integration with AWS S3.

ThoughtSpot

Key Insight: The vendor focuses on an approach it calls “search and AI-driven analytics.” The cloud-based solution delivers an appealing front end for data. It offers powerful tools for discovering, ingesting, connecting and managing data—through APIs and AI/ML. ThoughtSpot embeds search and insight-driven actions into apps using a low-code developer-friendly platform. It supports non-technical users and delivers a single source of truth, with robust security and governance.

Pros

• Supports numerous data types and provides numerous and flexible report templates.
• A powerful Google-like search engine and accompanying AI/ML supports complex natural language queries and questions.
• Delivers rich and flexible visualizations.
• Ideal for non-technical users.

Cons

• Performance may lag on extremely large data sets.
• Some users complain about the lack of tutorials and customer support.
• Some multi-tenant/multi-cloud features and support are lacking. 

TIBCO

Key Insight: Tibco has a solid reputation in the BI and analytics arena. Spotfire delivers real-time data visualization through NLQ powered search, AI-driven recommendations, and direct manipulation. It supports both on-premises and cloud frameworks, with a powerful and highly scalable analytics engine. The result is immersive dashboards, predictive analytics, geolocation analytics, and streaming analytics. Spotfire Mods allows organizations to build custom analytics apps.

Pros

• Includes more than 60 built-in connectors and support for almost every data type through customer APIs.
• Strong AI engine generates recommended visualizations on the fly.
• Handles extremely large data set well.
• Delivers tight coding integration through python and R.

Cons

• User interface isn’t particularly intuitive and drag-and-drop features are sometimes absent.
• Customizations can be difficult.
• User community is smaller than competitors.

Data Analytics Tools: Additional Market Leaders Google Charts

Google offers a free data visualization tool that works with JavaScript to generate presentations and reports.

OpenText

OpenText provides a variety of tools for generating data insights across a variety of vertical industries, including finance, automotive, healthcare and energy.

Birst

The vendor aims to deliver meaningful data insights from the boardroom to the shop floor. It focuses on pre-built industry and role-specific content and metrics. 

Domo

A “BI for All” framework is at the center of Domo’s solutions. It supports strong data integration, BI and analytics, intelligent apps and embedded analytics.

Zoho

The self-service tool delivers robust visualizations via intuitive dashboards. Powerful connectors pull together a multitude of data types and formats.

Xplenty

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

KNIME

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

 HubSpot

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

RapidMiner

The advanced analytics platform taps machine learning and AI to generate a wide variety of data insights, including predictive analytics.

Yellowfin

The vendor focuses on appealing dashboards to promote digital storytelling. The solution incorporate powerful natural language capabilities.

Data Analytics Tools: Vendor Comparison Chart

Data Analytics Tool

Pros

Cons

IBM Cognos Analytics

Advanced visualization tools suitable for IBM customers

 

Geared for IBM environments

Microsoft Power BI

Extensive capabilities tied to Microsoft products, including Azure

 

Not ideal for use with non-Microsoft applications and products

MicroStrategy Platform

Excellent choice for connecting data

 

Expensive, interface can be challenging

Qlik Sense

Powerful and versatile platform with strong AI and ML

 

Lacks some advanced functionality found in other leading solutions

SAP Analytics Cloud

Powerful BI and analytics capabilities for SAP users

 

Expensive

SAS Visual Analytic

Sophisticated BI and analytics, with excellent AI and ML

 

Can be challenging for non-technical users

Sisense Platform

Advanced features and capabilities with robust APIs and top-tier performance

Expensive. Better suited to power users

 

Tableau

Outstanding UI and UX, with deep Salesforce/CRM integration

Can be pricey

 

ThoughtSpot

Advanced AI and natural language search deliver powerful analytics capabilities

Performance can lag on extremely large data sets

 

TIBCO Spotfire

Highly flexible platform that’s ideal for data scientists and power users

 

Interface can prove challenging, particularly for non-technical users

The post Top Data Analytics Tools & Software 2022 appeared first on eWEEK.

COVID-19 accelerated a buy-from-home economy, expanding the role of the contact center beyond customer support to building customer relationships. According to a new report released by Talkdesk, the majority of customer experience (CX) professionals believe their contact center is a main driver of customer loyalty.

For this reason, companies are increasingly investing in engagement tools, strategies, and technologies designed to retain contact center agents, who have become a valuable asset.

The report, titled The Future of Customer Loyalty, is based on three online surveys conducted in 2021. The first survey was conducted among 650 CX professionals in July 2021 across 10 global markets. The second survey was fielded among 365 CX professionals in August 2021 across the U.S., Canada, and the U.K.

The survey participants are leaders and managers in CX, customer service, CX operations, as well as contact center agents from companies with more than 200 full-time employees. The third survey, conducted in October 2021, includes 5,513 consumer interviews across 12 global markets.

Also see: Bring Your Own Carries Solves Cloud Contact Center Migration Pain

Consumers are Churning Brand Loyalty at an Unprecedented Rate

Consumer expectations have grown tremendously since the start of the pandemic. Resolving customer service issues on first contact has become the number one driver of consumer loyalty, the report found. Indeed, poor customer service is a key reason why nearly half (49 percent) of the consumers surveyed in the report have stopped working with a company in the past year

Brand loyalty is already scarce among consumers, and COVID-19 has further interrupted established relationships. At the same, the pandemic created new opportunities for brands to gain loyal customers, with 57 percent of consumers saying their loyalty has grown with companies that helped their customers during these challenging times.

Omni-Channel is Now Mandatory

Additionally, the rise of buy-from-home economy has elevated the need for an omni-channel contact center, which allows customers to use the channel of their choice and then change when they want to, without losing context.

In the past year, there have had more digital interactions with contact centers via various channels than ever before. The most widely used channels:

• Live chat, 71 percent
• Virtual chat, 62 percent
• Asynchronous messaging, 61 percent
• Social media, 56 percent
• Video chat, 42 percent

There is no longer a de facto channel for customer interactions, as there was with voice for decades. Omni-channel is the only way forward.

More than half of those surveyed believe it’s important to transition easily between digital channels during a single interaction with a contact center. Companies that value customer loyalty are tasked with implementing technologies, which enable these seamless transitions between channels.

According to the report, 43 percent of companies are investing in contact center communication and collaboration tools over the next three years.

Also see: Digital Transformation Guide: Definition, Types & Strategy Younger Consumers Focus on Social Issues

While loyalty heavily depends on how quickly customer issues can be resolved, younger consumers are likely to choose brands based on their position on social issues, sustainability, and diversity.

The data shows 46 percent of Gen Z consumers stopped using a company’s services or products because of its views on social issues and 40 percent have stopped due to their stance on sustainability. Meanwhile, 53 percent of Gen Z consumers have started using a company’s services or products because of diversity in their customer service.

Connections, not Interactions Drive Customer Loyalty

The report also makes several predictions based on the survey findings. First, the report predicts, customer loyalty will increasingly be driven by a consumer’s overall connection to a brand, not specific interactions.

As noted earlier, younger consumers are more likely to choose companies that have a stance on certain issues. Most (92 percent) CX professionals agree that agents are brand ambassadors for their company, who will have carried out broader social conversations with consumers in the future.

The second prediction envisions contact centers gaining influence as they become profit centers that drive loyalty and deepen customer relationships. According to 91 percent of CX professionals, their contact center is what drives customer loyalty.

As revenue grows through customer engagement, the contact center will evolve beyond a cost center. Sixty-seven percent of companies report that their contact center is either in the process of transforming or has already transformed into a profit center.

Another prediction anticipates agents becoming more strategic in creating loyal customers. As more agents become brand ambassadors for their companies, they will play an important role in proactive customer engagement. In fact, 98 percent of companies plan to implement some type of proactive customer engagement in the contact center. Overall, 87 percent of CX professionals believe that proactive customer engagement is a driver of customer satisfaction.

Also see: Top Digital Transformation Companies

Employee Engagement and Retention

The last prediction centers on employee engagement and retention. As agents become strategic assets, companies will have to find ways to retain them by investing in workforce engagement management (WEM) tools. WEM includes hiring, onboarding, training, scheduling, motivating, and other activities related to increasing contact center agent engagement.

Most CX professionals want to invest in WEM tools in the contact center, with 78 percent saying it’s a priority for their company. Some of the key benefits companies expect to see by adopting WEM include:

• Greater efficiency or productivity in the contact center, 57 percent
• Higher customer satisfaction scores, 48 percent
• Increased profits, 46 percent
• Higher agent engagement levels, 43 percent
• Reduced costs, 37 percent
• Decreased agent attrition, 30 percent

In summary, creating loyalty inside and outside of the contact center has become a priority. Companies are recognizing the importance of not only connecting with consumers, but also engaging and retaining their contact center agents. This shift is what will drive companies to invest in engagement tools and new technologies going forward.

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

The post Contact Center Trends and Tech: Engagement is Omni-Channel appeared first on eWEEK.

I spoke with Vadim Vladimirskiy, CEO of Nerdio, about why the virtual desktop has migrated to the cloud more slowly than other applications; he also provided advice for typical challenges companies face with virtual desktops.

Among the topics we covered: 

• As you survey the virtual desktop market overall, what trends do you see? What customer needs are driving the market?
• What’s a common problem that companies face with their virtual desktop set up? Any advice that you would give?
• How is Nerdio addressing the virtual desktop needs of its clients? What’s the Nerdio advantage?
• The future of the virtual desktop? What are some key milestones we can expect in the years ahead?

Listen to the podcast:

Also available on Apple Podcasts

Watch the video:

The post Nerdio’s Vadim Vladimirskiy on Virtual Desktop Trends appeared first on eWEEK.

Looker by Google and Microsoft Power BI are both business intelligence (BI) and data analytics platforms with a strong following.

Clearly, the need for these data analytics programs is growing. No longer are they used only by an elite team of data scientists slicing and dicing data. In the era of digital transformation, mid-level management, marketing, sales, and other non-tech staff are all leaning heavily on data mining – daily.

As two well regarded data analytics platforms, users may need to choose between Looker and Power BI. There are arguments for and against each. Which business intelligence platform is best?

Also see: Best Data Analytics Tools  Looker vs. Power Bi: Key Features Comparison 

Microsoft encompasses a massive number of associated platforms and applications. Power BI, therefore, offers a diverse set of features that range far beyond BI and analytics.

Integration with the likes of Microsoft Office, Office 365, Teams, Azure, and SharePoint provide functions beyond the scope of Looker. Specific to analytics, Power BI encompasses data preparation, data discovery, dashboards, and data visualization.

