Top Data Quality Best Practices Every Business Should Follow

We live in a data-driven world, and high-quality data is the fuel that powers smart decisions, streamlined operations, and competitive advantage.

When your data quality is poor - riddled with duplicates, inaccuracies, and outdated records - your business can be derailed. Flawed analytics, wasted resources, and compliance risks can all become unavoidable. IBM estimates that bad data costs businesses upwards of $3 trillion in the United States alone, making data quality best practices, data quality standards, and data quality checks non-negotiable when it comes to success.

Whether you’re a small business owner, a data analyst, or a C-suite leader, keeping your data accurate and reliable is critical to staying ahead. Explore data quality best practices that every business should follow to ensure data is a strategic asset.

Why Data Quality Matters

Before we take a look at the data quality best practices, let’s understand why they’re so essential.

High-quality data means your business can operate at its optimum while making informed decisions and meeting regulatory requirements. Poor data quality, on the other hand, can lead to: 

• Flawed Insights: Analytics are skewed by inaccurate data, like the over-reporting of sales or misidentifying customer trends
• Operational Inefficiencies: Duplicate records or missing data waste time and resources
• Compliance Risks: Inaccurate data can violate regulations like GDPR, which in turn can lead to fines
• Lost Opportunities: Messy data can hinder targeted marketing and strategic planning

10 Data Quality Best Practices

Roadmap your organisation’s data quality journey to implement effective data quality management. Each practice includes steps, examples, and real-world insights from our work.

1. Define Clear Data Quality Objectives

When you align data quality with your business goals, you set the stage for success. Define what quality means for your organisation: accuracy for reporting? Completeness for customer profiles? Timeliness for operations? Here’s how to do it:

• Identify your business objectives
• Set measurable goals, such as a certain level of accuracy in customer data
• Engage stakeholders for alignment

A major telecoms provider defined clear objectives around measuring proposition value, using our Customer Lifetime Value approach to uncover actionable customer insights. Through data auditing, they were able to baseline data quality and set realistic improvement targets.

2. Establish Data Quality Standards

Data quality standards ensure consistency across the board, defining rules for accuracy, completeness, consistency, timeliness, validity, and uniqueness. Here’s how to do it:

• Document standards for data types (e.g., dates in MM/DD/YYYY, valid for emails)
• Create a data dictionary for formats, ranges, and acceptable values
• Share your standards across the different teams for compliance

Our data quality dimensions guide provides templates for robust standards. 

3. Implement Robust Data Quality Checks

Regular data quality checks can catch common data quality issues like duplicates or missing values before they impact operations.

• Use profiling tools to identify anomalies, such as outliers or nulls
• Schedule monthly audits where you can track metrics like errors or duplicate rates
• Validate these against external data sources

Our analytics helped a financial services client identify 90,000 multiple account holders, ensuring accurate risk assessments. You can automate your checks with our data quality management guide for real-time insights.

4. Cleanse Data Regularly

When you cleanse your data properly, you can remove errors, duplicates, and inconsistencies, maintaining high quality across your databases. 

• Start by removing duplicates with tools like Excel’s “Remove Duplicates” or SQL
• Use functions like TRIM to eliminate any extra spaces
• Standardise formats with ETL processes

Our data cleansing services played a vital role for one client in migrating over 1 million customer accounts, giving them clean, reliable data post-migration. To keep your data pristine in the long-term, it’s a good idea to schedule regular cleansing activities.

5. Enrich Your Incomplete Data

Even clean data can be incomplete. And when data is incomplete, your insights are limited. Data enrichment adds missing attributes, like demographics, financial, or lifestyle insights.

• There might be gaps in your profiling, such as missing customer emails or ages, so these need to be identified
• Source the necessary data from reliable providers
• Append your data using automated tools

Organisations use our data enrichment services to enhance customer insight and segmentation with additional attributes, enabling more targeted marketing and improved campaign ROI.

