Ventana Research Analyst Perspectives

Mind the Gap Between Data and Analytics

Posted by David Menninger on Nov 16, 2022 3:00:00 AM

If you’ve ever been to London, you are probably familiar with the announcements on the London Underground to “mind the gap” between the trains and the platform. I suggest we also need to mind the gap between data and analytics. These worlds are often disconnected in organizations and, as a result, it limits their effectiveness and agility.

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Topics: embedded analytics, Analytics, Business Intelligence, Data Governance, Data Management, data operations, Analytics & Data

Orchestrating Data Pipelines Facilitates Data-Driven Analytics

Posted by Matt Aslett on Oct 25, 2022 3:00:00 AM

I have written a few times in recent months about vendors offering functionality that addresses data orchestration. This is a concept that has been growing in popularity in the past five years amid the rise of Data Operations (DataOps), which describes more agile approaches to data integration and data management. In a nutshell, data orchestration is the process of combining data from multiple operational data sources and preparing and transforming it for analysis. To those unfamiliar with the term, this may sound very much like the tasks that data management practitioners having been undertaking for decades. As such, it is fair to ask what separates data orchestration from traditional approaches to data management. Is it really something new that can deliver innovation and business value, or just the rebranding of existing practices designed to drive demand for products and services?

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Topics: Data Management, Data, AI and Machine Learning, data operations, Analytics & Data

Astronomer’s Cloud-Based Data Orchestration Brings Efficiency

Posted by Matt Aslett on Sep 29, 2022 3:00:00 AM

I have recently written about the organizational and cultural aspects of being data-driven, and the potential advantages data-driven organizations stand to gain by responding faster to worker and customer demands for more innovative, data-rich applications and personalized experiences. I have also explained that data-driven processes require more agile, continuous data processing, with an increased focus on extract, load and transform processes — as well as change data capture and automation and orchestration — as part of a DataOps approach to data management. Safeguarding the health of data pipelines is fundamental to ensuring data is integrated and processed in the sequence required to generate business intelligence. The significance of these data pipelines to delivering data-driven business strategies has led to the emergence of vendors, such as Astronomer, focused on enabling organizations to orchestrate data engineering pipelines and workflows.

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Topics: Cloud Computing, Data Management, Data, data operations, Analytics & Data

Augmented Intelligence Reduces Dependency on AI/ML Skill Sets

Posted by David Menninger on Sep 15, 2022 3:00:00 AM

Business intelligence has evolved. It now includes a spectrum of analytics, one of the most promising of which has been described as augmented intelligence. Some organizations have used the term to describe the practical reality that artificial intelligence with machine learning is not replacing human intelligence, but augmenting it. The term also represents the application of AI/ML to make business intelligence and analytics tools more powerful and easier to use. It’s this latter usage that I prefer and I’d like to explore in this perspective.

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Topics: Analytics, Business Intelligence, natural language processing, AI and Machine Learning, Analytics & Data, Collaborative & Conversational Computing

Rockset Offers Cloud-Based Real-Time Analytics

Posted by Matt Aslett on Aug 30, 2022 3:00:00 AM

I have written recently about increased demand for data-intensive applications infused with the results of analytic processes, such as personalization and artificial intelligence (AI)-driven recommendations. Almost one-quarter of respondents (22%) to Ventana Research’s Analytics and Data Benchmark Research are currently analyzing data in real time, with an additional 10% analyzing data every hour. There are multiple data platform approaches to delivering real-time data processing and analytics and more agile data pipelines. These include the use of streaming and event data processing, as well as the use of hybrid data processing to enable analytics to be performed on application data within operational data platforms. Another approach, favored by a group of emerging vendors such as Rockset, is to develop these data-intensive applications on a specialist, real-time analytic data platform specifically designed to meet the performance and agility requirements of data-intensive applications.

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Topics: Cloud Computing, Data, Streaming Analytics, Analytics & Data, Streaming Data & Events, operational data platforms, Analytic Data Platforms