Microsoft releases weekly updates to its cloud-based Power BI service. Recent feature additions include AI-infused experiences, smart narratives (NLG), and anomaly detection capabilities. A Power BI Premium version enables multi-geography capabilities and the ability deploy capacity to one of Microsoft 42 data centers around the world.

Google acquired Looker in 2019. This tool is web-based and offers plenty of analytics capabilities that businesses can use to explore, discover, visualize, and share analyses and insights.

Enterprises can use it for a wide variety of data mining techniques. It takes advantage of a specific modeling language to define data relationships while bypassing SQL. Looker leverages its Google ownership by being tightly integrated with a great many Google data sets, including Google Analytics.

Looker earns good marks for reporting granularity and scheduling but lacks the feature set of Power BI. Microsoft wins in terms of breadth of service due to its ecosystem of integrated platforms.

A dashboard in Looker.

Looker vs. Power Bi: Comparing Ease of Use

Newer users appear to find Power BI much easier to use than Looker, which is said to have a steep learning curve due to the need to use the LookML proprietary programming language. But those familiar with that language say that once learned, it is easy to use. They add that it streamlines the distribution of insights to staff across many business units. But for those less familiar, it can be difficult to set up and master.

The conclusion: Power BI wins on broad usage by a non-technical audience whereas Looker wins with technical users who know its language.

A dashboard in Power BI.

Looker vs. Power Bi: Analytics Comparison

Despite its proprietary modeling language, users say Looker isn’t that difficult to master and it is worth it in terms of being able to define data relationships and bypass SQL. Once the learning curve is mastered, users remark about how simple it is to use the platform for analytics.

Visualizations, though, are relatively basic according to some users and queries can be a little slow. But a drag and drop interface and pre-built templates and data models help analysts repeat similar analyses rapidly rather than starting again from scratch.

Power BI, however, is particularly good at crunching and analyzing real-time data. Overall, Power BI has the edge in analytics, although advanced users operating in the Google world and competent on LookML would probably give Looker their vote.

Also see: Top Business Intelligence Software 

Looker vs. Power Bi: Comparing Cloud, On-Premises

Both Looker and Power BI have pros and cons with regard to the strength of their platforms in terms of the cloud. Power BI has a fully functional SaaS version running in the Azure cloud as well as an on-premises version resident in Power BI Report Server. Power BI Desktop is also offered for free as a stand-alone personal analysis tool.

When power users are engaged in complex analysis of multiple on-premises data sources, however, they need to download Power BI Desktop. The on-premises Power BI offering isn’t as rich as the cloud version in terms of dashboards, streaming analytics, natural language, and alerting.

Looker is wholly cloud-based and requires no software of any kind. It does not offer any kind of on-premises option. Power BI wins for on-prem and it’s a tie for cloud functionality.

Also see: Top Cloud Companies

Looker vs. Power Bi: CRM Comparison

CRM and BI often go together. Power BI integrates relatively well with Microsoft Dynamics CRM. Looker doesn’t attempt to have any kind of CRM capabilities. Thus, Microsoft wins easily in this category.

Looker vs. Power Bi: Comparing Integration

Gartner notes that Power BI’s handling of content promotion and publication can generate administrative overhead. This isn’t an easy fix, which will add manual labor between Power BI apps and its collaborative development environment known as Workspaces. Yet Power BI offers a vast ecosystem of integrations and partners. And it is tightly embedded into a great many other Microsoft tools. It can also run Mac and Linux.

Looker is also able to run Windows, Mac, and Linux. It offers APIs and other means of exporting its data models to external visualization platforms. And of course, it is closely integrated with many Google data sources. But it can’t really compete with the breadth of integration options available to Power BI.

Also see: Top Data Mining Tools 

Looker vs. Power Bi: Price Comparison

Microsoft is very good at keeping prices low as a tactic to grow market share. It offers a lot of features at a relatively low price.

Power BI Pro, for example, costs approx. $10 per month per user. The premium version is $20 per month. The bottom line for any rival is that it is hard to compete with Microsoft Power BI on price when it comes to automated machine learning capabilities and AI-powered services.

Looker is more expensive. Its enterprise-class features related to the distribution of users and reporting mean that those needing these capabilities must be willing to pay a higher price. Power BI wins on price.

Also see: Top Digital Transformation Companies

Looker vs. Power Bi: Bottom Line

Microsoft is committed to investing heavily in Power BI and enhancing its integration across other Microsoft platforms. Any organization that is a heavy user of Office 365, Teams, Dynamics, and/or Azure will find it hard to resist the advantages of deploying Power BI. And those advantages are only going to increase. On the AI front, for example, the company boasts around 100,000 customers using Power BI’s AI services.

Microsoft is also putting effort into integrating with other applications and in making it easy to autotune query performance. Power BI is being integrated more and more closely with Microsoft Teams, which has tens of millions of daily active users.

For the Microsoft user base, it might be hard to convince them why they shouldn’t just tack Power BI onto their existing Microsoft services. Consider, too, the existing sales teams and distribution channels for Microsoft products. With such a huge user base and so many tentacles across the world, Power BI is only be a click away for many organizations. Those with an eye on budget that yet want a rich BI platform will certainly favor Power BI.

Power BI was graded as the top tool by Gartner in its latest “Magic Quadrant for Analytics and Business Intelligence Platforms.” Looker was not part of the analysis as it is more specialized.

Looker doesn’t really try to compete head-to-head with Microsoft. But there are certain enterprise user needs for reporting and analytics distribution where Looker excels. And for those heavily leaning on Google platforms, it offers advantages to skilled analysts. It is for the truly Google-centric user that Looker stands out.

Also see: What is Data Visualization

The post Looker vs. Power BI: 2022 Software Comparison appeared first on eWEEK.

Construction companies were on the frontline during the pandemic, with the urgent need for development of medical facilities and other essential infrastructure. But for an industry that relies heavily on manual labor and on-site workers, the sudden site closures, new safety measures, and need for more remote employees have presented significant challenges.

Research suggests that the global construction sector decreased by 3.1% in 2020, its worst decline since the 2008 global financial crisis, but the problems didn’t stop there. Today, projects have resumed, but many are being impacted by global supply chain disruptions, worker shortages, and skyrocketing material prices, particularly for lumber and steel. These issues are set to continue into the foreseeable future.

If there was ever a time for construction to embrace digital transformation, it would be now. As sites begin to reopen and projects ramp-up, emerging technologies will be key to the industry’s recovery. According to McKinsey research, the mandate for change and technological adoption in construction has never been stronger, with the pandemic “only serving to provide additional urgency to the pre-existing productivity and data-visibility issues facing construction companies.”

In particular, the digital innovations that have emerged from this crisis, such as the use of IoT sensors and robotic drones, will help companies improve health and safety measures, standardize modular construction, and create a more sustainable building environment.

Also see: Top Edge Companies  First, Use Digital On-Site Touch Points to Prioritize Workplace Safety

As construction companies continue to comply with pandemic restrictions, technology has been essential to the implementation of health and safety measures.

For instance, firms can use wearables and AI sensors to detect when workers are not maintaining proper physical distance. Some construction projects are even using contact tracing devices that alert employees when there are too many personnel at a worksite; these can identify potentially infected individuals in the event of a confirmed COVID-19 case. These measures not only prioritize employee safety, but also help companies avoid entire site shutdowns.

Even remotely, technology is a vital asset to construction firms. With fewer personnel allowed on-site, companies can rely on new cloud-based video platforms to assist with site monitoring. In the city of Miami, virtual inspections of construction sites through either a Zoom or a Microsoft Teams video call are now routine between engineers on site and building control officials.

With usage tripling in 2020 alone, drones are also being used more frequently to improve mapping and surveying processes. With their advanced capabilities, drones can create a digital record of the site to inform project timelines – allowing all stakeholders to monitor progress safely, while also enhancing the accuracy, speed, and ease of projects.

Also see: Data Analytics Trends 

Then, Build in Controlled Environments with Modular Construction 2.0

Modern methods of construction (MMCs) offer many benefits over traditional options, and are a big part of why the industry was able to adapt during the pandemic.

In particular, modular construction – the process of prefabricating modules and components or even entire homes in a manufacturing facility and then shipping to the site – allowed many construction companies to continue operations in a safe, secure, and controlled way.

While this construction method is not new, the development of digital planning and production technologies has seen adoption rates increase as more companies look to remain operational despite pandemic restrictions.

This ability to enable more standardization across the building process allows construction firms to actively address the serious productivity problems affecting the quality and delivery pace of construction projects. For instance, McKinsey predicts that offsite construction can increase the speed of construction by as much as 50% and reduce costs, if done in the right environment, by 20%.

Also see: Top Data Visualization Tools 

Now You’re Ready to Lay the Data Foundation for a Net-Zero Future

As of late, customers have become more sustainability-conscious than before and are placing greater pressure on companies to reduce the amount of carbon embedded in new construction.

However, it doesn’t stop there – consumers are also urging companies to support the growth of a deconstruction industry that reuses huge existing stockpiles of construction material. This task will not be easy as, according to the World Green Building Council, the building and construction sectors are together responsible for 39% of all carbon emissions in the world.

As recent McKinsey research suggests, the global conversation about climate change, exemplified by the implementation of UN sustainability targets, will compel construction companies and material suppliers to factor sustainability into their products, construction processes, and designs. But the fragmented and project-based nature of the construction sector creates additional challenges for the adoption of sustainable practices – and this is where technology can make a difference.

Smart buildings and infrastructure that integrate the Internet of Things (IoT) can increase data availability and enable more efficient operations as well as new business models, such as performance-based and collaborative contracting. Companies can use IoT sensors and communication technology to track and monitor energy efficiency, and maintenance needs.

With the use of BIM (Building Information Modeling), construction companies can create a virtual 3-D model with precise transparency on all components used in a completed building, which can increase efficiency. Technology is set up to be a key player in helping companies better manage building life cycles and significantly reduce carbon footprints.

Also see: Top Digital Transformation Companies

The Mandate for Technological Change 

Companies throughout the construction ecosystem must change their strategies, business models, and operating models to effectively manage the recent industry disruptions. The pandemic has provided an opportune catalyst for industry-wide changes, but it’s up to construction firms to adapt and transition away from traditional processes.

Only those construction businesses willing to use technology and transform how they work for the future, by fast-tracking digital transformation and optimizing digital skills to become more efficient, will be sure to stay ahead of the competition and outlast the pandemic disruptions.

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

About the Author: 

Rick Veague, Chief Technology Officer, North America, IFS

The post How Digital Transformation is Rebuilding the Construction Industry appeared first on eWEEK.

