Data Management Trends in 2026: Moving Beyond Awareness to Action

Introduction

Over the last decade, organizations have increasingly embraced data management concepts such as data governance, quality, strategy, and literacy. However, many still struggle to implement these fundamentals effectively.

The 2025 DATAVERSITY Trends in Data Management (TDM) Survey highlights this striking disconnect. Most participants are at an early stage of data governance, with 61% listing data quality as a top challenge.

This gap between recognition and action has stark consequences. McKinsey reports that nearly two-thirds of firms have failed to scale their AI projects, while 70% of the largest public companies are pivoting from innovation to ROI focus. Forrester predicts this reorientation will delay 25% of AI spending into 2027. The message is clear: Organizations want to adopt AI processes but don’t seem to understand the importance of data.

Four critical areas separate leaders from laggards: data quality management, modern data governance, functional AI governance, and data literacy investment. According to Business Application Research Centre (BARC), best-in-classcompanies demonstrate an edge by executing these fundamentals tactically, not just strategically. In 2026, competitive advantage belongs to organizations that move beyond awareness to action.

Moving Data Quality Management to the Workflow

Many companies are in the beginning stages of action. Over 50% of participants in our 2025 TDM survey have implemented data quality initiatives. Additionally, 40% plan to optimize existing efforts and expand tooling. In the meantime, some detail a misalignment of software platforms with business needs.

Consequently, professionals run into regular hassles: 62% report incomplete data, 58% cite capture inconsistencies, and 57% complain about data integration issues. Moreover, 75% of leaders don’t trust their data for decision-making.

Data leaders are failing to scale artificial intelligence projects due to poor data quality, reports Gartner. According to IDC, companies that don’t prioritize AI-ready data will see a 15% productivity loss by 2027.

Steps to Improve Data Quality

To remedy these issues, businesses will need to continuously integrate metadata management, data architecture optimization, and cultural change into their operations to achieve AI-ready data that supports business strategy.

Active metadata drives greater AI model accuracy and operational efficiency by providing semantic meaning between information and AI models. Yet, according to our 2025 TDM survey, only 11% of organizations have high metadata management maturity.

Organizations must also optimize increasingly complex data architectures through data modeling to maximize critical system coverage by the business. DataOps and data observability provide technical capabilities to support the modeling processes.

Lastly, the best data quality tools in the world don’t drive adoption – companies will need to implement people-first practices to get an understanding of what the data means.

This prioritization is urgent: Gartner predicts that through 2026, organizations will abandon 60% of AI projects due to insufficient data quality.

Maturing Data Governance Practices

Data governance supports data quality and other data management functions by providing an ongoing data service across the enterprise. Many organizations recognize its importance and have implemented governance or plan to do so. Furthermore, 44% of companies have a data governance lead who is driving their data management. Despite this, only 15% of survey participants report having mature data governance. Comments in our 2025 TDM survey show an unevenness of data governance maturity across organizations, with some units more advanced than others.

To move forward with AI projects, companies need mature, adaptive data governance programs that safeguard data privacy and support transparency. Those that have this maturity achieve 24.1% revenue improvement and 25.4% improvement in cost savings from AI, according to a study conducted by IDC.

Steps to Improve Data Governance

Organizations need to make governance an enabler, not a burden that gums up innovation. To get to this point, firms need to efficiently identify critical data issues, address them, and acknowledge governance successes.

Donna Burbank, a recognized industry expert with over 25 years of experience, noted the importance of metadata in a recent webinar:

“More and more organizations are realizing that in order to drive business value from data, robust metadata is needed to gain the necessary context and lineage around key data assets.”

Data governance policies must remain nimble due to geopolitical uncertainties. By 2028, 60% of organizations will migrate sensitive workloads to new cloud environments, requiring coordination between data governance policies and business operations.

As companies move into 2026, they will face rapidly shifting priorities, leading to conflicts between business as usual and demands to adapt. Consequently, data governance programs will need to balance structured governance with empathy to hold data stewards accountable while supporting their daily responsibilities.

To address these three demands, data governance must mature beyond the planning and policy stages to implementation. Identify a high-value use case that demonstrates concrete business value while minimizing operational impact.

Implementing Functional AI Governance

While many organizations have some level of data governance, AI governance is notably undeveloped. According to the 2025 TDM survey, only 4% have high maturity in both data governance and AI governance.

Meanwhile, more than 90% of companies have workers using personal chatbot accounts for daily tasks, often without approval from IT – a trend known as “shadow AI.” With such proliferation and little guidance, 51% of respondents from organizations using AI say their organizations have seen at least one instance of a negative consequence.

