How to Improve Data Quality: 7 Actionable Steps for 2025

Bad data quality doesn’t just slow you down; it can seriously hurt your business.

Poor data chips away at everything - your marketing results, sales forecasts, and business decisions. When you rely on it to guide key moves, the price is steep: wasted time, lost revenue, and damaged customer trust.

Even if your data starts great, it won’t stay that way long. On average, 70.3% of it goes out of date every year. Gartner’s study shows that some out-of-date and potentially inaccurate data costs businesses up to $15 million.

Fortunately, the right strategies and tools can help you improve data quality, reducing missed business opportunities and potential liabilities from data hygiene issues.

Why does data quality matter in B2B?

Poor data quality can have a significant knock-on effect on sales, marketing, and reporting teams.

Good data isn’t just IT’s problem; it’s what makes the whole business tick.

Here are some reasons why data quality matters:

• More accurate forecasting and better business processes.
• Higher conversion rates from marketing campaigns.
• Increased sales productivity and effectiveness.
• Enhanced customer satisfaction through personalisation.
• Reduced compliance risks and associated costs.
• More valuable insights from data analytics and AI initiatives.

That’s why having a solid data hygiene strategy matters. It keeps your info accurate, relevant, and ready to go.

Otherwise?

You’ll end up calling people who don’t want to hear from you (those on DNC lists) and chasing cold leads while the hot ones slip away.

Your segmentation, data pipeline management, and ability to scale personalised outreach can all be derailed without anyone realising it. When this happens, sales performance can drop quickly.

And now, additional stressors are making data quality more important than ever. Let’s discuss them next 👇

Artificial Intelligence (AI) amplifies existing data problems

With AI adoption accelerating across business functions, the quality of your data directly impacts AI performance.

Machine learning trained on bad data gives you bad insights. That just makes your existing problems worse.

Regulatory requirements continue to evolve

The regulatory landscape has grown more complex, with the Digital Services Act joining the GDPR and CCPA in demanding higher standards for data management.

Poor data hygiene can create compliance risks that few businesses can afford, especially since they can cost you customer trust and result in regulatory fines of up to 20 million euros.

Customer expectations continue to rise

Today, 71% of customers expect personalised experiences that demonstrate understanding of their needs and challenges.

And guess what? You can’t personalise anything without clean, accurate, up-to-date data.

How do you improve data quality?

While maintaining data accuracy is an ongoing effort, let’s review seven highly effective strategies to improve sales data quality.

1. Establish a data governance framework

A data governance framework defines how your organisation collects, manages, and uses data. It determines who owns the data, your data collection and storage standards, and accountability for things like legal compliance.

Instead of ad-hoc decision making, you follow established guidelines for every data operation.

This approach ensures compliance with laws like the GDPR and maintains consistent data quality. It includes automated systems that enforce your rules and regular checks to ensure everything stays on track.

For B2B companies, this means having proper procedures for handling prospect information and customer data. It builds trust by showing you take data security seriously and helps avoid costly compliance violations.

Recommended tools

These tools can help you establish and monitor a data governance framework:

• Collibra.
• Ataccama.

2. Perform regular data profiling and audits

Consistent, regular audits can help your team identify duplicates, flag anomalies, and discover data errors that need modifying.

Set up a regular schedule; we recommend monthly for your CRM and quarterly for your data warehouse. Focus on revenue-critical data: customer contact information, lead scoring fields, and account information.

If there are ongoing issues, look for patterns in what’s going wrong and why. For example, you may realise there’s a problem with duplicates or data silos happening across your tech stack.

For best results, we strongly recommend creating audit schedules for different systems, including your CRM, ERP, and DWH platforms.

Recommended tools

These tools can help you conduct data audits and profiling:

• Talend.
• Informatica.
• Data Ladder.

3. Implement data validation at entry points

Data validation prevents bad information from entering your systems. It’s far easier to stop problems before they happen than to fix them after the fact.

Keep in mind that users may enter bad data for any number of reasons. They may share a false email address because they want access to a lead magnet but don’t want to be contacted, or it could even be an accident.

Set up real-time data assessment checks on your forms; verify email formats, confirm domains exist, and ensure phone numbers match country patterns.

Add business-specific rules, too. If you only serve enterprise customers, validate that the company size meets your minimum.

The key is balancing accuracy with user experience. Overly strict validation frustrates users and reduces conversions, so getting the necessary information is essential while delivering a seamless customer experience.

Recommended tools

In addition to custom validation scripts, these tools can help you verify data on entry:

• FormAssembly.
• Validity.

4. Use data enrichment tools to fill gaps

Data enrichment automatically fills in missing information in your database using external sources.

Even with perfect collection processes, gaps are inevitable. Enrichment tools solve this without manual research. They can be part of platforms offering data as a service (DaaS).

These tools pull in firmographic data, like company size, industry, and revenue, based on a domain or name. They also enhance contact records with phone numbers, professional emails, and social profiles.

The best enrichment tools go beyond filling the gaps, however. They continuously update your data as circumstances change, which is particularly valuable for tracking job and company updates.

This can reduce manual research time while continually improving data completeness and accuracy.

Recommended tools

Many data providers can facilitate enrichment, ZoomInfo being one.