6. Ensure Your Data is Consistent Across Systems

If your data isn’t uniform (say you have mismatched customer names across systems), you could end up with errors. Master data management to get that critical uniformity. Here’s how:

• Centralise your key data with suitable tools
• Compare your systems to spot any discrepancies
• Standardise the formats during integration

Businesses can use data leveraging to unify customer data, enhancing their digital marketing efficiency. Our data management platforms streamline consistency across systems. 

7. Maintain Timely Data Updates

Outdated data, like old addresses, can derail your operations. When you update your data regularly, you’ll know what you have is relevant and useful.

• Set update schedules (e.g., daily for inventory)
• Use real-time pipelines for critical data
• Monitor time-to-value metrics

8. Validate Data at Entry

Errors can be prevented by enforcing rules during the data collection process, like valid email formats and standardisations.

• Set validation rules (e.g., Excel’s Data Validation for ranges)
• Implement mandatory fields in your forms
• Train staff on entry protocols and business rules

9. Foster a Data Governance Culture

Data governance ensures accountability with policies and roles to sustain quality. To create and nurture this culture, you need to:

• Appoint data stewards to oversee your processes
• Create policies for data handling and updates
• Integrate quality checks into workflows and business as usual processes

10. Leveraged Advanced Tools and Automation

Manual processes don’t scale, but automation and AI can enhance the efficiency and accuracy. Here’s how:

• Use proven platforms for cleansing
• Implement AI and machine learning for anomaly detection
• Monitor with observability tools

Key Data Quality Checks to Implement

Effective data quality checks are the backbone of maintaining high standards, ensuring data remains reliable for decision-making. By routinely monitoring key dimensions, businesses can catch issues early and prevent costly errors. Include these checks in your strategy:

• Accuracy Check: Validate against trusted sources (e.g., postal databases)
• Completeness Check: Count any missing values
• Consistency Check: Make sure your data is uniform across your different systems
• Timeliness Check: Monitor your update frequency
• Validity Check: Verify the different formats, like valid email addresses
• Uniqueness Check: Use fuzzy matching to detect any duplicates

Our data segmentation work improves response rates and reduces acquisition costs by ensuring unique customer profiles through rigorous uniqueness checks. You can automate these checks with our data quality management tools for real-time monitoring and efficiency. 

Common Pitfalls to Avoid

Implementing these best practices requires careful planning in order to avoid the common mistakes that can derail your efforts. These pitfalls can undermine even the best-intentioned strategies, so it’s imperative to address them proactively:

• Unclear Goals: Make sure your objectives are specific and well-aligned
• Lack of Buy-In: All teams need to be on board
• Manual Processes: Automate these to make scalability feasible
• Irregular Audits: Checks need to be regular
• Perfectionism: Aim for practical quality levels

The Business Impact of Data Quality Best Practices

When you adopt data quality standards and checks, you can reap the transformative benefits that will drive your business’ success. High-quality data is an empowering tool that helps organisations operate more effectively while staying ahead of the competition.

• Smarter Decisions: Reliable analytics are fuelled by accurate data
• Better Customer Experience: Clean data enables you to personalise your customer offerings
• Cost Savings: With fewer errors, you can reduce rework
• Compliance: Meet GDPR and industry standards
• Competitive Edge: High-quality data drives innovation and puts you in a position to push ahead of your competitors

By refining customer segments through data enrichment and governance, Sagacity has helped organisations achieve stronger marketing ROI and substantial cost savings across sectors such as insurance, utilities, and financial services.

Practical Steps to Implement Best Practices

This structured approach will give you consistent, high-quality data across your organisation:

• Assess Data Quality: Profile your data with our data auditing services
• Define Standards: Document your rules for consistency
• Set Up Checks: You can automate these with Sagacity’s data quality management services
• Cleanse and Enrich: Use data cleansing and data enrichment
• Implement Governance: Assign stewards and policies
• Leverage Automation: Use AI and tools like Power Query
• Monitor Continuously: Refine your processes with audits

With these added steps, you can confidently implement your best practices and know that they will continue to benefit your business long-term.