How to Create a Data Quality Strategy (in 5 Steps)

Every data team has experienced this moment. A senior executive questions a number in a critical report. The analyst who built it starts digging. Three hours later, they’ve found the problem. Bad data that cascaded through multiple pipelines, affecting dozens of downstream reports. The fix takes another day. Trust takes much longer to rebuild.

This scenario plays out thousands of times across organizations every day. We live in an age where companies collect more data than ever before, build sophisticated analytics platforms, and hire talented data teams. Yet somehow, the fundamental challenge remains the same. Can we actually trust our data?

The answer, too often, is no. Despite massive investments in data infrastructure and tooling, most organizations still struggle with data quality. Teams spend more time validating numbers than analyzing them. Executives hesitate before making data-driven decisions because they’ve been burned before. Data engineers fight the same fires week after week, patching problems that keep recurring.

The solution isn’t more technology or bigger teams. It’s a deliberate approach to data quality that treats it as a business priority rather than a technical afterthought. Companies that get this right stop firefighting data quality issues and start preventing them. Their analysts spend time on analysis instead of data validation. Their executives make decisions based on numbers they actually trust.

Dror Engel, Head of Product, Data Services, at eBay, and Barr Moses, CEO and co-founder of Monte Carlo, share five essential steps for establishing a successful data quality strategy.

This article walks through five essential steps for building a data quality strategy that works. We start with securing leadership buy-in by connecting data quality to business outcomes. Then we explore which metrics actually matter and how to turn them into actionable SLAs. From there, we cover establishing ownership and accountability across your organization, implementing the right automation and tools, and creating communication channels that keep your strategy improving over time. These aren’t theoretical concepts but practical steps you can start implementing this week.

• What is a data quality strategy?
• 1. Secure Leadership Buy-In and Stakeholder Support
• 2. Define Clear Data Quality Metrics and SLAs
• 3. Establish Data Ownership and Stewardship
• 4. Automate Your Lineage and Data Governance Tooling
• 5. Create a Communications Plan For Your Data Quality Strategy
• Key takeaways

What is a data quality strategy?

A data quality strategy is your organization’s roadmap for maintaining trustworthy, accurate data through defined standards, governance practices, and systematic monitoring. It establishes who’s responsible for data quality, what good data looks like, and which tools and processes will keep your data reliable.

In practice, this means deciding who owns which data, what “good” looks like for each dataset, and how you’ll catch problems before they impact the business. It means creating automated checks that run continuously, establishing clear accountability, and building processes that prevent bad data from spreading through your systems. When data drives critical decisions, having this strategy isn’t optional anymore. It’s essential for success.

Yet many organizations still struggle with poor data quality. Bad data is expensive. Gartner reports poor data quality costs organizations at least $12.9 million a year on average. Forrester found that data analysts spend 40% of their time dealing with data quality problems instead of doing actual analysis. One data leader told us that bad data accounted for 1,200 cumulative hours per week for her 500-person team.The gap between recognition and action is striking. While 89% of organizations acknowledge that data quality matters, only 22% have a dedicated data quality program in place. This disconnect reveals a fundamental challenge. We’ve gotten good at collecting and storing data, but we haven’t mastered making that data trustworthy and usable. A proactive data quality strategy addresses this gap, saving money, freeing up analyst time, and building confidence in data across the company.

1. Secure Leadership Buy-In and Stakeholder Support

Your data quality strategy needs strong executive support and cross-functional buy-in to succeed. Without leadership backing, even the best strategy will fail due to lack of resources or alignment. This isn’t just an IT problem. It’s a business issue that affects revenue, compliance, and operational efficiency.

Start by assessing your current state honestly. How do you measure data quality today? What are your biggest data pain points? Be transparent about the status quo. If your teams spend significant time firefighting data issues, say so. If a data error recently impacted a business decision, document it. One CDO at a financial services company discovered his team was processing hundreds of thousands of jobs daily but still couldn’t trust the output. That kind of reality check gets leadership’s attention.

Connect data quality directly to business objectives. Clean, trustworthy data accelerates analytics for revenue growth. Bad data erodes customer trust and can violate compliance requirements. When you show executives how data quality impacts what they care about, the investment justification becomes clear. Present specific data quality KPIs that tie to business outcomes. Show how improving data completeness by 10% could increase marketing conversion rates. Demonstrate how better data freshness could speed up monthly reporting by days. These concrete connections between data quality metrics and business results make the value proposition undeniable.

Define who will be accountable for the strategy’s success. Name a Data Quality Lead or committee. Assign responsibilities to data owners in each department. Leadership wants to know who is responsible for what. Clear ownership and stewardship demonstrate that this initiative is organized and progress can be tracked. Establish which data quality KPIs each owner will be measured against. This transparency reassures stakeholders that the strategy will be executed systematically, not just discussed in meetings.

