How can you evaluate data accuracy for team decision-making?

1 Define data quality criteria

Before you start collecting, analyzing, or presenting data, you need to define what data quality means for your specific decision-making context and purpose. Data quality criteria are the standards or rules that you use to assess the accuracy, completeness, timeliness, relevance, and consistency of data. For example, you might want to check if the data source is credible, if the data collection method is appropriate, if the data is updated regularly, if the data matches your decision criteria, and if the data is compatible with other data sources.

2 Use data validation techniques

Data validation is the process of checking and verifying that the data meets the data quality criteria. There are different types of data validation techniques that you can use, depending on the nature and format of the data. For example, you can use syntax checks to ensure that the data follows the correct format and structure, range checks to ensure that the data values are within the expected limits, consistency checks to ensure that the data does not contradict other data sources, and logic checks to ensure that the data makes sense and does not contain errors or anomalies.

3 Compare and contrast data sources

Another way to evaluate data accuracy is to compare and contrast different data sources that provide information on the same or related topics. This can help you identify and resolve any discrepancies, gaps, or conflicts in the data, as well as gain a broader and deeper perspective on the issue or problem. For example, you can compare and contrast data from primary and secondary sources, qualitative and quantitative sources, internal and external sources, and historical and current sources.

4 Seek feedback and input from others

Evaluating data accuracy is not a solo task. You need to seek feedback and input from others who have relevant expertise, experience, or stake in the decision-making process. They can help you validate, verify, or challenge the data, as well as provide additional insights, perspectives, or suggestions. For example, you can seek feedback and input from your team members, colleagues, managers, customers, partners, or external experts.

6 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?