Data Quality Management for AI Success: Your 2025 Guide

Why is data quality important for businesses today?

In today’s fast-paced, data-driven world, high-quality data is fundamental to business success. As artificial intelligence (AI) becomes integral to operations, businesses increasingly rely on advanced models for insights and decision-making. So maybe now, more than ever, the age-old adage “garbage in, garbage out” (GIGO) remains true: without reliable data, even the most sophisticated AI can produce flawed outcomes.

Accurate data is essential for trustworthy insights, actionable results, and reduced bias risks. As AI adoption increases, businesses will look to outsource more of their decision-making to this technology. In order to do this effectively, we will need to trust that it is built on data we trust. The consequences of poor data quality become even more critical. Flawed data can lead to incorrect decisions, missed opportunities, and wasted resources.

By investing in robust data management strategies, businesses can mitigate these risks and unlock AI’s full potential. High-quality data is no longer just a technical requirement; it’s a strategic imperative in the AI era.

Key challenges businesses face in implementing data quality management

Despite its significance, ensuring data quality is no easy task. Common challenges include:

• Volume of data: The vast amount of data generated daily can be overwhelming.

• Disparate silos: Isolated systems lead to inconsistencies and prevent a unified view.

• Defining "good data": Teams often struggle to agree on what makes data accurate, complete, or relevant. 

• Accountability issues: Ambiguity around ownership creates gaps in quality oversight.

• Identifying priorities: Determining where to focus improvement efforts can feel daunting.

• Focusing on value: Many organizations lose sight of what is important

The rise of AI has added new layers of complexity to the data quality question. According to Gartner, the degree to which data must be high-quality is entirely dependent on the AI use case: “AI-ready data means that your data must be representative of the use case, of every pattern, errors, outliers and unexpected emergence that is needed to train or run the AI model for the specific use.” To tackle these challenges, businesses need a structured approach that prioritizes impactful improvements and closes quality gaps.

Step-by-step guide to implementing data quality management

Phase 1: Finding focus

Step 1: Understand how data quality impacts decisions

Begin by assessing how data quality affects business outcomes. Analyze past issues caused by poor data and identify datasets with the greatest impact on decisions, operations, or innovation.

Focus on Critical Data Elements (CDEs)—data that directly drives business success. Collaborate with stakeholders to pinpoint pain points and understand how poor-quality data hinders their work.

Key questions to ask:

• What decisions rely on this data?

• What processes or projects are disrupted by poor-quality data?

• What does ideal data quality look like?

• What’s the cost of bad data (e.g., lost revenue, wasted time)?

Quantifying the impact helps set clear priorities and builds a compelling case for improvement.

Step 2: Create data quality business rules

Develop targeted business rules for your CDEs. These should focus on defining what fit-for-purpose data looks like.

Start with business-focused questions like:

• Who uses this data, and why?

• What risks arise if it’s inaccurate?

Document, rapidly test the theory, and refine the rules you’ve crafted to ensure you measure what matters.

Step 3: Assess and profile data

Using your new rules, profile your data to gain an initial understanding of data quality at a specific point in time.

Translate business rules into queries

Use query language that your organization is familiar with, and that fits the complexity of the business rule. Some rules will inherently be less complex to implement than others. For example, a completeness rule checking for missing data will likely be easier to create than a multifaceted validity rule.

Measure quality across transformation layers

By measuring quality-specific data at multiple points, you can first understand the origin of the issue. However, you can also determine if your data engineers have been enriched at a transformation layer and, therefore, improved its quality. If, for example, you have a medallion-based architecture: You may initially be thinking of just profiling the data at the “gold” layer as this is the cleanest data ready for analysis. But by also profiling the data at the bronze and silver layers you may uncover data quality issues that could highlight broken business processes or systems issues upstream.

Evaluate data at every stage, from ingestion to storage to final reporting, ensuring quality is maintained throughout.

Example: Check for consistency during data integration, completeness at storage, and accuracy during analysis.

Analyze and identify patterns

Analyze query results to uncover patterns or trends in quality issues. Look for recurring problems like frequent data entry errors, duplicate records, or mismatched formats. Understanding these patterns helps identify root causes and systemic gaps that need attention.

Look for recurring issues, such as missing fields, duplicate records, or anomalies that may signal larger systemic problems.

Identify trends like frequent data entry errors or mismatched formats in specific pipelines.

Create simple metrics

Establish simple metrics to track and quantify data quality issues. Examples include the percentage of incomplete records, the number of duplicates, or the rate of rule violations in critical fields. These metrics provide a clear picture of your data's current state and help prioritize areas for improvement.

Use these metrics to prioritize areas for improvement as well as to communicate progress clearly.

Phase 2: Continuous improvement

Step 4: Remediation

Address data problems through a combination of immediate fixes and root cause analysis. Focus on issues that significantly impact critical use cases. Its important to consider value, effort and feasibility when it comes to prioritizing improvements.

