How is AI changing data quality management in 2026?

AI is only as good as the data it learns from—and that's raising the stakes for data quality management in a big way. As organizations lean more heavily on AI to drive decisions, automate workflows, and surface insights, the margin for error in underlying data has never been smaller. Garbage in, garbage out has always been true, but AI amplifies that reality at scale. This shift is pushing data quality from a reactive or manual task to a real-time, proactive discipline. Tools like DemandTools have helped organizations stay ahead of data issues.

Data Quality

How to Take Control of Data Quality Management

Q: What is data quality management?

Data quality management (DQM) is an ongoing organizational discipline that ensures data is accurate, consistent, reliable, and complete to support ongoing business decisions. DQM aims to keep your data clean and trustworthy so you can use it properly. It ensures that the data is correct and standardized before launching marketing campaigns or making other important organizational decisions. DQM combines technology (like DemandTools), processes, and people to ensure data integrity is maintained across an organization.

Q: Why is data quality management important?

Data quality management is important because data is at the core of every organization. Poor data leads to significant issues that can impact revenue, compliance, and competition. Having clean data is essential to making the best business decisions, creating personalized marketing campaigns, and meeting quality standards. An organization with mature data quality practices has a competitive advantage because it can make decisions faster and leverage data for higher customer satisfaction and resource allocation.

Q: How do you implement data quality management?

It is important to first conduct a data quality audit to identify key issues your organization may be facing with data. Once that audit is complete and you identify key areas of focus, you need to define data quality rules and definitions across your organization. Once defined, and data ownership and accountability are taken, you want to appoint data stewards for critical data domains to ensure data quality and entry standards are met. Once these governance foundations are in place, implement technology tools like DemandTools, which are at the heart of implementing ongoing data quality management for your organization. DemandTools automates data quality to help you clean data quickly and ensure it stays clean. Implementation of DemandTools only takes about 10 minutes.

Q: What are the main challenges in data quality management?

The biggest challenge of data quality management is dealing with inconsistent, inaccurate, incomplete, and duplicate data. These issues often come from not following data entry best practices and poor data standardization across an organization. Inadequate data entry or a lack of standardization can cause data quality issues, inefficient data management, inadequate ownership, and data silos. All of these issues impact data quality and overall organizational efficiencies.

By: Olivia Hinkle, Director of Product Marketing

on March 27, 2026

minute read

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Your data is only as powerful as it is trustworthy—especially in the age of AI.

Data quality management is the collection of practices, tools, and processes that ensure your data is accurate, complete, consistent, and reliable. It covers everything from profiling and cleansing to governance, validation, enrichment, and ongoing monitoring. But data quality management isn’t just a technical exercise—it’s what gives your organization a foundation it can actually build on.

At the heart of data quality management are six core elements: accuracy, completeness, consistency, timeliness, uniqueness, and validity. Together, these qualities determine whether your data can be trusted to drive business decisions, or whether it’s creating problems you haven’t caught yet.

Unfortunately, poor data quality in a customer relationship management system (CRM) doesn’t stay contained—it ripples outward, skewing how you segment customers, slowing operations, and corrupting the reports your team relies on. When you’re working at scale, that’s not just an inconvenience. It’s a competitive liability.

The goal of data quality management is simple: prevent data quality issues before they cause decision-making or compliance failures. Because clean data isn’t a nice-to-have— it’s the difference between a system that works for your business and one that works against it.

Key Data Quality Management Trends and Insights
How CRM data quality issues happen
Why data quality issues are hard to detect
The business impact of poor quality data
Five key data quality metrics to monitor
How to build a strong data quality management strategy
Top data quality management FAQ’s
Timeline for implementing a data quality management program
Why trust us for data quality management?
Data cleanliness starts here

Key data quality management trends and insights

High-quality data is essential for accurate reporting, smooth operations, strong customer relationships, and successful AI deployment. In 2026, poor data quality doesn’t just hurt decisions—it undermines your entire AI strategy.
Many organizations overestimate their AI readiness. Data quality problems often develop silently in CRM systems, and these hidden issues can undermine both day-to-day decision-making and AI initiatives long before they’re finally noticed.
Tracking the right metrics helps you detect and fix problems early, ensuring your data is AI-ready. Monitor accuracy, completeness, consistency, relevancy, and timeliness to protect data quality and enable confident AI adoption.
A clear CRM data management strategy and the right tools can help you maintain reliable, actionable, AI-ready data at scale. Proactive automation is now essential—reactive cleanup is no longer sufficient in the age of AI.

