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Data quality isn’t an abstract problem that should sit somewhere down the line on your business roadmap. It is an immediate problem for your business and it is costing you daily. 

Hidden hazards of poor data quality

When your data quality drops, you expose yourself to a host of problems resulting in poor insights and bad decisions, which then lead to subpar commercial performance.  

Flawed Decision-Making: Inaccurate or incomplete data can lead to misguided business strategies and poor decision-making, as executives rely on data-driven insights to understand market trends, customer behavior, and operational performance.

Operational Inefficiencies and Increased Costs: Poor data quality can cause redundant processes, transaction errors, and delayed decision-making, ultimately hindering productivity and driving up costs.

Regulatory Risks and Compliance Challenges: In industries like finance, healthcare, and manufacturing, data accuracy is crucial for regulatory compliance. Inaccurate data can result in costly violations, financial losses, and reputational damage.

Eroding Customer Trust: Inaccurate or outdated customer data can damage relationships and erode trust. For instance, sending targeted promotions to incorrect addresses or contacting unsubscribed customers can lead to customer alienation.

What you can do to improve data quality

Data hygiene shares a lot of principles with regular hygiene. Do you let your food and dishes be handled by anyone without supervision? Would you let someone from the outside walk into your house without cleaning their feet? Do you not clean your house regularly? Similarly, you need to ensure the hygiene of your data with some regular maintenance and good policies.

Data Validation and Verification

• Validate data at entry points by ensuring that data is accurate and complete when it’s collected.
• Use data verification processes like checking data against trusted sources or benchmarks to detect errors or inconsistencies.

Data Governance and Management

• Establish a data governance framework by defining policies, procedures, and standards for data management, security, and compliance.
• Assign data ownership and stewardship so there are clearly defined roles and responsibilities for data management and quality.

Data Cleansing and Maintenance

• Schedule maintenance to clean and update data, identify and correct errors, remove duplicates, and update outdated information.
• Keeping track of your data quality over time can help you gauge improvements in data quality over time.