Prioritize high-impact, easy-to-resolve issues

Focus on fixes that deliver the most value.

Fix the data

Eliminate duplicates, correct errors, and fill in missing information.

Identify root causes 

Use a lineage tool to map the flow of data across systems and identify where errors originate.

Establish feedback loops 

Monitor progress and refine processes for continuous improvement.

Step 5: Monitor data quality continuously

Continuous monitoring is essential for maintaining high-quality data over time. By automating validation processes and making data quality transparent, businesses can quickly detect issues, build trust in their data, and ensure it remains fit for purpose as it scales.

Automate validation

Set up automated checks to ensure data quality across the pipeline. These processes can include:

• Verifying consistency across systems to prevent discrepancies.

• Flagging incomplete or invalid entries for review.

• Triggering alerts when data quality thresholds are breached, enabling faster issue resolution.

Scale with no-code tools

To implement data quality rules quickly and at scale, leverage no-code or low-code tools. Platforms like these allow you to define, test, and deploy rules without requiring extensive development work, making it easier to adapt and expand monitoring processes as your data ecosystem grows.

Make data quality visible in your data catalog

Integrate data quality metrics into your data catalog so users can easily assess the usability of data. 

When people locate data, reports, or dashboards in the catalog, they should be able to:

• Instantly see whether the data meets quality standards.

• Trace the data’s lineage back to its sources and understand the quality of those sources.

This transparency not only boosts confidence in the data but also allows users to make informed decisions about its relevance and reliability for their needs. Continuous monitoring becomes a powerful strategy for sustaining data quality and enhancing trust in your organization’s data assets.

Step 6: Establish data quality metrics and certification

Setting clear data quality metrics and certification standards is crucial for monitoring progress, driving improvements, and ensuring trust in your data. These benchmarks provide a framework for assessing quality, identifying gaps, and taking corrective action when needed.

Define benchmarks and thresholds

Set thresholds for data quality metrics to establish a baseline for acceptable quality. When quality dips below these levels, it triggers action to address issues. These benchmarks also allow you to measure whether your improvement efforts are achieving the desired outcomes.

Create alerts for critical data

Leverage these thresholds to implement alert systems. Depending on the criticality of the data, alerts can notify relevant teams when quality falls below acceptable levels, enabling timely intervention. For instance, critical operational data might demand immediate fixes, while less urgent data can follow a scheduled remediation plan.

Set improvement targets

Use benchmarks to define improvement goals. By setting clear targets, you create a roadmap for enhancing data quality over time. These targets motivate teams to take ownership of data quality initiatives, driving engagement and fostering a culture of continuous improvement.

Introduce data certifications

Implement a certification process to formalize quality standards. Datasets that meet minimum thresholds can be marked as "certified," signaling their reliability and readiness for use. Certification:

• Builds confidence among users.

• Helps teams quickly identify high-quality data for decision-making.

• Highlights areas where datasets fall short and require attention.

Integrate certifications into your data catalog to make quality transparent. Users can instantly see if a dataset is certified, understand its quality, and trace its lineage to evaluate the sources used.

Step 7: Build data literacy across teams

Equip employees with the skills to maintain data quality. Provide training on:

• Applying quality rules.

• Using monitoring tools effectively.

• Data curation best practices.

• Foster a culture of accountability, where all employees take ownership of data quality.

Step 8: Implement a data governance framework

Create a governance framework to integrate every aspect of your strategy. Define clear roles, policies, and responsibilities to manage data effectively.

Key components of your governance framework include:

• Ownership and stewardship roles.

• Balanced governance models for flexibility and control.

• Accessibility with robust security measures.

• Integration of quality metrics, monitoring, and certification.

• A strong governance framework ensures consistency, scalability, and trust across the organization.

Top tips for success

Prioritize Impact: Focus on areas that deliver the highest business value.

Prove ROI: Demonstrate the financial and operational benefits of initiatives.

Automate: Use tools to streamline processes and ensure efficiency. Flag Trusted Data: Help the business know what data is the trusted source. Lineage can accelerate: Your ability to understand the route cause of data quality issues

With the right strategy, tools, and culture, organizations can transform data quality into a powerful asset for growth and innovation.

Conclusion

In 2025 and beyond, a proactive approach to data quality management is no longer optional—it’s a business imperative. As AI-driven decision-making becomes the norm, organizations must ensure their data is accurate, complete, and trustworthy. Addressing common challenges such as data silos, unclear ownership, and inconsistent quality standards requires a structured, continuous improvement strategy.

By prioritizing high-impact data elements, implementing automated validation, and integrating quality metrics into a data catalog, businesses can build a foundation of reliable data that fuels innovation and drives confident decision-making.

Alation makes this process easier with a modern data intelligence platform that enables teams to monitor, govern, and improve data quality at scale. Want to see how Alation can help your organization build trust in its data? Get a demo today and take control of your data quality strategy.