What are the best ways to prioritize data quality in marketing analytics?

1 Assess your data sources

Before you collect, analyze, or report on any data, you need to know where it comes from, how it is generated, and what it represents. Assess the quality and relevance of your data sources by checking their credibility, timeliness, completeness, consistency, and accuracy. Avoid using data that is outdated, incomplete, inconsistent, or inaccurate, as it will compromise your analysis and results. Use data quality tools and metrics to monitor and evaluate your data sources regularly.

3 Validate and clean your data

Even if you have high-quality data sources and standards, you still need to validate and clean your data before you use it for analysis. Data validation and cleaning involve checking, correcting, and removing any data errors, anomalies, or outliers that might affect your analysis and results. Data validation and cleaning can be done manually or automatically, depending on the volume and complexity of your data. Use data validation and cleaning tools and techniques to ensure your data is accurate, complete, and consistent.

4 Document and share your data

Data documentation and sharing are important for data quality, as they help you and others understand, access, and use your data effectively. Data documentation and sharing include creating and maintaining data dictionaries, metadata, codebooks, and other records that describe your data sources, standards, methods, and results. Data documentation and sharing also involve using secure and appropriate platforms and formats to store and distribute your data. Data documentation and sharing help you ensure data transparency, traceability, and usability.

5 Review and improve your data quality

Data quality is not a one-time task, but an ongoing process that requires constant review and improvement. Data quality can change over time due to various factors, such as changes in data sources, standards, methods, or requirements. Review your data quality periodically by conducting audits, tests, feedback, and evaluations. Identify and address any data quality issues or gaps that might arise. Implement data quality improvement plans and actions to enhance your data quality continuously.

6 Learn from your data quality

Data quality is not only a means to an end, but also an end in itself. Data quality can provide valuable insights and lessons for your marketing analytics practice and performance. Learn from your data quality by measuring and reporting on your data quality indicators, such as accuracy, completeness, consistency, timeliness, and relevance. Analyze and interpret your data quality results and trends, and compare them with your data quality goals and benchmarks. Use your data quality findings and feedback to inform and improve your marketing analytics strategy and tactics.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?