Analyst Perspectives

The increasing importance of intelligent operational applications driven by artificial intelligence (AI) is blurring the lines that have traditionally divided the requirements between operational and analytic data platforms. Operational data platforms have traditionally been deployed to support applications targeted at business users and decision-makers to run the business, with analytic data platforms typically supporting applications used by data and business analysts to analyze the business.

I have previously written about the functional evolution and emerging use cases for NoSQL databases, a category of non-relational databases that first emerged 15 or so years ago and are now well established as potential alternatives to relational databases. NoSQL is a term used to describe a variety of databases that fall into four primary functional categories: key-value stores, wide-column stores, document-oriented databases and graph databases. Each is worthy of further exploration, which is...

In recent years, many enterprises have migrated data platform workloads from on-premises infrastructure to cloud environments, attracted by the promised benefits of greater agility and lower costs. The scale of cloud data platform adoption is illustrated by Ventana Research’s Data Lakes Dynamic Insights research: For two-thirds (66%) of participants, the primary data platform used for analytics is cloud based. As the quantity and importance of the data platform workloads deployed in the cloud...

Ventana Research recently announced its 2024 Market Agenda for Analytics and Data, continuing the guidance we have offered for two decades to help enterprises derive optimal value and improve business outcomes.

Discussion about potential deployment locations for analytics and data workloads is often based on the assumption that, for enterprise workloads, there is a binary choice between on-premises data centers and public cloud. However, the low-latency performance or sovereignty characteristics of a significant and growing proportion of workloads make them better suited to data and analytics processing where data is generated rather than a centralized on-premises or public cloud environment. ...

As articulated in Ventana Research’s Data Platforms Buyer’s Guide and DataOps Buyer’s Guide research, the combination of cloud computing and advanced analytics has lowered the cost of storing and processing large volumes of data, accelerating the emergence of new data platform and data operations products that enable organizations to gain operational efficiency and competitive advantage. The right combination of data platform and data management products is essential to ensure that the right...

The phrase ‘big data’ may have largely gone out of fashion, but the concept of storing and processing all relevant data continues to be important for enterprises seeking to be more data-driven. Doing so requires analytic data platforms capable of storing and processing data in multiple formats and data models. This will be an important focus for the forthcoming Data Platforms Buyer’s Guide 2024. 

I recently discussed how fashion has a surprisingly significant role to play in the data market as various architectural approaches to data storage and processing take turns enjoying a phase in the limelight. Pendulum swing is a theory of fashion that describes the periodic movement of trends between two extremes, such as short and long hemlines or skinny and baggy/flared trousers. Pendulum swing theory is similarly a factor in data technology trends, with an example being the oscillation...

I previously described how Oracle had positioned its database portfolio to address any and all data platform requirements. The caveat to that statement at the time was that any organization wanting to take advantage of the company’s flagship Oracle Autonomous Database could only do so using Oracle Cloud Infrastructure (OCI) cloud computing service, their own datacenter or a hybrid cloud environment. The widespread popularity of Oracle Database and the advanced automation capabilities delivered...

I recently articulated some of the reasons why IT teams can fail to deliver on the business requirements for data and analytics projects. This is an age-old and multifaceted problem that is not easily solved. Organizations have a role to play in alleviating the issue by ensuring that their business processes and project planning support a collaborative approach in which business and IT professionals work together. Data and analytics product vendors can also help by delivering products that are...