We’re experiencing an uncanny renaissance. Databases, long derided as antiquated, are suddenly the darling of the industry. Years after pundits declared databases all but dead, a new breed of startups has captured Wall Street’s attention. However, IT leaders still struggle to efficiently migrate their existing workloads to these systems.

The vendor lock-in on databases is legendary. No other sector wields this much influence over their users. Naturally, vendor lock-in keeps customers beholden to the existing technology. However, it also keeps the competition and upstarts at bay. Snowflake’s CEO famously lamented about how hard it is to break into this market when he said: “Teradata has [made] it bloody hard to move off their platform.”

With the advent of database virtualization (DBV) – not data virtualization – a new methodology has entered the arena. DBV claims to break vendor lock-in on data warehouse technology. How applicable is it and how can IT leaders evaluate the technology? Here are five things to know about DBV.

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

1) How Does DBV Work? 

A DBV platform sits between database and applications. It enables applications written for one database to run natively on a different one. All queries and communication are translated in real-time. For example, applications written for Teradata can run directly on Microsoft Azure Synapse and won’t even “know” they are no longer running on Teradata.

DBV is fully transparent with the aim that applications wouldn’t require any or only minimal adjustments. This includes not only standard SQL but also proprietary extensions. And to be successful in practice, loaders, drivers, and utilities must also be supported.

Since DBV systems implement the translation of queries and data, they can operate with rather low overhead. The actual data processing is always performed on the data warehouse itself and leverages the massively parallel processing (MPP) capabilities of that system.

2) When to Use DBV Over Conventional Migration?

In a conventional migration, all existing SQL code, drivers, tools and utilities are replaced with their counterparts of the new destination system. For compact data warehouse systems with a small number of applications, this may be the preferred approach. Data marts with only by a limited number of users may qualify for this.

However, in the case of complex Enterprise Data Warehouses (EDW), DBV can outperform conventional migration significantly. DBV accomplishes the migration of workloads at a fraction of the time, the cost, and the risk.

3) Can DBV Cover My Workload?

Some workloads use specialized features extensively. Yet others use features that pre-date standardization efforts. In other words, no two data warehouse workloads are the same. This can make it difficult to gauge the coverage of a DBV system.

A full documentation of supported features seems desirable but is actually not very helpful. Most customers cannot succinctly describe which features their workloads are currently using. To make matters more complicated, often the original authors of queries or functions are no longer with the company.

This need not be an obstacle to the adoption of DBV, however. Since DBV comes with a low risk of adoption, it makes for very effective proof-of-concept (POC) implementations. Customers do not need to alter their applications in order to use DBV. They can test their actual applications directly in a POC.

A POC then can identify any missing coverage quickly. It is important to understand, while a 100% coverage seems desirable, high 90’s is usually enough. Taking care of the remaining issues often requires only negligible effort.

Also see: Top Business Intelligence Software 

4) How Does DBV Differ from Data Virtualization?

Data virtualization is a somewhat related, yet quite different approach. For data virtualization to be successful, all applications need to be rewritten first and adopt an abstract SQL dialect. Then, and only then, does it prevent future vendor lock-in. The primary application areas are “green field” scenarios which do not need to take existing applications into account.

In contrast, DBV breaks existing vendor lock-in. It leaves applications as-is. Over time this may lead to a varied assortment of different application technologies. However, this may not be much of a concern and is well offset by the uptake of new data technology.

5) Can DBV Replatform EDWs to Any Technology?

Every few years, a new technology challenges the supremacy of enterprise data warehouse systems. Often the newcomer outperforms the existing stack in one notable dimension. For example, the new technology might be more scalable. Another may simplify the sharing of data. Yet other appeals more to open-source developers.

Typically, expert teams can build bespoke solutions to move workloads from the EDW to most new technology. However, the bigger the gap in functionality, the more software engineering is required to operate these systems. For example, moving an EDW to a NoSQL system maybe technically feasible, but is economically not always advisable.

For DBV to be successful, source and destination must be meaningfully similar. However, this does not require equivalent functionality. DBV can compensate for the lack of most advanced features like stored procedure, macros, or even unsupported data types. Currently, cloud-native PaaS solutions are most successful in the context of DBV.

Choosing the Right Approach for Migrations

When choosing a new destination system for an existing EDW, many factors need to be considered. One often overlooked is that of the migration approach. DBV is highly effective at breaking vendor lock-in on legacy data warehouses.

By factoring the migration approach into their choice of the destination system, IT leaders can optimize for fast uptake while controlling risk and cost.

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

About the Author: 

Mike Waas is the founder and CEO of Datometry

The post Understanding Database Virtualization: 5 Key Points appeared first on eWEEK.

Most telecom operators have been in business for many years and as a result have many disparate systems scattered across the organization. These include front end system that run the networks, as well as back end systems that take care of customer engagements and billing.

But as most telcos well understand, they can’t continue to run vintage systems that were built, in some cases, decades ago and that stand alone without the data integration necessary to fully understand the total business operations. They must, and in most cases are, move to a modern cloud-based environment. This enables a multi-faceted view of all the systems that interact with the customer, as well as internal systems required to fully optimize the network operations.

While the telco experience is not unique – many industries are making this transition – it is illustrative of what needs to be done when legacy systems are forced to update.

Also see: Top Digital Transformation Companies

Transformation is Not a Quick Fix: Focus on Most Critical Systems First

Digital transformation has taken hold, but it’s unlikely that all systems can be migrated quickly or easily. Indeed, we expect the journey for many to take at least 3-5 years, and for some systems even longer.

It is therefore imperative that the telcos find and update the most critical systems first and then move on to the other systems, in order to implement a single data processing and insights gathering capability for all corporate data. The journey from a legacy big data storage capability, which some have implemented, to a complete data management and processing platform is not always easy or done quickly. But nevertheless it is imperative if companies are to stay competitive.

An example of one company’s journey is that of MTN, based in South Africa. With over 300M subscribers and 50M financial transaction customers, MTN needed to define a path toward transformation. They wanted to provide their customers with more value for the money, as well as provide better privacy and governance of all data collected, particularly as regulations vary across the multiple countries in which they operate.

Because some countries have data residency requirements, MTN has enabled a hybrid cloud infrastructure, using Google Cloud Platform tools. To transform and modernize, MTN changed from running independent systems and data sets in each of the various geographies served, to a model where data itself is considered the enterprise asset of value. Doing so meant adopting an enterprise-wide data cloud mentality.

By creating a “workbench” that enabled them to build components to be deployed via a Kubernetes structure, and by utilizing a single data management plane through implementation of a leading data platform, they are able to process data acquired across all their systems at a rate as fast as 3M records per second.

Further, since they are on a single integrated data platform that is located in corporate headquarters, they are able to quickly implement country-specific requirements without having to “touch” multiple local systems. This enables a major cost savings and a much reduced time to implementation of any required changes.

MTN is now examining how to transform into a fully virtualized environment so it can run on a multi-cloud infrastructure, while still being controlled through a single interface.

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

Moving to the Next Generation of Networks

The entire telecommunications space is in the process of changing. Indeed, we estimate that only about 40%-50% of carrier infrastructure is fully virtualized, with most of that effort occurring in network operations to better enable vRAN (and eventually O-RAN) for 5G operations.

But the back office systems are often still in need of updating. Much of the customer data analysis that can better understand customers and provide input for marketing campaigns and improved customer interactions goes unused.

While a large amount of data from customer interactions will come from centralized data centers, the impending move to Edge processing means that data will need to be collected and integrated from a variety of Edge-deployed locations. The only efficient and effective way to do so is to create a data platform deployed across a multi-cloud environment that can ingest and process data from all sources.

An example of this is Amdocs and Nokia, running their enterprise data cloud on top of a data platform to perform analytics processing and gain insights to customer usage trends and operations optimization. This integrated approach leads to much more effective analytics and insights.

As operators move to more intelligent systems running AI/ML processes, it’s even more imperative that all data is available and manageable from a single access point. And as many carriers move to offer managed services to enterprise customers, it is critical that all data governance be optimized through a single platform while still meeting the diverse needs of individual customers.

Also see: Top Edge Companies 

Using an Optimized Enterprise Data Cloud

Carriers worldwide are struggling to fully cloud-enable back end systems to better understand customer behavior and offer new services like payments, entertainment, paid apps, security services, and edge computing. This requires a centrally managed data cloud environment that can not only provide the required analytics to mange operations, but also provide the needed oversight for regulatory compliance and privacy.

Because these requirements may change often, doing so from a central point of management is critical. Without a totally optimized enterprise data cloud, companies will not be able to remain competitive in a highly regulated but quickly changing market. And while the above example is focused on network operators, there are many industries that have similar requirements that should learn from this example and implement accordingly.

Also see: Best Data Analytics Tools 

The post Making Digital Transformation a Priority, Telcos Move to a Data Cloud appeared first on eWEEK.

In 2002, Donald Rumsfeld, then the United States Secretary of Defense, said “Reports that say that something hasn’t happened are always interesting to me, because as we know, there are known knowns; there are things we know we know.”

However, he added “there are also unknown unknowns – the ones we don’t know we don’t know. And if one looks throughout the history of our country and other free countries, it is the latter category that tends to be the difficult ones.”

The same lack of knowledge is true in the cybersecurity world. Let’s take a look.

Also see: Secure Access Service Edge: Big Benefits, Big Challenges

Cyber Threats: A Short History

The very first type of cyber threat was the notorious virus. It was an innovative computer program that could create copies of itself and “jump” from one computer to the next. Back then, attackers used floppy disks, but as time went on, they began to use computer networks and the Internet.

The first anti-virus software created databases of signatures; parts of the binary code that included lots of different such viruses and compared each file on the system against that database. Back then, that strategy was enough because developing applications was complex and, more important, time-consuming, specifically using languages such as C/C++/Assembly.

Since then, dynamic languages such as Python have appeared that facilitate the software development process by allowing users to more easily collaborate and re-use code parts. This, together with the rise of cryptocurrency, increased the attackers’ motivation and made the usage of such signature databases infeasible. The number of such known signatures and the rate at which this list is growing are simply staggering.

Many security solutions, even today, are still trying to use various types of signatures to detect malicious files, network traffic, human behaviors, and several others. Even tools that are trying to harness the immense power of large user communities to create vast databases of tens of thousands of different signatures are only effective to some extent. Overall, this strategy is not enough to provide good protection against the known threats.

And even if it was effective in protecting against known threats, it still won’t provide any protection against the known unknowns – and certainly not against the unknown unknowns. These attack types are being conceived and developed by attackers somewhere right now.

