How to Take Control of Data Quality Management

At Validity, we like to call data “the good four-letter word.”

It’s probably easy to see why—CRM data is the backbone of business success. But here’s the catch: your data is only as powerful as it is trustworthy.

Data quality means having information you can rely on—information that’s accurate, consistent, and aligned across your systems. Poor data quality in your customer relationship management (CRM) system can create inaccurate reporting, slow operations, or skew how your team segments and serves customers. Especially when working with large volumes of data, maintaining quality isn’t optional—it’s key to efficiency, responsiveness, and competitiveness.

Table of contents

1. Why data quality matters more than ever
2. How CRM data quality issues happen
3. Why data quality issues are hard to detect
4. The business impact of poor quality data
5. Five key data quality metrics to monitor
6. How to build a strong data quality management strategy
7. Data cleanliness starts here

Key takeaways

• High-quality data is essential for accurate reporting, smooth operations, and strong customer relationships.
• Data quality problems often develop silently in CRM systems. These hidden issues can undermine decision-making long before they’re finally noticed.
• Tracking the right metrics helps you detect and fix problems early. Monitor accuracy, completeness, consistency, relevancy, and timeliness to protect your data quality.
• A clear CRM data management strategy and the right tools can help you maintain reliable, actionable data at scale.

How CRM data quality issues happen

Your CRM system 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• Retaining outdated information: Lack of revision and ongoing verification when customers change numbers, move, or update email addresses
• Manual entry mistakes:  Misspellings, typos, and selection of wrong options
• Integration conflicts: Different systems push conflicting data into the CRM or experience integration issues 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 you data to make decisions that don’t pan out, 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% 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 works differently, 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 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 calling outdated numbers, while analytics forecasts and performance reviews 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% 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 might miss out on sales opportunities or even lose customers. When businesses can’t trust their data, it becomes harder to compete in a market full of companies that use data accurately.

Five key data quality metrics to monitor

Data quality is one of the building blocks of a robust customer relationship management (CRM) data management 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 more 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 or order dates are blank, your marketing, analytics, and operations teams suffer. 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 across systems, like CRM and billing, it reduces the risk of confusion, duplication, and inefficiencies. Consistent data streamlines processes across departments, improves integration efforts, enables unified reporting, and creates a single source of truth that translates to better operational efficiency, collaboration, and decision-making.
• 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: For time-sensitive decisions like fraud detection, inventory updates, or lead follow-up, timely data supports responsive operations and enhances the customer experience. Teams can act quickly—that could mean restocking items before they run out, contacting leads while they’re interested, or resolving issues before they escalate for better customer service.

How to build a data quality management strategy

A strong data quality strategy starts with clearly defined standards—what “good” data looks like for your organization. Then, move on to creating your strategy:

• Assign ownership: 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.
• Govern your data: Establish a responsible governance team that owns data quality processes, controls, and monitoring

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 quality data in less time.