AI success starts with better data

AI Success Starts with Better Data

Artificial intelligence has made one thing clear: the quality of your outcomes depends on the quality of your data.

When AI produces an unexpected answer, it’s easy to assume the technology failed. In many cases, inaccurate or incomplete data is the real issue. As organizations expand their use of AI, improving data quality has become just as important as deploying the infrastructure that powers it.

That responsibility, however, doesn’t belong to IT alone.

Business owns the data—IT enables trust

IT builds and manages the technology foundation that stores, secures, and delivers data. The business owns the data itself; what it means, how it should be used, and what level of quality is required.

That distinction is critical because no single team understands every aspect of enterprise data. Finance owns financial information. Sales understands customer relationships. Product teams define product data. Each group brings expertise that IT cannot provide on its own.

IT’s role is to establish a center of excellence for the data governance that connects these stakeholders. We provide the processes, security, technology, and visibility that allow business teams to manage data consistently across the enterprise.

Start with your most important data

Data governance can feel overwhelming if you try to tackle everything at once. The best place to begin is with the business entities that matter most.

At NetApp, we started with critical domains such as customers and products. These areas support core business processes and often span multiple organizations, making them the right place to establish governance practices before expanding further.

Focusing on high-value data first allows organizations to build momentum, demonstrate measurable progress, and create a framework that can be extended across the business over time.

Know where your data comes from

One of IT’s most important responsibilities is understanding data lineage.

Enterprise data rarely lives in one place. Customer information, for example, may originate in ERP systems, sales applications, support platforms, or product telemetry. As data moves between systems, inconsistencies can be introduced through duplicate records, transformations, or outdated copies.

That’s why identifying authoritative source systems is essential. The further information travels from its original source, the greater the chance for errors to occur.

Organizations can also strengthen governance by simplifying their data landscape. Consolidating systems where possible and reducing unnecessary handoffs creates a more reliable foundation for analytics and AI.

Governance is an ongoing discipline

Many organizations approach data governance as a project with a finish line. In practice, it is an ongoing operational discipline.

Business priorities evolve. Applications change. New data sources are introduced. AI creates entirely new ways of consuming information. Governance must evolve alongside them.

Executive sponsorship is essential to sustaining that effort. NetApp’s leadership champions data governance, while business units assign data stewards who work alongside IT to define ownership, establish expectations, and maintain accountability.

We also maintain a catalog of key business entities, their critical attributes, business owners, and data stewards. Keeping that information current requires continuous attention, not a one-time documentation effort.

To support that work, IT provides guidelines to maintain a dedicated data organization that supports governance activities across the business. Their responsibility is not to own the data, but to help business teams maintain consistency as the organization grows.

Measure progress together

One of the biggest misconceptions about data quality is that IT should define what “good” looks like. However, that is ultimately a business decision.

Every organization balances accuracy with practicality, and different business processes require different levels of precision for different business outcomes. Business leaders determine those expectations, while IT provides the tools needed to measure performance against them.

AI is accelerating this work by helping organizations automate analysis, surface quality issues faster, and develop better data-quality tools. Those insights allow business teams to validate results, prioritize improvements, and continuously strengthen the quality of their data.

Building better AI starts with better partnership

Organizations do not improve data quality overnight. Success comes from identifying the highest-value opportunities, establishing clear ownership, measuring progress, and building momentum one improvement at a time.

AI has raised expectations for data, but the solution isn’t better technology alone. It requires business ownership, strong governance, and an ongoing partnership with IT.

Organizations that build that discipline today will be far better positioned to realize AI’s full potential tomorrow.