Data Governance Visionaries: Who Owns Data Quality for AI/ML?

AI and ML projects thrive on high-quality data. But who is responsible for ensuring that data is accurate, complete, and reliable? 

A recent BARC survey found that 46% of companies rely on business process owners to manage or oversee data quality for AI/ML projects—more than any other role, including data scientists and data governance leaders. While this highlights the growing role of business teams in data quality, it also raises a key question: Should data quality have a single owner? And if so, which team? 

In our experience with leading enterprise customers across the Fortune 500, the answer is no: Data quality is a team sport.

The Reality of Data Quality Ownership

The real work of data quality happens where bad data causes the most pain. That could be:

• A business analyst whose dashboard numbers don’t add up
• A data scientist whose model isn’t performing as expected
• A finance team member reconciling conflicting revenue figures

Each of these roles has a stake in data quality. That means no single team can or should be the sole owner. In fact, a single bad data table can wreak havoc across teams. 

Let’s imagine a table called monthly_revenue_summary. It’s a crucial dataset that feeds into dashboards, machine learning models, and financial reports. But this month, something is off. 

When monthly_revenue_summary loads incomplete data, a business analyst sees unexpected dips in sales, a data scientist’s churn model skews due to missing revenue, and the finance team scrambles to reconcile conflicting reports. The root cause? A failed data pipeline that delayed ingestion, something a data engineer must address to ensure timeliness. Meanwhile, the business process owner isn’t concerned with one-off glitches but wants assurance that such issues are rare and non-material.

Data quality isn’t one team’s responsibility. It’s a shared effort requiring vigilance, collaboration, and proactive monitoring to prevent costly business interruptions.

Anomalo is designed for the entire data team, whether you’re a data producer, consumer, or any role in between.

The Role of Data Governance in Scaling Data Quality

That said, data governance teams play a critical role in making data quality strategic and scalable. Across Anomalo’s enterprise customers, we’re seeing Data Governance leaders take on three key responsibilities:

1. Setting the strategy. Establishing policies, frameworks, and best practices for ensuring high-quality data across the organization.
2. Driving adoption. Encouraging teams to embed data quality into their workflows, rather than treating it as an afterthought.
3. Fostering a culture of data ownership. Empowering business and technical teams to take responsibility for the data they use every day.

This last point is essential. Data governance teams alone can’t “fix” data quality issues. In fact, a core ‘secret sauce’ underpinning the data-driven success of customers like Discover Financial, Nationwide, and ADP is their ability to democratize access to data and foster a data culture. Organizations that successfully scale data quality don’t just enforce rules. They create a culture of shared responsibility where business and data teams proactively work together.

Anomalo is trusted by the world’s top data governance leaders.

Emerging Challenges: Budget and Adoption

Despite this, major challenges remain: budget and adoption. While business teams feel the impact of bad data, data governance teams often end up carrying the responsibility—and the cost—of data quality solutions. This creates a disconnect, where data quality is seen as a governance function rather than a cross-functional priority.

The key to overcoming this? Making data quality a priority for the entire organization. That means:

1. Embedding data quality monitoring into AI/ML pipelines and business processes
2. Encouraging cross-team collaboration to spot and resolve issues faster
3. Aligning budgets with the teams most affected by poor data

Anomalo, Designed for the Entire Data Team

Data quality isn’t just an IT problem, a data science problem, or a governance problem: it’s a business problem. And like any business problem, it requires input and accountability from across the organization.

At Anomalo, we believe data is a team sport. And data quality is a shared responsibility among everyone who creates, touches, uses, and cares about the data. You can’t win with data and foster an innovative data-driven culture if you’re working in silos.

At the end of the day, the best approach to data quality is a shared one. The question isn’t just “who owns data quality?” Rather, it’s “how do we ensure that everyone is empowered to drive ownership and shared accountability?”

Anomalo is redefining enterprise data quality with AI-powered automation and an intuitive no-code UI, so your entire data team can detect and resolve issues fast. With seamless data catalog integrations, smart alerts, and automated root cause analysis, Anomalo cuts detection time from months to minutes—ensuring the right people see and solve issues before they impact your business.

Ready to transform your data governance strategy? Let’s talk.