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.
The distinction is particularly relevant to Ventana Research as we expand our data research coverage. To date, the majority of our data platforms coverage has specifically focused on analytic databases and data warehouses, complemented by our focus on data lakes. While there are distinct use cases and drivers for data warehouses and data lakes, these are both examples of analytic data platforms.
Analytic data platforms are designed to store, manage, process and analyze data, enabling organizations to leverage data to operate with greater efficiency across on-premises, hybrid and multi-cloud environments. These platforms support applications used to analyze the business, including decision support, business intelligence, data science, artificial intelligence and machine learning. They include data warehouses and data lakes as well as the increasing convergence of data warehouse, data lake and data-streaming technologies. Convergence is a primary theme in the analytic data platforms sector. I assert that, through 2024, data warehouse, data lake and data-streaming technologies will converge to create analytic data platforms, enabling organizations to collect and analyze all types of operations-generated information.
In addition to analytic data platforms, Ventana Research is now expanding data coverage to also address operational data platforms. Operational data platforms are designed to store, manage and process data to support worker-, customer- and partner-facing operational applications across on-premises, hybrid and multi-cloud environments. They support applications used to run the business, including finance, operations and supply chain, sales, human capital management, customer experience, and marketing. These platforms include relational and non-relational databases (including NoSQL) as well as the increasing convergence of relational and non-relational approaches, along with hybrid operational and analytic processing to support intelligent applications infused with personalization and AI-driven recommendations.
Convergence is also a key theme in the operational data platforms segment, with relational database providers adding support for non-relational data models (documents, graph), and NoSQL vendors adding capabilities and features that have previously been the preserve of the incumbent relational vendors. This has helped NoSQL vendors make in-roads into enterprise adoption, although the established vendors still dwarf their rivals thanks to the installed base. I assert that, through 2026, incumbent relational database vendors will continue to be used for the majority of existing operational workloads, with emerging relational and non-relational database providers primarily adopted for new applications.
There are many trends and themes that impact both analytic and operational data platforms, such as the rise of hybrid and multi-cloud data processing as well as increased demand for hybrid operational and analytic processing to support intelligent applications. As this suggests, there is an element of overlap between the analytic and operational data platform segments. There have always been general-purpose databases that could be used for both analytic and operational workloads, while there are some examples among early adopters using data lakes to support operational as well as analytic workloads. While the overarching trends and themes will also be a feature of our data research coverage going forward, I believe that for most use cases, there is a clear, functional requirement for either analytic or operational data platforms, and I recommend that organizations considering options for new data platforms continue to use this distinction as a starting point, drawing up a short list of potential technology providers for consideration.