50 statistics that show the importance of data & AI to business success in 2026

Data is now central to business competitiveness. Whether it is underpinning better decision-making, training AI models, empowering AI agents or increasing innovation and collaboration, successfully harnessing data is a critical business priority.

To transform data into value it has to be easily accessible and consumable by business users and AI. Trusted data products available on intuitive, self-service data product marketplaces deliver this access. Creating a strategy and operationalizing data sharing through an effective technology stack and business-wide collaboration is vital, but potentially complex. 

What’s the current state of data consumption, and where are there gaps to be filled? To find out, we collected and analyzed the latest research from leading consultancies and analysts, including Gartner, Forrester, IDC and McKinsey. Read on to get the full picture.

1. Just over half (51%) of respondents believe that the Chief Data Officer (CDO) role is well-established and successful in their organization (Wavestone Data and AI Leadership Executive Survey) 
2. CDOs and Chief Data & Analytics Officers (CDAOs) have moved from having 8 primary responsibilities to 15 over the last three years – an 87.5% increase (2023 and 2026 Gartner Chief Data and Analytics Officer Agenda Survey)
3. 87% of CDOs now report into the C-suite (Deloitte CDO Survey)
4. 70% of CDAOs are responsible for AI strategy and operating models (Gartner) 
5. Over the last year ago, 92% of data and analytics leaders have increased or maintained team size (Gartner CDAO Agenda Survey for 2026)
6. 56% of CDOs said their data management budget had increased over the last year (Deloitte CDO Survey)
7. However, 53% of CDOs said that they had insufficient resources to achieve their goals (AWS)

The role of the Chief Data Officer is expanding in both scope and importance, particularly with the rise of AI. However, while budgets are increasing, many feel they are not sufficient to successfully handle all of their growing responsibilities.

8. CDOs who successfully increased data sharing activities were 1.7 times more effective at showing demonstrable, verifiable value to stakeholders (Gartner)
9. Effective users of data sharing strategies could see financial benefits reaching as high as 9% of their annual revenue in the next five years, Benefits of data sharing include a 14% increase in workflow efficiency, 15% improvement in customer satisfaction, and 11% reduction in costs (Cap Gemini)
10. 60% of data leaders are making data sharing a strategic priority, a figure that has increased by 21% in three years (Huwise)
11. The value potential from data and analytics will reach $17.7 trillion over the coming years (McKinsey)
12. By 2027, utilities using open data will lower their cost to serve by 10% (Gartner)
13. 36% of senior leaders said that they expect to create new data, analytics or AI-based businesses in the next five years (McKinsey)
14. 28% of business leaders believe they have data assets that could be monetized, but that currently have unrealized commercial potential (McKinsey)
15. However, 68% of available data still remains unused in organizations (IDC)

The benefits of making data available and consumable across the organization are clear. Not only do they transform existing operations, but enable the launch of new services that create additional revenues and business models.

16. 71% of enterprises now create data products (Forrester)
17. 50% of respondents have already deployed data products, and another 29% are committed to piloting or considering deployment within the next year (Gartner CDAO Agenda Survey)
18. Organizations implementing product-oriented data strategies (including data products) report 15–20% efficiency gains in operations (McKinsey)
19. Organizations using data mesh architecture experienced a 25% improvement in data quality, thanks to domain-specific ownership and better-aligned data products (Accenture)
20. Data mesh has increased the re-usability of data products by 40% (McKinsey)
21. 56% of CDOs prioritize development of data products as a value driver (Deloitte CDO Survey)
22. 67% of decision-makers plan to deploy self-service data marketplaces to enable broader access and discovery (Huwise)
23. By 2026, natural language will become the dominant method to query and interface with existing data management ecosystems, leading to 10 times better data consumption (Gartner)

Data products are essential to scaling data consumption by both business users and AI. However, simply creating data products is the first step in the process – they have to be made easily available to all through intuitive data product marketplaces to enable seamless, ongoing consumption.

