Analyst Perspectives

I have previously written about the impact of intelligent operational applications on the requirements for data platforms. Intelligent applications are used to run the business but also deliver personalization, recommendations and other features generated by machine learning and artificial intelligence. As such, they require a combination of operational and analytic processing functionality. The emergence of these intelligent applications does not eradicate the need for separate analysis of...

Unstructured data has been a significant factor in data lakes and analytics for some time. Twelve years ago, nearly a third of enterprises were working with large amounts of unstructured data. As I’ve pointed out previously, unstructured data is really a misnomer. The data is structured; it's just not structured into rows and columns that fit neatly into a relational table like much of the other information enterprises process. Consequently, it requires different skills, different technology...

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.

We’ve been saying for years that natural language processing (NLP) and natural language analytics would greatly expand access to analytics. However, prior to the explosion of generative AI (GenAI), software providers had struggled to bring robust natural language capabilities to market. It required considerable manual effort. Many analytics providers had introduced natural language capabilities, but they didn’t really resonate with enterprise requirements. They required significant effort to...

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.

Ventana Research has announced its market agenda for 2024, continuing a 20-year tradition of credibility and trust in our objective efforts to educate and guide the technology industry. Our research and insights are backed by our expertise and independence, and we do not share our Market Agenda or our market research, including analyst and market perspectives, with any external party before it is published. We continuously refine our Market Agenda throughout the year to ensure we offer the...

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.