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

The Struggle to Block Unknown Threats

As mentioned, the signature-based defense tactic in which we create vast databases of IOCs (Indications of Compromise – e.g., IPs, domain names, users) is becoming less and less effective today with the rise of new computer languages.

Another trend that makes this approach less effective is the use of DGAs, Domain Generation Algorithms. Using this technique, the attacker embeds a piece of code inside the malware that uses variables known to the attacker and malware (for example, the current date), that will periodically and dynamically create a domain name and access it.

If it succeeds, it will be used, and if not, it will revert to the most recently used domain name that did work. Such techniques render signatures based on domain names and IPs essentially useless as these can easily be changed by the attacker at any time.

Another technique we see includes several types of network segmentation and segregation. This method spills the network into several sub-networks via various kinds of firewalls. Then, access to the Internet is not achieved directly but rather via a proxy or some sort of a terminal server, which receives keystrokes and mouse movements and returns images (i.e. screenshots).

This can effectively protect against certain types of threats but does not guard the organization against all types of attacks. Furthermore, such methods don’t provide the organization with any tools that help detect attacks or use evasion mechanisms against these devices or services. And they don’t improve the organization’s observability overall.

Another technique that is very effective but rarely used for practical reasons is whitelisting. Using this method, the organization will create and maintain databases of signatures of allowed executables, websites, certificate issuers, file hashes, etc. The downside is apparent: the amount of data the organization needs to collect and the manual maintenance required renders such a solution infeasible for most organizations.

Combining Technologies to Block Threats

As in many other cases in technology, when confronted with the need to choose between two options, the best path forward is – usually – to combine the two while getting the pros of both options and minimizing the cons.

The same is valid here. It’s recommended that you maintain, and continue improving, your current network segregation and firewalls. But add to it a passive network monitoring solution that will continuously monitor the traffic between computers on the organization’s local network and the traffic between the organization and the outside world. Also, usage of what is known as honey tokens can help tremendously in improving the detection capabilities of the passive monitoring solution.

Then, using the data collected from all these solutions, you can create alerts that fire whenever a new value appears in selected fields – in selected events – that would be a good indication of malicious activity.

These fields will include:

• Destination and source countries
• Processes launched by the root/Administrator user
• External DNS servers accessed by internal machines that are not the internal DNS server
• RDP/VNC users
• SSH server applications
• Application types
• Internally hosted services consumed by external hosts

We still may not be able to block all the “unknown unknowns” but we would at least be combing the best technologies we have right now.

Also see: The Successful CISO: How to Build Stakeholder Trust

About the Author:

Yuval Khalifa, Security Solutions Architect, Coralogix

The post Detecting the Unknown Unknowns in Cybersecurity appeared first on eWEEK.

Google Data Studio and Tableau are both packed with data analytics features and business intelligence capabilities that help organizations gain insights – and innovate. Clearly, as businesses focus on digital transformation, this ability to mine data for knowledge and guidance is critical.

Both Google Data Studio and Tableau offer attractive dashboards with rich graphics, and they’re both adept at ingesting, manipulating and processing data. GDS and Tableau are two of the key tools used as data analytics trends reshape business.

However, there are also key differences in these data analytics applications. Sorting through the various options – along with strengths and weaknesses – is critical. Depending on your organization’s IT framework, your budget and how it approaches BI and analytics, either Google Data Studio and Tableau may stand out.

Also see: Top Business Intelligence Software 

Google Data Studio vs. Tableau: Feature Comparison

It’s no surprise that Google Data Studio works best with Google software, tools and systems, including BigQuery, Google Analytics and Firebase. Native support for many of its own data formats makes GDS a natural choice for organizations that rely on Google Cloud and various other Google apps. Integrations are simple and work well.

However, using the platform with other software and tools can present challenges – including the need to rely on third party connectors and integration tools, sometimes available at an additional cost. For instance, the platform does not natively support Microsoft Excel files. Importing them requires additional steps and manipulation, including converting them to a Google Sheet document or a CSV file.

Google Data Cloud also doesn’t always play nicely with other vendor’s cloud data. Importing this data into GDS may also require additional steps or software. On the upside, the platform integrates with upwards of 150 cloud SQL, e-commerce and digital advertising platforms.

It’s no accident that Tableau has emerged as a leading data analytics solution. The application connects to a vast array of data sources, including Microsoft Excel, SQL Server, Google BigQuery, Snowflake, SAP HANA, Salesforce, Splunk and Amazon Redshift.

Furthermore, Tableau is equally adept at grabbing data from all the major players in the cloud-based data storage space, including Google Drive, Microsoft OneDrive, Dropbox and Box. Another advantage of Tableau is that it’s part of Salesforce. As a result, the BI software connects and integrates with Salesforce at a deep level. One of the strengths of Tableau is that it also seamlessly integrates with Slack and has a variety of other connectors designed for data science, business applications and other tasks.

Tableau is equipped to ingest large volumes of data and delivers models on Windows, Macs, iOS and Android devices. Its versatility – including integrations with various dashboards and web portals – is a big plus.

Also see: Top Business Intelligence Software 

A dashboard in Google Data Studio. 

Google Data Studio vs. Tableau: Comparing Data Visualization

Google Data Studio’s TBI and data visualization solution is adept at delivering attractive charts, graphics, geo maps, heat maps, bullet charts, pie charts, pivot tables, scorecards and numerous other representations. It offers dashboards that tie together various data sources and data streams, typically from Google applications or online advertising data sources.

This makes it ideal for viewing business and marketing metrics that are more digitally focused, including things like ad spend, site traffic, and search rankings. GDS allows users to customize reports and dashboards, including adding logos, icons and other properties.

GDS also delivers built-in comparison features that make it easy to view changes over time. However, the solution requires an Internet connection to operate effectively, and it only supports about 50 major functions – much less than Tableau.

Tableau’s data visualization platform has emerged as a market leader for a very clear reason: it transforms data into attractive and useful charts, graphics, infographics, heat maps, cluster maps and much more. The platform – which is designed for both data scientists and casual business users – makes it possible to create, modify and adapt visuals to match nearly any need or situation.

Tableau includes powerful interactive visual exploration and analytics tools and dashboards. However, the powerful visualization features that Tableau offers also come at a price: the platform is complex. It has a much steeper learning curve than GDS. It’s best suited for medium to large enterprises that have more sophisticated requirements and staff dedicated to generating visuals.

Also see: Top Data Mining Tools 

A dashboard in Tableau. 

Google Data Studio vs. Tableau: Functionality Comparison

An advantage of Google Data Studio is that it delivers a simple and clean interface that’s relatively easy to use. The disadvantage is that the platform doesn’t accommodate the highly complex functionality, interactivity and reporting that Tableau supports.

Nevertheless, GDS is a flexible and powerful BI tool. The cloud-based solution is fast and efficient, and it includes excellent automation features, including scheduled updates and reporting. It also allows limited customizations for those who don’t mind working with code. Finally, because GDS resides in the cloud, Google constantly updates the solution and adds new features.

The Tableau platform offers five versions: Tableau Desktop, which is used to create and edit reports; Tableau Public, which allows users to share visualizations; Tableau Server, an external sharing tool; Tableau Online, a cloud version of the software; and Tableau Reader, which allows anyone to view a visualization or report.

Tableau delivers fast data processing, supports large numbers of concurrent users, and consumes minimal system resources. It also offers an array of advanced features and functionalities. This includes data cleansing capabilities, which are absent from GDS, and native connectivity with a wide range of databases, cloud resources, CRM systems such as Salesforce, and file types, including Excel.

This makes it easier to ingest data and export files and data. Because the solution works without an Internet connection, it’s ideal for those traveling or working offline at times. Tableau also delivers stronger and better collaboration options than Google Data Studio.

Also see: Top Data Visualization Tools 

Google Data Studio vs. Tableau: Comparing Security

Security controls within Google Data Studio include password-enabled sharing options for visualizations and reports. GDS also supports Secure APIs for data connections. However, because the solution operates in a public cloud, anyone who has access to the space can view and download data. Likewise, the public cloud framework limits privacy options and controls.

The Tableau BI platform offers broader and deeper security controls than GDS. This includes multifactor authentication and granular Row Level Security. However, the way that an organization uses Tableau matters. For instance, those sharing workbooks and visualizations in Tableau public cloud have fewer controls. Essentially, anyone in the cloud can view and download reports and data. The platform also offers stronger privacy controls and options.

Also see: Top Digital Transformation Companies

Google Data Studio vs. Tableau: Usability and Support Comparison

Installing Google Data Studio is easy and painless. It’s a simple download followed by a login to a Google account. This makes it ideal for SMBs and others looking for fast, efficient and robust BI visualizations without a lot of overhead. Using the BI tools is relatively easy, though the platform has some size and flexibility limitations. Those that use other Google products, such as Google Docs, will find the controls and settings familiar. The platform has a large online community and offers a robust knowledge base with videos and other tools. However, there is no phone or live support for GDS.

While the Tableau BI solution delivers unsurpassed capabilities, this comes at a cost. The learning curve for Tableau is much steeper than GDS. In order to use more advanced functionality, there’s typically a need for training. Fortunately, Tableau has an enormous online community, and it offers substantial online resources, including a knowledge base, to aid in using its products.

The vendor’s support options include standard support (available between 8 a.m. and 5 p.m.), which is included in a subscription purchase, extended support that includes 24×7 live support (including weekends), and premium support, which offers extended availability and prioritized service from a senior support team.

Also see: Top Cloud Companies

Google Data Studio vs. Tableau: Price Comparison

A big draw for Google Data Studio is its price: free. It simply requires a Google account.

Depending on your BI requirements, Tableau can range from affordable to expensive.

Tableau’s tiered pricing model includes a free version, Tableau Public, which offers limited storage and privacy options. Fully featured Tableau Creator, which can run in the cloud or on-premises, costs $70 per month (billed annually). It includes the desktop software and Tableau Prep Builder, which is used to create visualization.

Tableau Explorer, which includes a single Tableau Server license, costs $35 per month (billed annually) is on-premises and costs $42 per month (billed annually). Tableau Viewer, which allows users to view visualizations, costs $12 per license, billed annually.

The bottom line is that the Tableau solution is pricey. The typical cost of a license is between approximately $800 and $2,000 per year.

Also see: Top AI Software

Google Data Studio vs. Tableau: Overall Comparison

Both Google Data Studio and Tableau deliver robust visualizations through dashboards and other tools. If you’re on a tight budget and have limited BI requirements, Google Data Studio is most likely the tools for you. It offers the additional advantage of delivering a user-friendly and easy-to-use interface.