24. Over 75% of organizations now use AI in at least one business function, reporting an average of a 5% increase in revenues and 10% reduction in costs (McKinsey) 
25. AI-ready data is related to 20% greater business outcomes (Gartner)
26. By 2027, companies that do not prioritize high-quality, AI-ready data will struggle scaling GenAI and agentic solutions, resulting in a 15% productivity loss (IDC)
27. 58% of CDAOs say “investment in AI-ready data” is a top 5 choice (Gartner)
28. But 90% say their architecture needs a minor/major overhaul to support AI (Gartner)
29. Only 26% of D&A or AI leaders think their data engineering practices are highly or extremely effective in supporting existing AI use cases (Gartner)
30. By 2027, 75% of CDAOs who have not established themselves as an indispensable strategic collaborator to AI success will lose their C-Level position (Gartner) 
31. By 2028, 80% of GenAI business applications will be developed on organizations’ existing data management platforms, reducing implementation complexity and time to delivery by 50% (Gartner)

Reliable, trustworthy data is the foundation of AI. However, due to the complexity of their infrastructure and a lack of collaboration, many organizations are struggling to deliver AI-ready data to the business, leading to higher costs, failed AI projects, and missed opportunities.

32. Data governance is one of the top three differences between firms that capture the value of digitization and firms that don’t  (McKinsey)
33. 62% of CDOs report data governance as a top data challenge to AI initiatives (Precisely) 
34. Only 13% of leaders report that their data & analytics governance functions can fully lead AI governance (Gartner)
35. By 2027, 60% of organizations will fail to realize the anticipated value of their AI use cases due to incohesive data governance frameworks (Gartner)
36. 71% of organizations now have a formal data governance program, up from 60% in 2023 (Precisely) 
37. 51% of CDOs rank data governance as their top priority for the next year (Deloitte CDO Survey)
38. For CDOs, data governance increased in priority from 41% → 57% year-on-year (Precisely) 
39. Organizations with mature governance programs report 15–20% higher operational efficiency (McKinsey)
40. 69% of organizations plan to increase spending on data governance (Forrester) 
41. 45% of CDOs consider data governance as their top concern (Gartner)

Strong data governance has always been essential to protecting data and ensuring regulatory compliance. However, it now needs to go further and balance cataloging and protecting data with providing secure access to business users and AI to unlock business value.

42. Poor data quality leads to a 15-25% revenue loss (MIT Sloan) 
43. 93% of data leaders said poor/untrustworthy data is their biggest barrier to AI success (Wavestone) 
44. Three-quarters (76%) of data leaders say data-driven decision-making is their primary goal, yet 67% don’t completely trust their data (Precisely) 
45. 55% of organizations admit they can’t always trace data from its source to the point of consumption (Accenture) 
46. 55% of respondents say data quality is their top barrier to unlocking AI’s full potential (Beyond Big Data)
47. 79% of leaders said AI was essential to their company’s future, but just 14% believed that their data maturity could support AI at scale (Wipro) 
48. To aid quality and consumption 30% of organizations will adopt active metadata practices by 2026 (Gartner)
49. 80% of firms are prioritizing metadata (Dataversity)
50. The global metadata management tools market size will reach US$36.44 billion by 2030, growing at a CAGR of 20.9% from 2025 to 2030 (Grandview Research)

The adoption of AI has put the spotlight firmly on data quality. In particular, effective metadata is essential to ensuring that data and data products can be easily discovered, accessed, and consumed by both employees and increasingly AI agents.

Analyst research and CDO surveys demonstrate a clear trend for putting data at the heart of business operations and AI programs. However, these statistics also highlight a need to focus on technology, processes, and skills to successfully harness data and use it to transform business performance moving forward. Keep up to date with the latest developments in the data management space by visiting our blog or signing up for our monthly newsletter.