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Quality Assurance for Data & Analytics Projects

Updated: Feb 11, 2021

  1. Check if your results are sensible: Does your data, insight, or graph makes sense? Based on what you know about your data and/or business do the trends/volumes/insights make sense?

  2. Spot-check any calculated metrics/ fields: Manually calculate any calculated or aggregated visualizations, KPIs and/or fields and check that your manual calculations match any automated process.

  3. Match your data against other sources: Check if a different system or data source matches the results of your analysis or the output of your dashboards. Depending on the source of data you're comparing against, you may be looking for an exact match or a directional one.

  4. Check your final product against the initial requirements: It's always a good idea to go back and check that your work matches what you set out to do.

  5. Have a set of fresh eyes perform the final check: After the project is completed it's very important that someone new to the project reviews the finished product.

  6. Perform "User Acceptance Testing" (UAT): Give a preview of the final product to a small group of your end-users. This will allow you to identify any gaps or confusing elements in your product before you distribute it broadly.

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