If on the other hand, your organization has more complex requirements and a need to manage broader and more varied data sets, Tableau is more likely to fit the bill. It can tackle nearly any type of BI and data analytics requirement, it includes strong built-in security features, and it makes sharing attractive visualizations easy. 

Also see: Data Analytics Trends 

The post Google Data Studio vs. Tableau: 2022 Software Comparison appeared first on eWEEK.

On Tuesday, March 15, at 11 AM PT, @eWEEKNews will host its monthly #eWEEKChat. The topic will be Low Code / No Code Trends, and it will be moderated by James Maguire, eWEEK’s Editor-in-Chief.

We’ll discuss – using Twitter – the challenges, potential and best practices for low code / no code software development. How does low code help companies scale out faster? How does low code / no code empower non-tech staffers?

How to Participate: On Twitter, use the hashtag #eWEEKChat to follow/participate in the discussion. But it’s easier and more efficient to use the real-time chat room link at CrowdChat.

Instructions are on the Low Code / No Code Crowdchat page: Log in at the top right, use your Twitter handle to register. The chat begins promptly at 11 AM PT. The page will come alive at that time with the real-time discussion. You can join in or simply watch the discussion as it flows.

Special Guests: Low Code / No Code 

The list of experts for this month’s Tweetchat currently includes the following – please check back for additional expert guests:

• Loren Goodman, CTO, InRule
• Prince Kohli, CTO, Automation Anywhere
• Vindod Iyengar, Head of Products, H20.ai
• Tony Newcome, CTO, Active Campaign
• James Clark, VP of Platform, Brightspot 
• Llanor Alleyne, Managing Editor, IT Business Edge 
• Chris Ehrlich, Managing Editor, Datamation
• James Maguire, Editor-in-Chief, eWeek [moderator]

Chat room real-time link: Go to the Crowdchat page. Sign in with your Twitter handle and use #eweekchat for the identifier.

Questions for the Tweetchat

The questions we’ll tweet about will include – check back for more/ revised questions:

1. Why do companies choose low code no code platforms? What’s the advantage?
2. Where is low code / no code in its growth curve? How far are we from peak low code / no code?
3. What’s companies’ comfort level with their low code / no code deployment? Mostly okay, or confused and challenged?
4. What’s the biggest pain point that companies have with low code / no code?
5. What advice would you give them to optimize their low code / no code deployment?
6. What’s a big myth associated with low code / no code?
7. What vendors are the biggest winners in low code / no code?
8. The future of low code? Where will we be in 3-5 years?
9. What else is important about low code development – what else should companies be aware of?

Go here for CrowdChat information.

#eWEEKchat Tentative Schedule for 2022*

Jan. 18: Trends in Digital Transformation
Feb. 15: Navigating Multicloud Computing
March 15: Low Code / No Code Trends
April 12: Edge Computing: Monitoring, Observability and More
May 17: Data Analytics: Optimizing Your Practice
June 14: Expanding Your AI Deployment

*all topics subjects to change

The post eWEEK TweetChat, March 15: Low Code / No Code Trends appeared first on eWEEK.

It’s a well-known fact that Internet connectivity in high density environments like stadiums can be erratic and slow. Anyone who has been in airport, conference center, hotel, stadium, or other high-volume facility has experienced the pain of inconsistent Wi-Fi –  the network works well one minute only to lag or stop the next.

The National Football League (NFL) has addressed this unique challenge by building out its infrastructure with Extreme Networks to provide fans with a better Super Bowl experience. Although Extreme does not provide Wi-Fi to every stadium, every venue uses its Wi-Fi data analytics product, which provides key insights into performance, fan usage, utilization and more.

Also see: Best Data Analytics Tools 

NFL IT Executives Share Super Bowl Insights

Extreme Networks recently hosted a webinar with the NFL’s IT executives: Michelle McKenna, Chief Information Officer; and Aaron Amendolia, Vice President of Information Technology Services.

The two discussed their digital transformation journey and shared key networking tech takeaways from Super Bowl LVI, which took place on February 13 at a brand-new sports and entertainment complex, SoFi Stadium in Inglewood, California.

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

Record Wi-Fi traffic at Super Bowl LVI

Wi-Fi engagement was significantly up from previous years. According to data shared by the NFL, during Super Bowl LVI there was:

• 2 terabytes (TB) of data transferred via Wi-Fi.
• 57,618 users connected to Wi-Fi.
• 541 megabytes (MB) average usage per connected device.
• 82 percent of fans connected to Wi-Fi in attendance.

“The thing that strikes me most is the large percentage of fans who were connected at the stadium. I can remember a time when it was in the low 20s and we thought that we were doing a good job,” said McKenna.

This is the ninth consecutive year that the NFL has tapped Extreme for Wi-Fi analytics, and I’ve been watching the year over year trends. I concur with McKenna’s comments: even a couple of years ago, the Wi-Fi attach rate was only about half the fans, with the rest using cellular service. What this data point shows is that if high quality Wi-Fi is available, people will use it as a first choice.

Also, the total volume of traffic – just over 31TB – surprised me. Super Bowls have historically been in the teens or low 20s. Typically a number that large comes with something like a BTS or Taylor Swift concert.

The increased number of fans obviously drove more volume over Wi-Fi, and this is an indicator of things to come. Newer phones have higher resolution cameras, which are larger in file size. Businesses should take this as a warning and be prepared to see a steady increase in traffic on their networks – and use an analytic tool to predict trends that can keep experience high.

Also see: Top Digital Transformation Companies

Key to Wi-Fi Success: Preparing for the Unknown 

As the amount of total data keeps growing each year, the stadium’s Wi-Fi infrastructure must support fans when capacity is at its peak. While the NFL can’t predict what’s going to happen, it must be prepared for any scenario.

The NFL has addressed this challenge by planning for the outliers or specific data points that fall outside the range of probability. Planning for the outliers ensures that Wi-Fi users don’t experience major interruptions.

For example, an outlier from Super Bowl LVI was an app that enabled fans to download and view the halftime show from multiple angles. Although the NFL hadn’t tested for that prior to the event, it was able to accommodate the changes in the halftime show by planning for the unknown. This would have likely “stopped us dead in our tracks” a few years ago, according to McKenna.

During the 2021 NFL season, there were 6.21 TB of data transferred and the average one-minute throughput was 6.03 gigabits per second (Gbps). When comparing the numbers to 2014, there were 1.9 TB of data transferred and the throughput was 0.65 Gbps. Wi-Fi usage has spiked during that time as well. In 2013, 18 percent of fans were connecting to a Wi-Fi network, compared to 47 percent in 2021.

The NFL can predict massive spikes in Wi-Fi through the Extreme Networks’ analytics solution, which tracks data across all games, all season long. Extreme Analytics tracks app utilization and how trends change over time. App usage can flare up in different areas of the country at certain times, so it’s important for the NFL to watch such trends across its fan base.

Testing: Mandatory but Must Be Modernized

Pre-testing before the Super Bowl is currently a tedious process that involves staff continuously walking up and down the stairs on a steep incline to evaluate Wi-Fi strength. However, the NFL is looking to modernize this impractical process by investing in devices, which can be deployed in the stadium to perform this type of testing throughout the year.

This past season wasn’t typical for the NFL. Fewer fans attended the Super Bowl due to COVID-19 restrictions. The NFL had to do things differently to limit in-person interaction at the stadium, like moving to 100 percent mobile ticketing and more engagement on mobile devices. The NFL had to make broad changes to provide ubiquitous and reliable connectivity.

“In order to contact trace, everyone had to be connected to Wi-Fi to know who they were in contact with. Connectivity and data are key to being able to do these things,” said McKenna. “It’s not a nice to have, but it’s like electricity, water, and plumbing. You have to have it and it has to perform at its highest level.”

Luckily, today’s devices and wireless carriers are more sophisticated at leveraging the infrastructure, said Amendolia. There’s an automatic handoff between Extreme Networks’ Wi-Fi technology and Verizon systems, which are pervasive across the NFL and other sports leagues. Auto offloading provides a comprehensive approach to driving the fan experience, regardless of device or carrier.

Artificial Intelligence Provides Better Experiences

“Fans come to the stadium for the experience and Wi-Fi is really the unifying force, a neutral host,” Amendolia said. “We’re trying to get away from a world where fans must manage connectivity themselves. It’s supposed to be transparent to the fans by walking into the stadium and the device picking the best connection for them.”

Ultimately, fans are the content creators who are in control of sharing their game day experience. McKenna believes the NFL must be prepared for any use cases that fans bring with them. For instance, she sees the concept of a metaverse taking off, where real and virtual worlds converge using artificial intelligence (AI).

AI-powered machines get smarter by collecting enough data to be able to mimic the human brain. The NFL ingests massive amounts of data and video, which is analyzed by sophisticated modeling. This data could be key to creating what McKenna referred to it as “extreme personalization” for fans.

As devices get smarter, turning Wi-Fi on and off will go away. People will be connected to their metaverse wherever they go. Much like we’ve come to rely on always having good cell service, it’s going to be the same for any form of connectivity.

That’s the NFL’s vision for the future and should be the vision for almost all businesses. Customer and employee experience are critical to business success and, given the network-centric world we live in, a consistent, high performance Wi-Fi network is no longer a “nice to have,” it’s a “must have.”

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

The post What Super Bowl LVI Revealed about Wi-Fi Technology appeared first on eWEEK.

IBM Cognos Analytics and Microsoft Power BI are two of the top business intelligence (BI) and data analytics software. Both of these applications are in heavy demand, as organizations seek to harness the vast repositories of data – with ever more created hourly.

Whether from unstructured data, social media, relational databases, or overworked enterprise applications, there is a vast amount of data subjected to data analytics. Instead of a small team of business intelligence pros mining data, now there are teams from marketing, sales, and IT all utilizing analytics in search of the benefits of digital transformation.

As two highly regarded analytics platforms, users often are forced to choose between Power BI and Cognos. There are arguments for and against each data analytics platform. It is hard to say that you could go wrong with either BI tool. But which is best?

Also see: Best Data Analytics Tools 

Cognos vs. Power BI: Key Features Comparison 

Microsoft encompasses a massive number of associated platforms and applications. Power BI, therefore, offers a diverse set of features that range far beyond BI and analytics. Integration with the likes of Microsoft Office, Office 365, Teams, Azure, and SharePoint is a major selling point. Specific to analytics, Power BI encompasses data preparation, data discovery, dashboards, and data visualization.

