The global data intelligence, analytics, and infrastructure (DIAI) market is projected to grow at a 17% compound annual growth rate through 2028 off a base of $541.1 billion in 2026 to exceed $1.2 trillion by 2031, according to a forecast published today by the Futurum Group.

Brad Shimmin, vice president and practice lead for data intelligence, analytics and infrastructure at the Futurum Group, said much of the spending will be driven by investments in artificial intelligence (AI) as organizations come to terms with the data management challenges that lie ahead.

For example, AI development and operations are forecasted to increase (24%), while demand for tools needed to observe data will see a similar spike (22%) in 2026. There will be increased demand next year (19%) for data management tools that operate at the semantic level to provide a higher level of abstraction above the raw data stored in, for example, a data lake.

In comparison, demand for data integration tools and storage platforms will grow at a slower 12% and 11% rate, respectively, in 2026. However, as the volume of data being generated using AI tools continues to increase, the data storage platform market will be growing at a rate of 18% by 2030, according to the report.

Right now, however, too many enterprises remain structurally rooted in the past, noted Shimmin. In fact, the winners in 2026 will be defined not by who has the best AI model, but by who has the discipline to stop, go back, and fix their data management plumbing, he added. The enterprise is philosophically ready for this and the will to change is there as the budget is allocated, noted Shimmin.

As data management evolves in the age of AI, there will be a fundamental shift away from manual data engineering workflows as more IT teams embrace automated extract transform and load (ETL) pipelines, also known as Zero-ETL, said Shimmin. In effect, data engineering teams are evolving into shepherds of data that is increasingly being used to drive AI applications and agents, he noted.

The pace at which organizations will make that transition will naturally vary but in a world of automated agents, bad data will no longer exist as an annoying reporting error, said Shimmin. Instead, it will stand as a direct brand risk, he added.

Ultimately, a cultural sea change is now occurring where data quality is finally moving upstream, noted Shimmin. A new standard focused on defining data meaning and enforcing adherence through tools such as data catalogs and techniques such as data contracts that block bad data at the source before it ever has a chance to confuse a model is emerging, he added.

The IT vendors that should benefit most from this shift are the leading vendors across the DIAI segment, with Oracle ($27.25 billion) in the lead, followed by IBM ($15.79 billion), Amazon Web Services ($15.76 billion), SAP ($15.53 billion, Microsoft ($13.76 billion), Alphabet ($12.46 billion), Cisco ($12.06 billion), Dell ($10.61 billion), Salesforce ($7.4 billion) and Hewlett-Packard Enterprise ($6.44 billion).

However, as with any major disruption in how data is being created and consumed, it’s probable other rivals will emerge and thrive, especially if any of the leading vendors rest too much of their existing laurels.