GenAI to Boost Demand for Hybrid Data Platforms

George Winslow
·3 min read
AI.
Credit: Getty

STAMFORD, Conn.—Demand for advanced data platforms is likely to grow significantly over the next three years as more organizations adopt intelligent applications driven by GenAI and other forms of artificial intelligence, according to a new research report from the technology research and advisory firm Information Services Group (ISG).

In a development that will be important for streaming media providers and media companies seeking to provide personalized experiences, the ISG Data Platforms Buyers Guide, indicates that by the end of 2027, the development of intelligent applications providing personalized experiences through the use of GenAI will increase demand for hybrid data platforms capable of supporting both operational and analytical processing.

Despite the anticipated growing demand for hybrid data platforms, the report says there will continue to be a role for specialist analytic and operational data platforms to support a range of intelligent applications that might rely on real-time analytic or operational processing to deliver functionality and contextual recommendations or processing.

“The importance of advanced data platforms capable of supporting both operational and analytic workloads has been accentuated by an increased investment in artificial intelligence,” said Matt Aslett, director of analytics and data research at ISG. “While data-driven companies continue to use specialist analytic and data science platforms to train models offline, the need for online predictions and recommendations requires operational data platforms that are capable of performing AI inferencing in real-time.”

Adoption of GenAI capabilities by data platform providers remains relatively nascent, the study says.

Among the more than 40 providers evaluated by ISG, only about one quarter (10) have generally available digital assistant capabilities for converting natural language questions into database queries. A further ten have natural language query capabilities in preview, while the remainder have such capabilities in development, the study found.

Data platforms are essential for organizing, managing, and analyzing data within an enterprise, supporting operational applications for business operations and analytic applications for business evaluation. Additionally, data operations platforms and tools, as well as data intelligence platforms and tools, complement data platforms by enabling agile development and facilitating a better understanding of data production and consumption, the researchers reported.

When choosing a data platform, the primary consideration is whether the workload to be run on the platform is operational or analytic, the researchers said.

Operational data platforms support applications for running the business, such as finance, operations, sales and customer experience, while analytic data platforms are used for decision support, business intelligence, data science and AI/ML analysis.

ISG research finds the data platforms market is shifting from traditional relational data models to a proliferation of non-relational data platforms, including the use of NoSQL databases and object storage. The report also notes the increasing importance of intelligent operational applications driven by AI is blurring the traditional segmentation between operational and analytic data platforms.

“The use of data platforms is essential to the digital foundation of every enterprise and to the governed utilization of AI, which overall needs to be trusted and used securely, but also must be reliable and adaptable to the systems across the cloud and on-premises environments,” said Mark Smith, partner, ISG Software Research.

For its 2024 Data Platforms Buyers Guides, ISG evaluated software providers across three data platform categories – Data Platforms, Operational Data Platforms, and Analytic Data Platforms – and produced a separate Buyers Guide for each. A total of 42 unique software providers were assessed: Actian, Aerospike, Aiven, Alibaba Cloud, AWS, Cloudera, Cockroach Labs, Couchbase, Databricks, DataStax, Dremio, EDB, EXASOL, Google Cloud, Huawei Cloud, IBM, Incorta, InterSystems, KX, MariaDB, Microsoft, MongoDB, Neo4j, OpenText, Oracle, Percona, PingCAP, PlanetScale, Progress Software, Redis, Salesforce, SAP, ScyllaDB, SingleStore, Snowflake, SQream, Starburst Data, Tencent Cloud, Teradata, TigerGraph, VMware by Broadcom and Yugabyte.