Cognos Analytics supports data analytics from discovery to operations and is available in cloud and enterprise editions. It can be hosted onsite or in the cloud. It enables users to connect, verify, and combine data, and offers plenty of dashboard and visualization options. Cognos is particularly good at pulling and analyzing corporate data, providing detailed reports, and assisting in corporate governance. It is built on a strong data science foundation and is supported by heavy duty analytics courtesy of IBM Watson.

Also see: Top Business Intelligence Software 

A dashboard in IBM Cognos.

Latest Features and Updates

Microsoft releases weekly updates to its cloud-based Power BI service. Recent feature additions include AI-infused experiences, smart narratives (NLG), and anomaly detection capabilities. A Power BI Premium version enables multi-geography capabilities and the ability to deploy capacity to one of 42 data centers around the world.

Cognos, of late, has been upgrading its home screen to simplify the experience and give it a more modern look and feel. Onboarding for new users has been streamlined with new video tutorials and accelerator content organized in an easily consumable format. Improved search capabilities, too, and enhancements to Cognos AI Assistant help generate dashboards automatically, recommend the best visualization, and suggest questions to ask (via Natural Language Query) to dive deeper into data exploration.

Who wins on features? It depends on user needs. Microsoft is stronger on general cloud and mobility features while IBM wins on reporting, governance, and security.

Also see: Top Data Mining Tools 

A dashboard in Microsoft Power BI.

Cognos vs. Power BI: Comparing Ease of Use

Newer users appear to find Power BI a little easier to use than Cognos. But the AI-powered and Watson-backed analytics of Cognos lower the barrier to the use of advanced data science techniques.

The conclusion: Power BI wins on broad usage by a non-technical audience whereas IBM has the edge with technical users. That said, both platforms cater to beginning and advanced users. Overall, Microsoft wins in this category due to generally more favorable user reviews and commentary about ease of use.

Also see: Top AI Software 

Cognos vs. Power BI: Analytics Capabilities Comparison

IBM Cognos has tools to bring together a multitude of data sources as well as an AI Assistant tool that can communicate in plain language to get fast recommendations that are easy to understand. It also generates an extensive collection of visualizations.

This includes geospatial mapping and dashboards that enables the user to drill down, rise up, or move horizontally through visuals which are updated in real time.

Power BI, too, is good at crunching and analyzing real-time data, but Cognos may have the edge in this area.

Also see: Data Analytics Trends 

Cognos vs. Power BI: Comparing Cloud and On-Premises

Both products have pros and cons with regard to the strength of their platforms. Power BI has a fully functional SaaS version running in the Azure cloud as well as an on-premises version resident in Power BI Report Server.

Power BI Desktop is also offered for free as a stand-alone personal analysis tool. Yet when power users are engaged in complex analysis of multiple on-premises data sources, they need to download Power BI Desktop. The on-premises Power BI offering isn’t as rich as the cloud version with regard to dashboards, streaming analytics, natural language, and alerting.

Cognos offers cloud and on-prem versions. But its DNA is rooted in on-prem so it lags behind Microsoft on cloud-based bells and whistles. Therefore, Microsoft gets the nod for cloud analytics, and Cognos for on-prem. But both are good at each.

Also see: Top Data Visualization Tools 

Cognos vs. Power BI: CRM Comparison 

CRM and BI often go together. Power BI integrates relatively well with Microsoft Dynamics CRM. IBM Cognos doesn’t venture much into the CRM territory, although IBM is more than happy to integrate Cognos with other CRM platforms.

Microsoft Power BI, then, wins when a business wants a unified BI/CRM package.

Also see: Top Digital Transformation Companies

Cognos vs. Power BI: Comparing Integration

Microsoft has an extensive array of integration options, APIs, and partnerships. Power BI is tightly embedded into much of the Microsoft and Windows ecosystems. The company, though, is addressing some integration challenges.

Gartner noted that Power BI’s handling of content promotion and publication can generate administrative overhead. This isn’t an easy fix, which will add manual labor between Power BI apps and its collaborative development environment known as Workspaces.

IBM Cognos connects to a large number of data sources, including spreadsheets. It is quite well integrated into some parts of the vast IBM portfolio. Microsoft is stronger on mobile integration and wins overall in this category.

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

Cognos vs. Power BI: Price Comparison 

Microsoft is very good at keeping prices low as a tactic in growing market share. It offers a lot of features at a relatively low price. Power BI Pro, for example, costs approximately $10 per month per user. The premium version is $20 per month.

The bottom line for any rival is that it is hard to compete with Microsoft Power BI on price when it comes to automated ML capabilities and AI-powered services.

IBM Cognos Analytics, on the other hand, has a reputation for being expensive. It is hard for IBM to compete with Power BI on price alone.

Also see: Data Mining Techniques 

Cognos vs. Power BI: Bottom Line

Microsoft is committed to investing heavily in Power BI and enhancing its integration across other Microsoft platforms. Any organization that is a heavy user of Office 365, Teams, Dynamics, and/or Azure will find it hard to resist the advantages of deploying Power BI.

And those advantages are only going to increase. On the AI front, for example, the company boasts around 100,000 customers using Power BI’s AI services. The company is also putting effort into integrating with other applications and in making it easy to autotune query performance. Those with an eye on budget, yet still want a rich BI platform, will probably favor Power BI.

Consider, too, the existing sales teams and distribution channels for Microsoft products. With such a huge user base and so many tentacles across the world, Power BI may only be a click away for many organizations.

But IBM isn’t called Big Blue for nothing. It boasts a massive sales and services team, and global reach into large enterprise markets. Cognos, then, is likely to do well against Power BI within the existing IBM customer base. But for non-IBM shops, it struggles to compete with Power BI.

Where Cognos Analytics can win, though, is at the high end. Microsoft offers most of the features that small, mid-sized and even many large enterprises would ever need on analytics. But at the very high end of the analytics market, and in corporate environments with hefty governance and reporting requirements, Cognos can certainly carve out a niche.

Such factors certainly played a role in Gartner’s latest “Magic Quadrant for Analytics and Business Intelligence Platforms.” Microsoft was graded above all other similar applications as a Leader whereas IBM merited only a Niche Player rating.

Also see: What is Data Visualization

The post Cognos vs. Power BI: 2022 Software Comparison appeared first on eWEEK.

Datadog and Sumo Logic are both well-respected vendors that offer application performance monitoring (APM) features – which are highly important in the cloud era. Datadog is better established and is more of a broad APM platform. It is graded as a Leader in the latest Gartner APM Magic Quadrant (MQ). Sumo Logic isn’t part of the Gartner MQ for APM, as it is more of a log management tool that includes APM features.

While there are some similarities in terms of features and functionalities, there are plenty of differences. So how do users go about determining which one is best for their specific environment?

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

Also see: Data Analytics Trends 

Datadog vs. Sumo Logic: Key Feature Comparison

Datadog is focused on cloud monitoring and security. It offers the ability to see inside any stack or application at any scale and in any infrastructure. Infrastructure monitoring, APM, log management, device monitoring, cloud workload monitoring, server monitoring, and database monitoring fall within its feature set.

It is particularly astute at dealing with the performance and visibility of multiple clouds operating on the network and in managing cloud services. Datadog helps IT to drill down into performance data. It generates alerts about potential problems and helps IT to discover any underlying issues.

Datadog can assemble data from logs and other metrics to provide context that is helpful in minimizing incident response time. The 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.

Sumo Logic is characterized as a cloud log management tool for application logs and IT log data. It uses cloud-based machine data analytics to identify availability and performance issues lurking within the infrastructure. This can also help to improve the security posture and streamline application rollouts.

For troubleshooting, Sumo Logic is said to lower mean-time-to-resolution by up to 50%. Sumo Logic outdoes Datadog in terms of some capabilities, such as having a real-time continuous query engine, and constantly updated dashboards and reports for immediate visualization. Its anomaly detection engine, too, enables organizations to uncover events without having to write rules.

Overall, both platforms can perform basic APM functions, but Datadog is a more comprehensive platform when it comes strictly to traditional application monitoring. Both are Software-as-a-Service (SaaS)-based applications.

Datadog has broader applicability in terms of APM capabilities as well as monitoring other areas, such as security, networking, and infrastructure. Yet Sumo Logic reigns supreme on log management as well as being able to monitor containerized environments, which are becoming more common. Sumo Logic also offers real-time monitoring, which is not available in Datadog. In terms of raw APM features, though, Datadog comes out ahead.

Datadog vs. Sumo Logic: Comparing Implementation and Ease of Use

Datadog installation is straightforward via the deployment of agents, but 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 a lot of capability. It’s great for experienced users, but it might be tough for new users who may be overwhelmed by the number of options.

Some users find Sumo Logic relatively easy to use; other don’t. the company offers a comprehensive API to address a wide range of log sources, data sources, and search parameters. But detailed configuration work may be required to get the most out of it.

Overall, Datadog gets the nod on implementation and ease of use.

Also see: Best Data Analytics Tools 

Datadog vs. Sumo Logic: Support and Integration Comparison

Datadog can work with a reasonable set of data sources and formats, but it is not a platform that is set up deal with a large number of information sources. For instance, data formats like.xml, .csv, and .json are not supported. That said, it integrates well with other security and IT management tools. Datadog supports community APIs and extensions to integrate it into existing IT infrastructure.

Sumo Logic can collect data from on-premise sources as well as SaaS and PaaS environments, and private, public, and hybrid clouds. The company has support personnel available to help customers with implementation and integration, and users generally report a good experience with Sumo Logic support. The company also offers free training, which helps with some of the trickier implementation and integration issues.

There is little to choose between them on support and integration. But where Sumo Logic pulls ahead is support for Kubernetes and containers in general. Datadog is said to be slower in tracing customer issues in containerized environments. 

Datadog vs. Sumo Logic: Security Comparison

There was a time – several years back – when you could provide application performance management tools and software without worrying too much about security. How times have changed. 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.

As a SaaS application, Datadog has had to intensify its focus on security. It has been steadily adding security features in recent years. It has gotten certified (SOC 2 Type 2, CSA STAR, HIPAA, FedRAMP-Moderate Authorized). But it has further to go. Its basic query language supports relatively simple logic/count functions in its detection rules. Additionally, it is not yet PCI compliant.

Sumo Logic, on the other hand, can detect threats and respond to security incidents courtesy of its quite capable security suite. On top of the certifications held by Datadog, Sumo Logic boasts PCI DSS 3.2, and Privacy Shield. Sumo Logic wins on security.

Also see: Top Data Mining Tools 

Datadog vs. Sumo Logic: Comparing Telemetry and Troubleshooting

Datadog supports a variety of open standards, including OpenTelemetry and OpenTracing. But Sumo Logic offers three types of telemetry — logs, metrics, and traces. This helps it to provide strong observability and security monitoring.

