Ventana Research Analyst Perspectives

Dremio Embraces Data Lakehouse with Cloud Launch

Posted by Matt Aslett on Jun 24, 2022 3:00:00 AM

I previously explained how the data lakehouse is one of two primary approaches being adopted to deliver what I have called a hydroanalytic data platform. Hydroanalytics involves the combination of data warehouse and data lake functionality to enable and accelerate analysis of data in cloud storage services. The term data lakehouse has been rapidly adopted by several vendors in recent years to describe an environment in which data warehousing functionality is integrated into the data lake environment, rather than coexisting alongside. One of the vendors that has embraced the data lakehouse concept and terminology is Dremio, which recently launched the general availability of its Dremio Cloud data lakehouse platform.

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Topics: business intelligence, Analytics, Data, data lakes, data platforms

Oracle Positions to Address Any and All Data Platform Needs

Posted by Matt Aslett on May 5, 2022 3:00:00 AM

I recently described how the operational data platforms sector is in a state of flux. There are multiple trends at play, including the increasing need for hybrid and multicloud data platforms, the evolution of NoSQL database functionality and applicable use-cases, and the drivers for hybrid data processing. The past decade has seen significant change in the emergence of new vendors, data models and architectures as well as new deployment and consumption approaches. As organizations adopted strategies to address these new options, a few things remained constant – one being the influence and importance of Oracle. The company’s database business continues to be a core focus of innovation, evolution and differentiation, even as it expanded its portfolio to address cloud applications and infrastructure.

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Topics: business intelligence, Analytics, Data Integration, Data, AI and Machine Learning, data platforms

Improving the State of Analytics in Organizations

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

Despite all the advances organizations have made with respect to analytics, our most recent research shows the majority of the workforce in the majority of organizations are not using analytics and business intelligence (BI). Less than one-quarter (23%) report that one-half or more of their workforce is using analytics and BI. This is a problem. It means organizations are not enabling their workforce to perform at peak efficiency and effectiveness. It means the workforce in many organizations does not have access to the same information by which they are being measured. It means organizations must find other ways to communicate with, and manage, the workforce.

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Topics: Sales, business intelligence, embedded analytics, Analytics, Data, Sales Performance Management, Digital Technology, Digital Commerce, natural language processing, subscription management, partner management, Revenue Management, Sales Engagement, Collaborative & Conversational Computing

Data Platforms Landscape Divided Between Analytic and Operational

Posted by Matt Aslett on Dec 14, 2021 3:00:00 AM

As I noted when joining Ventana Research, the range of options faced by organizations in relation to data processing and analytics can be bewildering. When it comes to data platforms, however, there is one fundamental consideration that comes before all others: Is the workload primarily operational or analytic? Although most database products can be used for operational or analytic workloads, the market has been segmented between products targeting operational workloads, and those targeting analytic workloads for almost as long as there has been a database market.

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Topics: business intelligence, Analytics, Data, data lakes, AI and Machine Learning, data operations, 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