Regulations will continue to grow in complexity and costs for non-compliance will rise. The EU’s AI Act takes full effect on August 2, 2026, with fines up to €35 million or 7% of global revenue.

By 2027, fragmented regulations will cover 50% of world economies, driving $5 billion in regulatory compliance costs. Organizations will scramble to build a functional AI governance framework.

Steps to Improve AI Governance

To legally deploy and use AI solutions, businesses must implement functional AI governance policies with a roadmap by the end of the year. AI governance must tie to data management capabilities, informing the rate of AI adoption. Especially, they will need to monitor AI transactions, as 80% of unauthorized ones will originate internally, not from external attacks.

Precise metadata enables real-time data tracing through its lifecycle, providing context for AI behaviors. Access to this kind of information through metadata management, with the support of AI governance, will help organizations to explain AI outputs and demonstrate risk mitigation.

As data ecosystems grow complex with AI projects, Forrester predicts these challenges:

• AI data center upgrades will trigger multiday cloud outages.
• At least 15% of enterprises will seek private AI atop private clouds to manage risk, reduce costs, and lock in data.
• Enterprises will adopt neoclouds, cloud environments created for AI workloads through GPUs, offering scalable and high-performance infrastructures at a lower cost.

As companies consider these factors, IT teams must return to governance basics, ensuring their AI-ready data infrastructures align with business.

The speed of AI innovation also puts significant pressure on business cultures to adapt. Multiple studies, including the 2025 TDM survey, PacificAI’s 2025 AI Governance Survey, and the McKinsey Global Study, observe a growing enthusiasm for generative AI without the governance maturity needed. To navigate this situation, organizations will need to assess AI maturity to know how to proceed with AI governance.

Organizations must inventory their capabilities and invest in AI maturity assessments that involve stakeholders. With 80% of unauthorized AI transactions originating internally, companies need these assessments to understand existing risks. Additionally, investing in the maturity assessments will lead to clearer communications around AI.

Investing in Data Literacy and Culture

Many companies recognize that data literacy underpins a mature data management approach and the successful usage of AI. In the 2025 TDM survey, responses identify data literacy as one of the biggest data management challenges and one of the most valuable topics for training and educational content.

Professionals emphasized a need for training in:

• Data governance and quality: 65%
• Data strategy: 57%
• AI and machine learning: 48%

These professionals have a strong appetite for practical, role-relevant content. However, as of 2025, only 36% of organizations have implemented data literacy.

Understanding AI inputs and outputs connects with data literacy and managing AI. By 2028, Gartner predicts that loss of control of AI solutions will be a top concern for 40% of Fortune 1000 companies.

This situation has only encouraged regions to embrace data literacy. For example, 60% of organizations in the United Arab Emirates (UAE) have identified AI as the top emerging technology investment priority, while 62% have already identified data literacy and AI skills enablement as a top priority for 2025. Organizations that lag in these areas will only fall further behind.

Steps to Improve Data Literacy

Organizations will require workers to advance their data literacy skills to meet job expectations. IDC forecasts that 40% of all G2000 job roles in 2026 will involve working with AI agents, redefining long-held traditional entry, mid, and senior-level positions.

An Accenture survey found that 75% of executives believe employees are data-proficient, yet only 21% of employees feel confident. This perception gap must close as metadata management skills separate high performers from strugglers.

With vendor AI promises outpacing actual functionality, company leaders must also upskill to understand AI capabilities and risks. Gartner estimates organizations emphasizing executive AI literacy will achieve 20% higher financial performance by 2027.

Fostering worker trust to adopt AI in their processes will pose a big challenge to many organizations. Poor data and AI literacy erode confidence among 21% of AI decision-makers. To increase adoption and reduce risk, 30% of large enterprises will mandate AI training.

As 40% of G2000 roles will involve AI agents in 2026, role-specific training will become critical. Organizations must measure the impact of literacy training on business objectives. To counter poor literacy, enterprises will mandate and measure AI and data literacy to increase adoption and reduce risk.

Conclusion

In 2026, most businesses will acknowledge data management fundamentals as critical. They will speak about improving data quality for their operations and maturity in data governance. They will talk about achieving a functional AI framework and increasing a data-literate workforce.

Success requires action that manages metadata, technical architecture, and evolving organizational culture. Training is required to integrate these requirements and data guidance. Companies that act strategically on these fundamentals – not just acknowledge them – will increase their competitiveness in 2026.