From the course: Leading Teams Working with Data: Pitfalls and Best Practices
Completeness
From the course: Leading Teams Working with Data: Pitfalls and Best Practices
Completeness
- The most important thing to check for when you're evaluating work that's done as data modeling, or forecast, or analysis is completeness. You need to make sure that the work is complete. That means it is done and that it's not missing anything. And by missing things, I mean the data you've included isn't missing anything, there weren't gaps in the process as you went from data collection, and cleaning, and analysis, iteration and presentation, all of that followed the process and there were no gaps. Everything was done completely. And you need to make sure that the final implications include all attributes of the analysis that are relevant for decision makers and stakeholders. If something's missing from the final presentation, it's like it never happened either. There are many different ways to evaluate the deliverables of a data project or of the members of your team. The most important is to make sure that the work is complete.
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