From Trust to Transformation: Elevating Data Quality with AI and Catalogs

In an era where data drives nearly every strategic decision, poor data quality isn’t just a nuisance—it’s a risk. For modern enterprises, the consequences of unreliable data can range from operational inefficiencies to financial loss and reputational damage. As the Senior Director of Product Management at Alation, I’ve had the chance to develop and lead our data quality strategy and have seen firsthand how critical data health is to organizational success.

This blog distills my experience into a practical guide for data leaders. We explore what data quality means today and how companies can build robust, scalable data quality programs.

What is data quality in the enterprise?

In large organizations, data quality isn’t just about clean rows in a database. Key attributes include:​

• Accuracy: Ensuring data correctly represents real-world entities or events.​

• Consistency: Maintaining uniformity across datasets and systems.​

• Completeness: Having all necessary data without omissions.​

• Timeliness: Keeping data up-to-date and available when needed.​

• Lineage and Governance: Tracking data origins and transformations to ensure transparency and compliance.​

High-quality data serves its intended purpose effectively, whether for strategic planning, operational efficiency, or customer engagement.

Why data quality is critical for business success

Data underpins nearly every strategic decision in modern enterprises. If the underlying data is inconsistent, outdated, or incorrect, it can lead to flawed insights and misguided strategies—both of which pose operational and financial risks. 

Poor data quality can derail AI initiatives, stall analytics projects, and trigger compliance violations. Conversely, strong data quality practices reduce waste, lower risk, and create a solid foundation for innovation and growth.

Consequences of poor data quality

The ramifications of subpar data quality are extensive and can significantly hinder an organization's performance. Consequences of low-quality data being used for critical decision-making include:

• Eroded trust: Inaccurate data diminishes stakeholder confidence, leading to reluctance in adopting data-driven initiatives.​

• Financial loss: Erroneous data can result in costly mistakes, such as mispriced products or incorrect financial reporting.​

• Operational inefficiencies: Teams may spend excessive time rectifying data issues instead of focusing on strategic tasks.​

• Reputational damage: Public exposure of data inaccuracies can tarnish an organization's credibility.​

One need not look far for examples of poor data quality checks having disastrous consequences. In 1999, NASA suffered a data integration error when one team used imperial units while another used metric units. This misalignment led to the spacecraft's trajectory being off-course, ultimately causing its destruction. This overlooked inconsistency cost NASA more than $125 million.

Signs you might have a data quality problem

Many organizations don’t realize they have a data quality issue until the symptoms become impossible to ignore. Here are some of the most common red flags:

• Frequent rework or reconciliation: Teams waste time cleaning and validating the same data repeatedly.

• Mismatched KPIs across reports: Different dashboards tell different stories with the same metrics.

• Lack of trust: Analysts and business users avoid certain datasets they suspect are unreliable.

• High support ticket volume: Data issues create a steady stream of user complaints.

• Poor outcomes from data projects: AI models and analytics efforts fall flat due to faulty inputs.

These symptoms often stem from siloed systems, inconsistent standards, outdated rules, and the absence of automated monitoring.

Hidden data quality issues and how to find them

Beyond obvious errors, subtle data quality issues can lurk unnoticed, posing significant risks:​

• Metadata misalignment: Discrepancies between business definitions and actual data calculations can lead to inconsistent reporting.​

• Stale data: Datasets that appear valid but haven't been updated, leading to outdated insights.​

• Schema drift: Unanticipated changes in data structure or format that can disrupt data pipelines.​

• Obsolete validation rules: Outdated data validation criteria that no longer align with current business processes.​

Detecting these hidden issues requires a combination of automated data profiling, regular monitoring, and leveraging advanced tools. AI-powered solutions can proactively surface anomalies, detect schema changes, and highlight underutilized datasets, enabling organizations to address potential problems before they escalate.​

Moreover, as AI and machine learning models become integral to business operations, the need for high-quality data becomes even more critical. AI systems trained on flawed data can produce inaccurate predictions and perpetuate biases, leading to misguided decisions and potential ethical concerns. Ensuring data quality is foundational to the success of AI initiatives.