But the real standout in this space is Cognism. We offer:

• CSV enrichment: Upload your list, and Cognism instantly fills in missing fields with accurate, verified data.
• API enrichment: Auto-enrich leads directly in your CRM, sales tools, or workflows.
• Real-time updates: Get the latest job changes, mobile numbers, and company details with no lag.
• Scalable enrichment: Whether it’s hundreds or millions of records, process them all in minutes.
• Diamond Data® quality: GDPR-compliant, human-verified contacts for faster outreach.
• Seamless data integrations: Sync enriched data into your existing sales tech stack.

Take an interactive tour 👇

5. Standardise and normalise data

Consistent data formats save time and enable accurate cross-platform reporting.

Data standardisation ensures that your systems treat “VP of Marketing,” “Vice President, Marketing,” and “Marketing VP” as the same job title.

Without this, inconsistent formats create chaos in reporting, lead prioritisation, and automation.

How should you manage this type of data quality control? Follow these steps:

• Create mapping rules for common variations in job titles, company names, and other key fields.
• Ensure data formats are consistent across all your platforms so that reporting is seamless.
• For global businesses, account for international differences in naming conventions, address formats, and phone number structures.

Recommended tools

• OpenRefine.
• Dataedo. 
• Talend.

6. Automate duplicate detection and merging

There are plenty of reasons you can end up with duplicate customer records, including:

• Multiple SDRs reaching out to a single prospect.
• Multiple or shifting decision-makers at a single organisation.
• A single lead signs up for multiple lead magnets.

This clutters your CRM fast.

Luckily, the right tools can stop the sprawl and make sure you’re using the correct records. These tools allow you to automate merge logic based on priority fields.

Recommended tools

Tools that can help you identify and remove duplicate database errors include:

• Validity’s DemandTools.
• Insycle.

7. Train your teams on data quality best practices

Your data quality procedures and policies will only be effective if your team understands them.

You should provide training for all new staff and quarterly refreshers to ensure everyone is familiar with your data management policies. Make new training available as soon as your policies change.

Top tip:

Explain the impact bad data has on the business. Your team will follow your policies when they understand how failing to use accurate data could result in hefty fines or declining sales performance.

This can encourage a data-first culture across GTM teams, ensuring that data quality will be an ongoing priority for everyone.

Onboarding and training tools like Trainual can help you both facilitate and monitor employee learning.

FAQs about improving data quality

What is data quality, and why does it matter?

Data quality refers to the accuracy, completeness, consistency, and relevance of data.

It’s essential for marketing, sales, and product teams that want to take advantage of reliable insights. It’s also critical for performance optimisation and legal compliance purposes.

If your team operates on outdated data, you can easily find yourself targeting the wrong ICP, resulting in ineffective campaigns, less pipeline, and fewer closed deals. It could also result in lost customer trust and potential compliance issues.

What are the common causes of poor data quality?

The most common causes of poor data quality include:

• Manual input errors (we’re all human, after all!).
• System integration problems that prevent proper data transfers.
• Lack of governance and frameworks.
• Outdated records and no standard data hygiene protocols.

Is GDPR compliance linked to data quality?

Yes, GDPR compliance absolutely is linked to data quality.

The GDPR requires accurate and up-to-date data to ensure you’re contacting the right people with consent.

If some users have opted out or you’re relying on incorrect contact information, it can pose a problem. It may even increase your legal risk and lower user trust.

Click here to see how Cognism is fully GDPR and CCPA compliant.

How can tools help improve data quality?

Data quality tools can automate the most time-consuming aspects of maintaining clean data.

They can:

• Validate information in real-time as it’s entered.
• Automatically enrich incomplete records with additional context.
• Identify and merge duplicate entries with contact data cleansing.
• Continuously monitor your database for quality issues.
• Run audits to monitor data changes.

This kind of automation can improve accuracy at scale and save time.

How should you evaluate tools and solutions?

When selecting data quality tools, focus on solutions that align with your specific business needs and technical capabilities rather than trying to implement everything at once.

Prioritise key criteria

Look for tools that offer features like the following:

• Automated GDPR compliance features.
• Native integrations with your existing CRM and marketing platforms.
• Straightforward setup processes that don’t require extensive technical resources.
• Comprehensive audit logs for tracking changes.
• The ability to scale as your data volumes grow.

Understand different tool categories

The exact features you need will depend on your existing business and data governance processes.

• Data enrichment tools like Cognism or Clearbit fill gaps in your existing data.
• Cleaning and standardisation tools like Talend and OpenRefine help normalise inconsistent information.
• Validation tools like Validity prevent bad data from entering corporate systems.
• Governance platforms like Collibra provide oversight and policy management across your entire data ecosystem.

Think about what obstacles your team currently has, and start there.

Start strategically

Rather than implementing multiple tools simultaneously, start with one or two solutions that address your most pressing data quality challenges.

These might be enrichment tools if you have significant gaps in your prospect data, or deduplication tools if you’re struggling with multiple systems creating duplicate records.

A strategic start will ensure you’re first addressing your most significant challenges.

What is the best tool for improving data quality?

Cognism gives your sales team the fresh, verified data they need to hit quota faster.

With instant CSV and API enrichment, you can fill in missing fields, refresh stale records, and keep your CRM fully up to date, without wasting hours on manual admin.

And that means your team will work faster, sell smarter, and drive more revenue.