How CRM data quality issues happen

Your CRM is designed to be the single source of truth for your customer information. However, CRMs are fed by multiple data streams. Sources like imports, integrations, manual entry, and automated capture introduce new variables and risks into the system. Errors can happen through:

Duplicate record entry: When the same customer is entered more than once under different details
Retaining outdated information: Lack of revision and ongoing verification when customers change numbers, swich jobs, or update email addresses
Manual entry mistakes: Misspellings, typos, and selection of wrong options
Integration conflicts: Different systems push conflicting data into the CRM leading to data corruption
Incomplete data capture: Missing key fields during imports of form submissions

Why data quality issues are hard to detect

Data quality issues can be sneaky—and often develop slowly. One problem here and there won’t be obvious at first. But the more problems pile up, the faster the cracks are revealed. Additionally, the fast-growing volume of data exacerbates the data quality issue further. Analyzing all that data is a major obstacle—CDO Trends notes that 57 percent of data practitioners say maintaining data quality is their biggest challenge during data analysis.

Data quality problems hide in plain sight due to:

Isolated errors: A single incorrect record can go unnoticed until it impacts an important report or a customer interaction.
Inconsistent standards: Different teams often follow different data entry rules. When everyone uses their own standards, it’s harder to spot errors.
“Good enough” checks: If a dataset looks fine with a once-over, teams might assume it’s reliable without deeper checks.
Volume overload: Large datasets make manual review impractical. If you lack the tools to review them, problems will sneak in.
Lack of clear ownership: A lack of data ownership results in poor data quality and fragmented practices. Without accountability, users can create “data islands” outside the CRM, undermining system integrity and allowing data issues to go unchecked.

The business impact of poor quality data

Poor CRM data quality can severely impact your customer and marketing-related decisions and services. Sales reps may waste time reaching out to outdated contacts, while analytics forecasts and performance breakdowns are skewed, leading to misinformed strategies. Teams relying on flawed insights and data simply can’t operate at their best. According to Validity’s State of CRM Data Management in 2025 report, 37 percent of teams report losing revenue as a direct consequence of poor data quality.

Beyond the time spent chasing the wrong leads, poor data quality can impact your relationships with customers. Missing or incorrect customer can lead to slow service, broken personalization attempts, and potentially awkward interactions (for example, contacting a current customer as if they were a new lead). Your business could miss out on sales opportunities or even lose customers. When businesses can’t trust their data, the competition wins.

Five key data quality metrics to monitor

Data quality is one of the building blocks of a robust CRM strategy. To effectively oversee data quality, you need to track the metrics that matter. These five metrics give you insight into how practical, trustworthy, and actionable your data is and show you where to improve.

Accuracy: Accurate data reflects the real world. It’s current, correct, and error-free. When you maintain accuracy, your reports are reliable, outreach hits the mark, and internal decisions are fact-based. For your company, this means fewer failed communications, better forecasting, and increased trust in your CRM.
Completeness: Missing data leads to incomplete insights. If key fields like customer phone numbers are blank, your marketing, analytics, and operations teams suffer. According to Validity’s 2024 State of CRM Data Management report, 68 percent of organizations struggle with incomplete data.Focusing on completeness helps your team make informed decisions, automate more processes, and unlock the full value of your data assets.
Consistency: When data is consistent, it reduces the risk of confusion, duplication, and inefficiencies. Validity’s data deduplication resources can easily pinpoint duplicate records and fuzzy matches to keep your CRM spotless.
Relevancy: Is the data you’re collecting still serving your business needs? Customer preferences, product usage, or engagement behavior may have been relevant when first captured, but that can change. Irrelevant data clutters systems, dilutes insights, and wastes resources. Prioritizing relevant data ensures the information you collect, store, and analyze aligns with your current goals, strategies, and customer needs.
Timeliness: Things change quickly. People leave jobs, companies merge, and your data becomes outdated. According to Validity’s 2024 report, 48 percent of admins have noticed accelerated customer data decay—making automated, timely data maintenance critical for preventing wasted marketing spend on outdated contacts and ensuring accurate customer segmentation.

How to build a data quality management strategy

A strong data quality strategy starts with clearly defined standards:

Assign ownership and a data governance strategy: Define the individuals or teams responsible for maintaining data quality.
Measure what matters: Track key data quality metrics to spot issues early.
Automate routine tasks: Use secure data quality management tools to update, monitor, and clean data efficiently.
Audit regularly: Review your data and processes to keep everything on track.

Data quality management in action: Real customer results

The business case for data quality management is clear — but what does it look like in practice? Here’s how three organizations used DemandTools to take control of their Salesforce data.