When you get leaders invested from the start, data quality becomes a company-wide priority instead of an afterthought for the data team. This foundation sets up everything that follows.

2. Define Clear Data Quality Metrics and SLAs

Just as software teams use SLAs to guarantee system uptime, data teams need SLAs for data quality. These are explicit targets and thresholds that data must meet. Without measurable standards, “good data” remains a vague concept that no one can achieve or verify.

Focus on tangible data quality dimensions that matter to your business. Data accuracy measures error rates or how well your data matches trusted sources. Data completeness tracks the percentage of missing values in critical fields. Data timeliness captures data freshness and how quickly information flows from source to destination. Data consistency ensures data conforms to standard formats across datasets. Validity confirms data falls within acceptable ranges and follows business rules. Keep it simple with these well-understood measures instead of creating opaque custom scores that confuse everyone.

Turn these metrics into realistic SLAs. Define target thresholds for each key metric. Maybe 99% of customer records need complete contact information. Perhaps daily sales data must be 95% error-free and loaded by 8am each day. Your raw data ingestion might tolerate more issues than your final warehouse data. Different lifecycle stages require different standards. A logistics company might require real-time GPS data to be 99.9% accurate but accept 95% accuracy for historical route data used in quarterly planning.

Each SLA should clearly link to business impact. Complete customer contact fields mean marketing campaigns won’t miss contacts due to gaps. Daily data refresh by 8am ensures analysts have current information for morning reports. Revenue forecasting requires yesterday’s sales data with 99% accuracy to maintain model reliability. When stakeholders see how each metric affects their work, they’ll support the standards and help enforce them.

These metrics and targets must be agreed upon collaboratively. Finance needs their data by 9am for regulatory reporting. Marketing requires customer segments updated weekly. Sales wants real-time pipeline visibility. Capture these requirements in your SLAs. Success needs to be defined in numbers, just like uptime or response time in other domains. This quantification lays the groundwork for monitoring and improvement.

3. Establish Data Ownership and Stewardship

Data quality is everyone’s responsibility, but without clear ownership, it quickly becomes nobody’s responsibility. You need specific people accountable for specific datasets, with defined roles and expectations. This human element makes or breaks your strategy.

Start by identifying data owners for major data domains or critical datasets. These individuals, typically within business units, take accountability for the quality of their data. The head of sales owns customer relationship data. The CFO owns financial reporting data. The supply chain director owns inventory data. These owners set expectations and make decisions about their domains.

Working alongside owners, data stewards handle the day-to-day implementation of quality practices. They enforce standards, investigate issues, and coordinate fixes. This division of labor ensures both strategic oversight and operational execution. When a data issue arises, everyone knows exactly who to contact. Problems stop falling through cracks because someone is always responsible.

Beyond individual roles, you need a broader stewardship program that creates shared accountability. Form a Data Quality Council with representatives from engineering, analytics, and business units. These champions don’t just attend meetings. They actively advocate for quality in their departments, help colleagues resolve data issues, and share what works. They run training sessions on data best practices. They document and celebrate quality improvements. They create forums where teams can discuss problems openly.

The real goal is cultural change. Data quality needs to become as important as code quality in engineering or customer service in sales. This happens through visible actions and consistent reinforcement. Include data quality KPIs in team performance metrics. Recognize teams that improve their data quality scores. Fix that one data error that’s been annoying everyone for months, then tell everyone about it. When the marketing team sees that cleaning up customer data improved campaign performance by 20%, they become believers. When finance realizes that better data quality means fewer late nights during quarter close, they become advocates. Success builds on success, creating momentum that sustains the program long-term

4. Automate Your Lineage and Data Governance Tooling

Manual data quality processes like ad-hoc cleaning or sporadic SQL checks aren’t enough anymore. As data environments scale, these approaches become bottlenecks. Automated data quality monitoring catches issues in real time and keeps data trustworthy. Think of it like having a tireless quality inspector on your assembly line, checking every piece of data that passes through, while your team focuses on solving problems instead of finding them.

Modern data quality tools continuously monitor your pipelines and datasets for anomalies, missing data, schema changes, and other quality issues. They use machine learning or predefined rules to detect problems the moment they occur. If your daily sales pipeline suddenly drops in volume by 30%, automated monitors trigger alerts. If a column that’s usually 100% complete suddenly has nulls, you’ll know immediately. These tools track data lineage to trace errors back to their source, detect anomalies automatically, and send alerts to the right people through email or Slack.

The real value comes from scalability and speed. Companies now deal with dozens of data sources, real-time feeds, and constantly changing schemas. Humans alone can’t watch all of this efficiently. Automated tests and monitors run 24/7 across all your datasets, ensuring quality at a scale manual efforts can’t match. While only about 14% of organizations have fully automated their data quality processes, those who adopt automation early gain significant advantages in efficiency and reliability.

