Poor data quality costs companies a lot of money. Yet data provides valuable information for every business. Every click, every transaction, and every customer interaction generates data. This data provides insight into the needs, preferences, and behaviours of existing customers, especially.
Analysing this information helps improve processes, save costs, and optimise the customer experience. However, this analysis depends on the quality of the data. The better the data, the more reliable and actionable the insights. Ensuring data quality requires time, human resources, policies, and tools. A systematic approach helps obtain accurate, reliable, and consistent information. Companies faced with the question of how to improve data quality should consider the following nine tips.
1. Data quality: Guidelines for data management
Responsibilities for data management should be clearly defined and roles assigned. This approach helps companies monitor that the collected data meets established standards. Standardising data entry and maintenance procedures helps ensure consistency and reduce errors. A well-structured data governance framework should define who enters new data, updates records, approves changes, and monitors quality.
2. Data validation at input points
Preventing errors at the source is easier than correcting them later. Companies achieve this by applying input validation techniques at data entry points. Users are then required to fill in mandatory fields, and the format of the data and numbers is pre-configured and checked. Reference lists for validation are just as helpful as quality controls during data entry or updating.
3. Regularly clean and remove duplicates
Strict quality controls are an important step. However, data redundancies or compromises can still occur. Using data cleansing tools to identify errors, typos, inconsistencies, and redundant data records can help. Scheduling regular maintenance work, such as quarterly maintenance by internal teams or external specialists, ensures that data quality is maintained even as data volumes grow.
4. Data profiling and monitoring tools
Data profiling and monitoring tools identify patterns, outliers, and anomalies, uncovering potential quality issues such as blank fields, out-of-range values, and format inconsistencies. Monitoring tools help identify quality fluctuations and enable automatic detection and rapid remediation.
5. Standardise data formats and definitions
Companies should create a comprehensive data dictionary that defines each data element and the required formats (e.g. MM/DD/YYYY for dates). They should also establish consistent formatting rules across departments, such as standardised name fields with correct capitalisation, to ensure consistency.
6. Data quality: Introduce master data management
Business-critical data such as customer, product, and supplier records should have a central single source of truth. Master data management (MDM) creates this single data system for the most important reference information. Companies should establish processes for regularly updating and maintaining master data. For example, specific employees can be assigned to review master data weekly for errors or changes.
7. Conduct regular data quality audits
Conducting regular audits is essential to assess the health of the data. Automated tools look for anomalies, while manual reviews confirm accuracy. Documenting all issues found facilitates follow-up, ensures bug fixes, and improves processes.
8. Employee training on best practices
Training on quality policies should be both comprehensive and regular. It should explain the impact of data quality on the organisation and the individual’s responsibility for maintaining high standards. Regular training increases understanding of policies, strengthens governance, and enables better data capture practices. All of this ensures that data quality is maintained throughout the organisation.
9. Data quality: Use of AI and machine learning
Advanced AI-powered tools can help analyse large amounts of data by leveraging machine learning algorithms to identify patterns and anomalies that may be due to compromised data quality. Over time, the tools become more accurate as they are trained on more and more data. Potential issues can be flagged so employees can quickly validate and resolve them.
Maintaining high data quality standards is an ongoing process that requires continuous adaptation of strategies. Companies must establish transparent governance, implement validation tools, conduct regular audits, and train employees in best practices. Most importantly, quality management must evolve with the growing data and deploy the right solution.