BARBRI: Merging 6,000 duplicate rercords a month—automatically

System integrations were flooding BARBRI’s Salesforce with duplicates, causing reps to work the same prospects multiple times. DemandTools automated the entire cleansing process, turning what once took days into minutes—and giving the team back time to focus on sales enablement instead. Read the full case study →

Thornburg Investment Management: 120 hours saved every week

Thornburg was battling a constant stream of duplicates and unstandardized data from third-party sources. After implementing DemandTools, they recovered 120 hours of manual data management time per week—the equivalent of adding a developer to the team without hiring one. Read the full case study →

908 Devices: Making data quality a competitive advantage

At 908 Devices, Salesforce data feeds everything from daily sales activity to executive decision-making in Tableau. DemandTools became the backbone of every data project—handling deduplication, standardization, and record management at scale so the team could move fast without sacrificing accuracy. Read the full case study →

Top data quality management FAQ’s

Still have questions about data quality management—or looking to bring a colleague up to speed? We’ve answered the most common questions below:

What is data quality management?

Data quality management is an ongoing organizational discipline that ensures data is accurate, consistent, reliable, and complete. It combines technology (like DemandTools), processes, and people to maintain data integrity across an organization—so that when it’s time to launch a campaign or make a critical business decision, your data is clean, standardized, and ready to use.

Why is data quality management important?

Data is at the core of every business decision. Poor data leads to real consequences: lost revenue, compliance failures, and poor decision-making. Organizations with mature data quality practices have a competitive advantage—they move faster, serve customers better, and allocate resources more effectively.

How do you implement data quality management?

Start with a data quality audit to surface your biggest issues, then define data quality rules and standards across your organization. Appoint data stewards to own critical data domains and enforce entry standards. From there, implement a tool like DemandTools to automate ongoing data quality—cleaning your data and keeping it clean.

What are the main challenges in data quality management?

The biggest challenges are inconsistent, inaccurate, incomplete, and duplicate data—typically caused by poor data hygeine practices and a lack of standardization. Left unaddressed, these issues compound into data silos, inefficient processes, and gaps in accountability that affect the entire organization.

How much does poor data quality cost businesses?

More than most organizations realize. According to Validity’s State of CRM Data Management in 2025 report, 37 percent of CRM users reported losing revenue directly because of poor data quality. Direct costs include wasted marketing spend on invalid contacts and data compliance fines. Harder-to-quantify costs include lost revenue opportunities, flawed decision-making, failed AI projects, and customer churn from irrelevant communications.

How is AI changing data quality management?

AI is only as good as the data it learns from—and that’s raising the stakes considerably. As organizations rely more on AI to drive decisions and automate workflows, the margin for error in underlying data shrinks. “Garbage in, garbage out” has always been true, but AI amplifies the consequences at scale. This shift is pushing data quality from a reactive, manual task to a real-time, proactive discipline—one where tools like DemandTools help organizations stay ahead of problems before they surface.

Timeline for implementing a data quality management program

Everyone’s data quality journey is different, but here’s a general overview of what you can expect for most implementation situations.

Phase 1: Assessment & Requirements (Weeks 1-4)
- Week 1-2 data quality audit
- Week 3-4 define data standards & requirements
Phase 2: Tool Selection (Weeks 5-7)
- Week 5: Research platforms that fit your needs & requirements
- Week 6: Evaluate & test tools, ensuring you request demos and trials
- Week 7: Tool selection and contracting
Phase 3: Data Governance & Tool Implementation
- Weeks 8-9: Appoint data stewards and create data quality policies and procedures
- Weeks 10-12: Deploy technology by ensuring your new tool is implemented
- Week 13-14 Start initial data cleansing and new tool training
Phase 4: Data Monitoring & Ongoing Optimization (week 15+)
- Launch ongoing monitoring and automated quality checks for data
- Ensure regular data reviews are in place

Why trust us for data quality management?

Thousands of organizations rely on DemandTools from Validity to take control of their Salesforce data, and for good reason. For over two decades, we’ve helped businesses of all sizes clean, manage, and maintain high-quality data that drives better operations, more intelligent decisions, and stronger customer relationships.

DemandTools allows you to effortlessly clean, organize, and maintain your organization’s data. From eliminating duplicates to standardizing fields and preventing incorrect data from entering your system, we give you the control to manage data quality at scale.

Data cleanliness starts here.

Quality data is essential for efficient operations, accurate reporting, and better business outcomes. With DemandTools, you have everything you need to build and maintain a high-quality Salesforce database. DemandTools offers flexible, powerful ways to standardize fields, eliminate duplicates, update records, and prevent bad data from entering your system in the first place.

Get a personalized demo of DemandTools and see how you can protect your Salesforce data quality in less time.

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