The final step in a data analysis process is monitoring and validation. After decisions have been put into play and allowed a short time to work, it’s important to go back and check to see if outcomes are as expected.
Monitoring and validating results can take many forms For example, summary reports and simple charts of actual versus targets or average revenue or sales over time.
The goal is to make sure results are as expected. Otherwise, review any assumptions, check for errors in the data feeds or any unexpected changes to data attributes. Look to see if something changed in the market in an unexpected way.
By continually monitoring and going through the above data analysis process steps, problems can be detected early on and corrected before decision-makers find themselves trying to understand non-sensical outputs, or worse, the entire project is branded a disappointing failure. With a good process in place finding and fixing issues will be routine—and with a good complement of software tools, quality and assurance can be built into the system.