Sumo Logic is also stronger than Datadog on search. The Sumo Logic query language allows searches across structured and unstructured data from metrics and traces to logs, without the need to sample data for full fidelity. Datadog has search, too, and can correlate sampled sets of data together using breadcrumbs. But its search capabilities aren’t as extensive.

Datadog vs. Sumo Logic: Price Comparison

Datadog prices out at around $15 per user, roughly (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.

Sumo Logic has a free version, but its Professional version is far pricier than Datadog at $90 per month. Datadog wins on pricing.

Also see: Top Data Visualization Tools 

Datadog vs. Sumo Logic: Bottom Line

There is no doubt that Datadog and Sumo Logic are both excellent tools. They can solve a great many challenges related to application performance monitoring, log management, and more.

Datadog is probably the platform to choose based solely on the need for mainstream APM functions. It 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.

Sumo Logic enters the APM sector, but its strengths lie in search, real-time functionality, Kubernetes, and log management. For those specifically in need of log monitoring and the monitoring of Kubernetes-based applications, Sumo Logic comes out ahead. 

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

I spoke with Bernard Golden, Executive Technical Advisor at VMware, about why forward-looking cloud deployments require a cloud native approach; he also discusses the spectacular growth of the hyperscalers.

Among the topics we covered:

• You’ve written an interesting article for eWeek, Why Cloud Means Cloud-Native. I want to talk about why that’s true, but first, what’s your sense of how are companies approaching cloud native? Reluctantly? Eager to get on board?
• So why does cloud mean cloud-native?
• A few cloud native best practices?
• You wrote an article for eWeek about cloud growth. Clearly the hyperscalers are growing leaps and bounds. What does it mean for customers?

Listen to the podcast:

Also available on Apple Podcasts

Watch the video:

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Automation can provide us with new ways to perform software root cause analysis, it can help tune systems for improved performance, and it can lower Total Cost of Ownership (TCO), among many other uses. Indeed, the array of RPA-based efficiencies goes way beyond mere data entry that was so often found before the ‘intelligent Cloud services’ era.

New advancements in increasingly cloud-native platforms, data architectures and the still-nascent world of Artificial Intelligence (AI) and Machine Learning (ML) are changing the way we all work – they all drive automation. With so much intelligent automation now being brought to bear upon business workflows systems, we can now remove the human element from time-consuming and often repetitive tasks.

Modern tools and systems can improve the efficiency and accuracy of the data-driven business decisions while simultaneously freeing up human manpower to improve and grow other areas of the business.

The global economic shake-up that has occurred in the last 18 months gives a valuable opportunity to step back and see where we have advanced. In an analysis of the recent technological advancements in business, intelligent acceleration and automation tools shine in how they have shaped new ways of working.

Also see: What Does 2022 Hold for Intelligent Automation

Customized Applications and AI

Any tool is as useful as you make it, rather than being innately, equally beneficial to all in every situation. Digital automation is no exception – it is the customized applications of the tool that make the difference. Companies utilize the same tool for a wide range of data and situations, and all derive different results.

On the leading edge of automation, we concentrate on making every tool agile, scalable, and usable. Regardless of skill level, we aim to ensure that the tool is ‘fit for purpose’ for every user. This is where AI comes in.

In practical, real-world terms, an automation efficiency in an ERP system can drive some fairly complex orchestration. Traditionally, every iota of that functionality will have been built by a developer, naturally limiting it somewhat. Today’s technology has fewer limitations, thanks to AI.

Developers are still very much in the driver’s seat, but now AI provides intuitive acceleration created from patterns observed over time, broadening the functionality.

Also see: Top AI Software 

Autonomic Functioning

In the digital transition currently in motion, there are endless applications for automation.

If a business has a heavy transaction workload, it may be prudent to reduce the size of the system being engineered. Or, to best make use of time and functionality, the business may move batch jobs to the evening so that higher priority, intensive jobs can be performed during the workday. Even with services like those delivered on cloud platforms, freeing up transacting CPU time when demand is highest makes sense.

In parallel to the human body, automation can function in an autonomic way, self-healing and dealing with daily processes without direct, conscious intervention.

A systems administrator can now spin up boxes and start-up systems in an automated way to perform auto-healing actions related to a variety of ERP system events. These could be security provisioning issues, maintenance tasks, data deduplication processes, or some form of standardized system patch.

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

Automation Accelerators

The new breadth of the cloud, and the tools we now use to interconnect systems and services, make this the right time to automate. Automation accelerators that tap into cloud-based resource capacity to perform actions in a manner that is non-disruptive, seamless, and effective are part of the innovative intelligent automation movement.

The leap to intelligent automation allows us to handle the orchestration of many entities (data sources, application functions, analytics calls, storage processes, and more) that are performed concurrently inside a system installation. This intelligently engineered parallelism enables us to accelerate our level of IT performance and thus, our business performance.

Getting Ahead of Problems

At this point on the automation innovation chart, organizations start to accelerate as they fix problems before they occur. A modern ERP system has a universe of data points all traversing internally and externally connected systems. Intelligent automation will detect and prevent problems even in complex systems.

Innovative technology shouldering the work reduces human error and man-hours spent on the same task while increasing the accuracy and consistency of the end-product. Additionally, we can now also track user experience to guarantee longer-term satisfaction. After incorporating this technology, the business can start to experiment with systems to seek out accelerated automated innovations that invigorate users to do more and leads to growth in the automation field.

Intelligent automation can be applied from the base operational substrate of an enterprise IT stack and grown outward to new incremental services as the business develops.

Along this new venture towards the future of the smart business, there will be some organizational and cultural shifts. In this regard, a truly forward-looking organization can streamline its adoption processes and think about how it handles change management and digital adoption as a formalized practice.

Also see: Top Digital Transformation Companies

Shifting Mindsets to Welcome Change

A traditional ERP team operating a traditional ERP shop is going to go through a paradigm shift on its way to becoming an innovative, automated shop with full-blown digital transformation initiatives.

To a business set in its ways, the incorporation of innovative, intelligent automation technology may seem like an insurmountable obstacle. The landscape of cloud business services has changed drastically, even over a short timeline.

The first step towards any major change is planning. The help of a trusted partner can lend stability to the rocky transitional period and move you from planning to execution quickly, preventing disruption.

Developing a culture that embraces change and taking every step to smooth a continual process of transition and ongoing development are the best ways to maximize future success and longevity. Stakeholders and employees alike can feel assured as they start to gain a deep and clear understanding of the growth potential of the new innovations we can build with modern, accelerated, automated platform intelligence. The cloud is here but the skies are clear, it’s time to look upward and head higher.

About the Author:

Matt Clemente, EVP, Global Operations at Lemongrass.

The post How Automation Drives Today’s IT appeared first on eWEEK.

In the tech industry, strategic partnerships are often layered with nuance. Some aim to enter or capture market opportunities that the partners have recognized or targeted. Others subtly shift the stance or terms of long-established relationships. A formalized deal between long time competitors likely reflects negotiations and horse trading that went on for months ahead of the announcement.

In most cases, partnerships help the respective companies to be seen or perceived in a new light. And in certain circumstances, that issue can be vital. One example is Kyndryl, which since its spinoff from IBM last November has moved aggressively to ink new or expanded partnerships with IT industry stalwarts, including Microsoft, VMware, Google Cloud, Pure Storage, Nokia and AWS. Those last two in particular provide insights on what customers and investors can expect from Kyndryl as an independent entity.

Also see: Top Cloud Companies

Benefits and Challenges of Independence

An interesting point in many of the news stories about the Kyndryl spinoff was how little commentators knew about the company’s business. Some described it as purely focused on offering consulting assistance to IBM customers hoping to modernize legacy systems and applications. Others saw Kyndryl’s deep expertise in on-premises hardware and software as out of touch with cloud-bound businesses.

While those notions may be partly correct, they also oversimplify what the company is and does today, as well as what it was and did as IBM’s infrastructure managed services unit.

Obviously, the organization has expertise in IBM hardware, middleware and associated software. But like virtually all global enterprises, IBM’s customers also use other vendors’ solutions. In order to succeed, its infrastructure managed services professionals had to be deeply knowledgeable about those offerings, as well. That is how the group (and now Kyndryl) drove $18.7B in FY2021 revenues, more than twice that of its nearest competitor.

Similarly, while many services engagements deal with on-premises systems, other sectors like cloud computing, hybrid cloud, cloud migrations and cloud-enabled digital transformation have been central to IBM’s efforts for the better part of a decade. This includes its infrastructure managed services unit.

In other words, far from being inflexibly fixed on what many think are stodgy, traditional IT skills and business technologies, Kyndryl’s 90,000 or so executives, managers and consultants are intimately acquainted with the latest and greatest computing solutions that enterprise vendors – including IBM – have to offer.

Good enough, but what can an organization do when outsiders don’t understand or know much about your business? Often you change the way you talk about your organization. Equally effective, you let the company you keep do some of the talking for you.

Also see: Top Digital Transformation Companies

Kyndryl’s Strategic Partners: Nokia and AWS

How is Kyndryl accomplishing this through its strategic partners and partnership announcements? Consider its relationships with Nokia and AWS.

Nokia: Edge to AI 

Announced on February 17th, Kyndryl and Nokia’s new global network and edge computing alliance focuses on helping customers transform their businesses with industrial grade LTE and 5G private wireless networks. The pair already co-developed a solution combining the Nokia Digital Automation Cloud (DAC)application platform with Kyndryl’s consulting, design, implementation and managed services offering. This has resulted in successful deployments and proof of concept for Dow.

Dow is using the Kyndryl/Nokia team to support asset tracking and worker safety and collaboration processes that it plans to deploy across its global facilities. Customers can gain immediate benefits with the solutions by leveraging existing LTE infrastructures, then use future Third Generation Partnership Project (3GPP) updates with Nokia DAC 5G for 5G enhancements.

With their new solution already bearing fruit, Kyndryl and Nokia plan to explore and develop integrated solutions and services for Edge Cloud, IP networking, Optics, Fixed Access, 4G and 5G Core and Network Operations. The companies also noted the benefits that private wireless connectivity can deliver in facilitating automation, robotics, AI, augmented and virtual reality.

Also see: Top AI Software  AWS: Cloud Infrastructure

In a February 23rd press release, Kyndryl and AWS announced a strategic expansion of their existing relationship. Kyndryl is already an AWS Premiere and MSP-certified partner and has built AWS competencies and a team of AWS-certified service professionals.