Who owns data quality? Building a culture of accountability

Data quality is a shared responsibility that spans both technical and business domains:​

• Technical stakeholders: Data stewards, engineers, and architects are tasked with defining data standards, implementing monitoring systems, and enforcing governance policies.​

• Business domain experts and data consumers: These individuals provide contextual understanding, validate data accuracy within business processes, and report anomalies.​

Fostering a culture of accountability ensures that data quality is integrated into the creation, transformation, and consumption of data, rather than being an afterthought.​

Implementing a data product operating model can further clarify roles and responsibilities related to data quality. In this model, data is treated as a product, with dedicated roles such as data product managers overseeing the lifecycle of data assets. These managers ensure that data products are designed, built, and maintained to deliver real business value, aligning technical efforts with business objectives. This structured approach enhances usability, governance, and the overall impact of data within the organization.

How to measure the ROI of data quality initiatives: 5 key metrics

Investing in data quality yields tangible business outcomes—but to secure ongoing support and funding, data leaders need to demonstrate that value with clear, measurable results. Below are key ROI metrics organizations can use, along with examples of how each can be quantified:

#1: Reduced manual rework

What to measure: Time and cost savings from less manual data cleansing, validation, and reconciliation.

Example: Before a data quality initiative, a marketing analyst might spend 10 hours per week cleaning customer segmentation data. After implementing automated validation and standardized input rules, the analyst only spends 2 hours per week. Over a year, that’s 416 hours saved per analyst, which translates to tens of thousands in labor cost savings across the team.

#2: Faster time-to-insight

What to measure: Reduction in the time it takes to generate accurate reports or complete analytics projects.

Example:  An executive dashboard previously took 3 weeks to prepare due to inconsistent data definitions across departments. After establishing centralized data standards and cataloging metrics definitions, the same report can now be assembled in 5 days. That’s a 66% improvement in speed, enabling quicker decision-making.

#3: Lower incidence of costly errors

What to measure: Decrease in errors that lead to financial or compliance penalties.

Example: A financial institution reduced regulatory reporting errors by implementing data validation rules aligned with compliance standards. This led to a drop in annual compliance fines from $500,000 to $50,000, a direct and quantifiable return on improved data quality.

#4: Improved user adoption

What to measure: Increased usage of data platforms and tools as trust in the data improves.

Example: Pre-initiative, only 25% of business users regularly accessed the internal analytics platform due to concerns about accuracy. After improving data quality and transparency (via a data catalog and quality scores), adoption rose to 60%. This more than doubled the user base, amplifying the value derived from data investments.

#5: Increased revenue or productivity

What to measure: Revenue growth or productivity gains directly tied to better data.

Example: A retail company used high-quality customer data to personalize email marketing campaigns. Conversion rates improved by 20%, generating an additional $1.2M in annual revenue. Similarly, better supplier data enabled faster procurement cycles, saving hundreds of hours annually for operations teams.

Data quality and customer trust

Customer relationships hinge on accurate information. One billing error or inconsistent message can undermine a customer’s confidence in your brand. High data quality ensures every touchpoint—from marketing emails to customer service interactions—is based on accurate, trustworthy data. Over time, this builds loyalty and credibility in the market.

The role of AI and ML in data quality management

AI and ML are transforming data quality from reactive to proactive. Emerging capabilities include:

• Automated rule suggestions: Recommending checks based on observed usage patterns.

• Automated data recommendations: Suggesting relevant datasets or attributes to validate or improve data quality.

• Anomaly detection: Spotting unusual patterns or values that suggest data issues.

This “augmented data quality” approach combines human expertise with machine intelligence—creating smarter, faster, and more scalable solutions.

The value of a data catalog for data quality

A data catalog is one of the most powerful tools in the data quality toolkit. Here's why:

• Centralized visibility: Catalogs offer a unified view of metadata, lineage, and usage.

• Integrated monitoring: Built-in DQ checks help surface issues in the same interface where users search for data.

• Crowdsourced context: Users can flag problems, add documentation, or verify definitions—all within the catalog. Automation at scale: Catalogs can trigger workflows when issues are detected, streamlining remediation.

By embedding data quality into the data discovery experience, catalogs turn quality management into a daily practice—not an afterthought.

Conclusion: Embedding data quality for long-term success

Data quality is not a one-time project. It’s an evolving program that must keep pace with a changing data landscape. Organizations that embed data quality into everyday workflows—using smart tools, shared ownership, and continuous monitoring—will gain a competitive edge.

Trusted data is the foundation for everything: confident decisions, successful AI adoption, and enduring customer relationships. Get data quality right, and the rest will follow.

Learn how Alation is bringing the power of AI to data quality challenges.