David Menninger's Analyst Perspectives

Enhancing Data Catalog with AI

Posted by David Menninger on Sep 22, 2022 3:00:00 AM

Organizations are collecting data from multiple data sources and a variety of systems to enrich their analytics and business intelligence (BI). But collecting data is only half of the equation. As the data grows, it becomes challenging to find the right data at the right time. Many organizations can’t take full advantage of their data lakes because they don’t know what data actually exists. Also, there are more regulations and compliance requirements than ever before. It is critical for organizations to understand the kind of data they have, who is handling it, what it is being used for and how it needs to be protected. They also have to avoid putting too many layers and wrappers around the data as it can make the data difficult to access. These challenges create a need for more automated ways to discover, track, research and govern the data.

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Topics: Business Intelligence, Data Governance, Data Management, Data, data operations, AI & Machine Learning

Zoho Unifies Data and Analytics

Posted by David Menninger on Jul 7, 2022 3:00:00 AM

Organizations are continuously increasing the use of analytics and business intelligence to turn data into meaningful and actionable insights. Our Analytics and Data Benchmark Research shows some of the benefits of using analytics: Improved efficiency in business processes, improved communication and gaining a competitive edge in the market top the list. With a unified BI system, organizations can have a comprehensive view of all organizational data to better manage processes and identify opportunities.

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Topics: business intelligence, embedded analytics, Data Governance, Data Management, natural language processing, AI and Machine Learning, data operations, Streaming Analytics, Streaming Data & Events, operational data plaftforms

Semantic Models Benefit Analytical Processes

Posted by David Menninger on Jun 28, 2022 3:00:00 AM

I’ve never been a fan of talking about semantic models because most of the workforce probably doesn’t understand what they are, or doesn’t recognize them by name. But the findings in our recent Analytics and Data Benchmark Research have changed my mind. The research shows how important a semantic model can be to the success of data and analytics processes. Organizations that have successfully implemented a semantic model are more than twice as likely to report satisfaction with analytics (77%) compared with a 33% overall satisfaction rate. Therefore, I owe it to all of you to write about them.

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Topics: Business Intelligence, Data Management, AI and Machine Learning, data operations, Analytics & Data, semantic model

Data and Analytics Processes: Can We Get Personal?

Posted by David Menninger on May 24, 2022 3:00:00 AM

There is a fundamental flaw in information technology, or at least in the way it is most commonly delivered. Most technology systems are developed under the assumption that all people will use the system primarily in the same way. Sure, there are some options built in — perhaps the same action can be initiated by either clicking on a button, selecting a menu item or invoking a keyboard short-cut. The problem is that when every variation needs to be coded into the system, the prospect of providing personalized software programs to every individual is impractical.

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Topics: Business Intelligence, Data Management, natural language processing, AI and Machine Learning, data operations, Analytics & Data

Accelerate Business Outcomes with Immuta Data Access Governance

Posted by David Menninger on May 19, 2022 3:00:00 AM

The data governance landscape is growing rapidly. Organizations handling vast amounts of data face multiple challenges as more regulations are added to govern sensitive information. Adoption of multi-cloud strategies increases governance concerns with new data sources that are accessed in real time. Our Data Governance Benchmark Research shows that organizations face multiple challenges when deploying data governance. Three-quarters (73%) of organizations report disparate data sources as the biggest challenge, and half of the organizations report creating, modifying, managing and enforcing governance policies as the second biggest challenge.

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Topics: Data Governance, Data Management, data operations

AtScale Universal Semantic Layer Democratizes and Scales Analytics

Posted by David Menninger on Feb 10, 2022 3:00:00 AM

Organizations of all sizes are dealing with exponentially increasing data volume and data sources, which creates challenges such as siloed information, increased technical complexities across various systems and slow reporting of important business metrics. Migrating to the cloud does not solve the problems associated with performing analytics and business intelligence on data stored in disparate systems. Also, the computing power needed to process large volumes of data consists of clusters of servers with hundreds or thousands of nodes that can be difficult to administer. Our Analytics and Data Benchmark Research shows that organizations have concerns about current analytics and BI technology. Findings include difficulty integrating data with other business processes, systems that are not flexible enough to scale operations and trouble accessing data from various data sources.

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Topics: Analytics, Business Intelligence, Data Integration, Data, data lakes, AI and Machine Learning, data operations, Streaming Analytics

ThoughtSpot Enables Simpler Analytics with AI and NLP

Posted by David Menninger on Jan 21, 2022 3:00:00 AM

Organizations today have huge volumes of data across various cloud and on-premises systems which keep growing by the second. To derive value from this data, organizations must query the data regularly and share insights with relevant teams and departments. Automating this process using natural language processing (NLP) and artificial intelligence and machine learning (AI/ML) enables line-of-business personnel to query the data faster, generate reports themselves without depending on IT, and make quick decisions. Some organizations have started using NLP in self-service analytics to quickly identify patterns and simplify data visualization. Our Analytics and Data Benchmark Research finds that about 81% of organizations expect to use natural language search for analytics to make timely and informed decisions.

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Topics: embedded analytics, Analytics, Business Intelligence, Data Integration, Data, natural language processing, data lakes, data operations, AI & Machine Learning, data platforms

Analytic Ops: The Last Mile of Data Ops

Posted by David Menninger on Nov 24, 2021 3:00:00 AM

Organizations have become more agile and responsive, in part, as a result of being more agile with their information technology. Adopting a DevOps approach to application deployment has allowed organizations to deploy new and revised applications more quickly. DataOps is enabling organizations to be more agile in their data processes. As organizations are embracing artificial intelligence (AI) and machine learning (ML), they are recognizing the need to adopt MLOps. The same desire for agility suggests that organizations need to adopt AnalyticOps.

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Topics: business intelligence, Analytics, Data Governance, Data, Digital Technology, data operations, data platforms

Data in 2021: Ventana Research Market Agenda

Posted by David Menninger on Feb 26, 2021 3:00:00 AM

Ventana Research recently announced its 2021 Market Agenda for data, continuing the guidance we have offered for nearly two decades to help organizations derive optimal value and improve business outcomes.

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Topics: Data Governance, Data Preparation, Information Management, Data, data lakes, Streaming Data, data operations, Event Data, Data catalog, Event Streams, Event Stream Processing

DataOps: Managing the Process and Technology

Posted by David Menninger on Oct 7, 2020 3:00:00 AM

For decades, data integration was a rigid process. Data was processed in batches once a month, once a week or once a day. Organizations needed to make sure those processes were completed successfully—and reliably—so they had the data necessary to make informed business decisions. The result was battle-tested integrations that could withstand the test of time.

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Topics: Data Governance, Data Integration, Data Preparation, Information Management (IM), dataops, data operations