With this new effort, the pair will invest in building joint practices globally, and Kyndryl will establish an AWS Cloud Center of Excellence (CCOE) focusing on state-of-the-art solutions and services to develop joint solutions, including services for mission-critical infrastructure, mainframe, network and edge computing services, ERP and modernized applications and workflows across industries.

Kyndryl and AWS will also leverage their respective deep partnerships with VMware to develop an accelerator for VMware Cloud on AWS. Numerous Kyndryl customers are already running VMware on premises so the accelerator should expedite those organizations’ plans and efforts to run mission-critical workloads in the cloud.

In addition to deepening its collaboration with AWS, Kyndryl also plans to build out its own internal infrastructure in the cloud, leveraging AWS as a preferred cloud provider. As part of this announcement, AWS will become a Kyndryl Premier Global Alliance Partner.

Final Analysis: Challenging Industry Assumptions

What do these two individual partners and partnerships say about Kyndryl, and how might they change public perceptions?

Let’s start with AWS, the one most likely to have short-term impact on the company’s fortunes. In essence, this partnership expansion brings the two companies closer together via the co-development of joint practices, Kyndryl’s CCOE and the VMware accelerator.

Just as important is the pair’s focus on developing services for mission-critical infrastructure, network and edge computing, ERP and mainframe, application and workflow modernization. Those are potentially lucrative opportunities that also underscore goals and plans that AWS enumerated at its re:Invent conference in November. In essence, Kyndryl and AWS are pledging to broaden their common efforts and will continue ramping up services to address vital emerging solutions.

That same mix of current and future-focused plans is clear in Kyndryl’s partnership with Nokia. Though the companies’ efforts are still in early stages, the co-developed solution for Nokia DAC and its adoption by global customer Dow portend well. The same can be said for focusing on immediately useful LTE-based private wireless networks for industries that can be updated when customers are ready to take advantage of 5G technologies.

Dow’s initial use of the new solution for asset tracking and worker safety and collaboration – and its plans to expand the solution across global facilities – is a good example of how this might play out. It is also easy to see how customers could be intrigued and benefit from Kyndryl and Nokia’s plans to develop private wireless network solutions supporting next-gen automation, robotics, AI, augmented and virtual reality use cases.

Overall, these Kyndryl announcements highlight how a market leading vendor can continue to perform and improve in its areas of expertise while also working with strategic partners to support new and future use cases, processes, solutions and services. Anyone that believes Kyndryl is little more than an IBM stand-in or purveyor of stodgy data center services might want to reconsider those assumptions.

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

The post Kyndryl Charts a Clear Course with Strategic Partnerships appeared first on eWEEK.

Start-up Uniphore has announced its Q for Sales product, which brings the benefits of conversational artificial intelligence (AI) to enterprise sales organizations.

Conversational AI is a hot topic but has primarily been applied to contact centers, where the technology has been focused on dissecting chatbot interactions between businesses and consumers. This has been the “low hanging fruit” for the conversational AI industry yet it’s just scratching the surface of what’s possible.

Also see: Top AI Software 

Conversational AI Needs to Address More than Chatbots

Most of the two dozen or so vendors in this industry have been laser-focused on the chatbot market to improve inbound customer service, but Uniphore has taken a bit of a different approach.

Instead of building a conversational AI product, the company chose to develop a platform capable of delivering AI-based data analytics to any type of conversation – chat, voice or video. Last year, it added to its platform with the acquisition of Emotion Research Labs (ERL), which brought emotion detection into the fold.

This Uniphore platform was the foundation of the new Q for Sales product, which uses a combination of computer vision, tonal analysis, automatic speech recognition and natural language processing to capture the full emotional spectrum of sales conversations.

Also see: What Does 2022 Hold for Intelligent Automation

Improving Virtual Sales Meetings 

The company cited some interesting research that highlights the need for such a product. Pre-pandemic, 61% of B2B sales were conducted in person. This let salespeople read visual cues, adapt and build trust.

Today, 66% of B2B sales are done virtually, making it much more difficult for a salesperson to interpret body language and other non-verbal communication. Uniphore’s Q for Sales can bring better emotional intelligence to virtual meetings. In fact, it’s likely that this technology could make virtual meetings more effective than in-person ones.

Key features of Q for Sales include:

• Real-Time EQ Meeting Assist: Real-Time Emotion AI assistant to help sellers “read the room,” sense emotional cues and improve engagement during virtual meetings.
• Meeting EQ: Meeting playbacks, contextual key moments, engagement & sentiment trends for all members of a meeting.
• Deal EQ: Opportunity-level insights, customer sentiment & engagement timelines, and key moments from first meeting to closed-won.
• Key Moments: Provides real-time analytics that show highlights and low lights in the conversation — moments of peak and weak audience engagement so the presenter can self-correct.
• Team EQ and Improve: Shows presenters a performance chart of their EQ performance, which can be used for post-meeting analysis and coaching.
• Integrations: Works with major virtual meeting platforms including Zoom, and Webex by Cisco. Integrates with Salesforce CRM, Google Workspace and Office 365. Additional integrations to be announced soon.

Also see: Top Digital Transformation Companies Use Cases Beyond Transforming Sales 

While there are many use cases for the broader definition of conversational AI, I was not surprised that Uniphore chose to apply it to sales first.

In this 2021 ZKast with John Chambers, who was an early investor in Uniphore through his firm, JC2 ventures, Chambers talked about the opportunity for conversational AI. He did admit the customer service is the obvious opportunity but after that, one must “follow the money.” That is, apply the technology to the areas in which businesses invest a lot of capital, such as sales, as small differences in efficiency can pay big dividends. Other near term use cases include marketing, customer success and other customer facing roles.

Long term, this could also be integrated into remote education, telehealth, legal and other areas where interpreting body language is important. This pivot from in-person meetings to video is likely to stay in place for the foreseeable future and businesses need to bring in new technologies such as Q for Sales to close the human–digital gap.

Q for Sales is available now to qualifying beta customers in North America, and will soon be expanded to other regions around the globe. General availability is expected to be late summer or fall of this year. This announcement caps a strong first quarter for Uniphore, which recently announced a series E round of funding of $400M with a valuation of $2.5B.

I believe conversational AI to be a powerful technology that most businesses will adopt in some form. Businesses that are considering an investment in the technology need to understand the scope of it. While it can improve chatbots, which is where the industry has been focused, that’s just a small component of what’s possible. Conversations need to be thought of holistically – chat, voice, video and emotions are all used to interpret interactions – and AI can play a role in all of this.

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

The post Uniphore Rolls Out Conversational AI for Sales appeared first on eWEEK.

According to a recent estimate, almost 700,000 megabytes of data were created every minute in 2021. This incomprehensible tsunami of data has created an increased demand for data scientists and data analysts who can look at, comprehend and provide useful insights based on the information.

These roles have become so pivotal, in fact, that Glassdoor included them in its Top 50 Best Jobs in America list, coming in at #2 and #35, respectively.

It might be tempting for leaders who are starting their companies’ data journeys to cut corners by choosing either a data scientist or an analyst. In reality, both are necessary to achieve data nirvana because they have more differences than similarities. Let’s examine five major differences between data scientists and data analysts.

Also see: Best Data Analytics Tools 

1) Data Analysts vs. Data Scientists: Use of Data 

Data scientists are usually the first employees to look at data sets and can be thought of as their decoders. The data sets they work with are large, poorly structured, and require the data scientist to perform abstract analysis tasks such as sorting through it to find “signals” or previously unknown insights in the data.

Think of data scientists as your do-it-all resource, from wrangling/mining data, cleansing and structuring data, extracting business insights, building machine learning models, to delivering reporting to the business.

After getting a better understanding of their data, they can curate insights that can be put into action by data analysts. Once they receive known data sets with insights, data analysts are commonly tasked with finding trends in the data, creating reports and metrics that answer specific business questions, and communicating to a non-technical audience. Their analyses are used to help understand how a business is running and where there are opportunities for improvement.

Also see: Top Data Mining Tools  2) Data Analysts vs. Data Scientists: Use of Tools

Data scientists need to mine data, perform exploratory data analysis, and build machine learning models. So their tool set includes programming languages such as Python, Java, and R. There are also data science platforms that combine toolsets to speed up the data pipelining and mining processes.

Data analysts have a bit more leeway in terms of the tools they use. Some might set up pivot tables in Microsoft Excel, while others with more technical skills might use SQL queries against source systems. No matter the tools or platforms, the key is that the data sets are known, trusted and can be assembled into actionable business metrics for the C-suite and executive leadership.

Also see: What is Data Visualization

3) Data Analysts vs. Data Scientists: Education and Skills

Data scientists typically have a background in mathematics, programming, engineering, statistics or computer science. Since data scientists often come from a programming background, they typically have many technical skills. These skills all come into play when data scientists are searching for trends and insights the organization is not yet aware of.

Data scientists typically have graduate degrees more often than data analysts do. A data analyst’s educational background generally involves knowledge in quantitative fields such as computer science, statistics and mathematics, but also from specialized business domains.

Also see: Data Mining Techniques  4) Data Analysts vs. Data Scientists: Who Manages Them

Data scientists are likely to report to the chief data officer. With all the data scientists in an organization acting as a team, different business units may leverage them to provide high-value data sets for their respective data analysts to harvest. One day, a data scientist could be sorting through supply chain data and the next day combing through earnings information.

As suggested above, data analysts often reside within one business area. It is not uncommon to have one data analyst reporting to finance and a different data analyst reporting to sales or marketing, both focused on the metrics and reports that are most important to their business units. This narrow approach allows data analysts to truly become experts in their business area. 

Also see: Top Business Intelligence Software  5) Data Analysts vs. Data Scientists: Business Value

Because these two roles require differing backgrounds, skills and objectives, it’s natural that they also bring different value to organizations.

For data scientists, their value comes from uncovering undiscovered opportunities in data sets. Data analysts bring value to their organizations by turning these opportunities into actionable insights. 

Also see: Real Time Data Management Trends

Data Analysts vs. Data Scientists: the Big Picture

It should be clear by now that data scientists and data analysts are not the same – and that your organization needs both. That’s how you’ll get the most out of your data. To only employ one of these leaves the puzzle unsolved.

You need data scientists to identify new insights, and you need data analysts to give leaders what they need to answer specific business questions. Together, data analysts and data scientists fully use data to complete the picture of what your business could do and be.

About the author

Rex Ahlstrom, CTO and EVP of Growth & Innovation at Syniti. 

The post Data Analysts vs. Data Scientists: Five Key Differences appeared first on eWEEK.