Implementation doesn’t have to be complex. Start by integrating data quality checks directly into your data pipelines. Run automated validation tests whenever data loads, similar to how software tests run in continuous integration. Set up anomaly detection for your most critical metrics. Monitor schema changes that might break downstream processes. Use data observability tools that provide visibility across your entire data stack. The specific tools matter less than having systematic, automated coverage of your data assets.

Instead of waiting to hear “the data looks off” from a business user, your team catches issues first and maintains trust. Data engineers spend less time in reactive firefighting mode and more time building reliable systems. Analysts stop second-guessing their numbers and focus on generating insights. The entire organization benefits from this shift from reactive to proactive data quality management.

5. Create a Communications Plan For Your Data Quality Strategy

A data quality strategy isn’t a “set it and forget it” project. It requires ongoing attention, transparent communication, and constant refinement based on what you learn. The best strategies adapt and improve over time.

Start with regular communication to keep everyone aligned and invested. Weekly or monthly reports on data quality metrics, resolved issues, and SLA progress show stakeholders that their data is being actively managed. These updates can be simple email summaries, live dashboards, or monthly meetings. The format matters less than consistency and clarity. A brief weekly email showing three caught issues and twenty hours of rework prevented often proves more valuable than lengthy quarterly presentations.

While you’re sharing progress, stakeholders need clear channels to raise their own concerns. When the sales team notices account data seems off, they should know exactly how to report it. When finance has questions about data freshness, they need a direct line to answers. This bi-directional flow creates a partnership where everyone works together to improve data quality, rather than a situation where the data team works in isolation.

Transparency builds trust, especially when you’re honest about problems. Sending post-mortems after major incidents might feel uncomfortable, but it demonstrates accountability. Stakeholders appreciate knowing what went wrong, what you’re doing about it, and how you’ll prevent similar issues. Even skeptics become supporters when they see this level of engagement and honesty.

Every piece of feedback and every incident becomes fuel for improvement. That recurring error in the sales pipeline tells you where to add validation. The confusion about data freshness suggests you need better documentation. The spike in quality issues after each product launch reveals a gap in your change management process. Instead of treating these as isolated problems, look for patterns that reveal systematic improvements you can make.

Build formal feedback loops to accelerate this learning. Survey analysts quarterly about which data sources they trust and which they avoid. When they report problems, don’t just fix them. Follow up to explain what you did and verify the solution works for them. This responsiveness transforms data quality from an abstract metric into something that directly improves their daily work.

After six months, evaluate where you stand. Data issue frequency might have dropped significantly. Resolution times may have shrunk from days to hours. Analysts might report spending far less time double-checking numbers. Share these wins to maintain momentum, but also acknowledge what hasn’t worked. If a monitoring approach failed to catch important issues, explain what you learned and how you’re adapting. This honest assessment keeps the organization engaged and ensures your strategy keeps getting better rather than stagnating.

Key takeaways

Implementing a solid data quality strategy pays off in trustworthy data and confident decision-making. By securing leadership buy-in, defining clear metrics, assigning stewardship roles, leveraging automation, and maintaining open communication, you build a foundation of reliable data. With that foundation, data engineers and analysts can focus on driving insights and innovation rather than scrambling to fix errors.

The contrast is stark. With a strategy, those same teams make faster decisions, catch problems before they spread, and actually trust their numbers. Projects run smoothly. Reports get delivered on time. Business users stop questioning every metric. The entire organization moves faster because people aren’t constantly second-guessing the data.

Data quality isn’t just about avoiding problems. It’s about enabling your organization to confidently leverage data for growth. When marketing knows their customer data is accurate, they can personalize campaigns that actually work. When finance trusts their numbers, they can spot trends and opportunities instead of just reconciling discrepancies. When operations has reliable inventory data, they can optimize supply chains instead of fighting fires.

The steps outlined above aren’t theoretical. They’re practical actions you can start taking this week. Pick one critical dataset and assign an owner. Define three quality metrics that matter to your business. Set up one automated monitor. Send one update to stakeholders about data quality progress. Each small step builds toward a culture where data quality becomes standard practice, not an afterthought.

Your data will never be perfect, and that’s fine. The goal isn’t perfection but trust. When your organization knows that data issues are caught quickly, resolved systematically, and prevented from recurring, they can use data confidently to make better decisions. That transformation from data skepticism to data confidence is what makes all the effort worthwhile.

At the end of the day, the goal of your data quality management strategy will be to ensure that teams across the entire company feel empowered to use data that is trustworthy. In fact, we believe a robust and comprehensive data quality strategy makes all the difference when it comes to doing just about anything in data, from scaling an effective data team to building a great data platform.

So pat yourself on the back: by reading this article, you’re already a step (or should we say, five steps) ahead.

Interested in learning more about how to implement a successful data quality strategy? Reach out to Dror Engel or Barr Moses.

This article was originally published in Forbes.

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