What should be the first step when data project numbers do not match expectations?

The first step when data project numbers do not match expectations should be to verify your data set. This is essential because discrepancies can often arise from issues such as data entry errors, missing values, incorrect data types, or problems stemming from how the data was collected or processed. By verifying the data set, you ensure that the foundation upon which any analysis or interpretation is built is solid and trustworthy.

Checking for data quality helps identify whether the numbers you are working with are accurate and reliable. If the data set contains errors, adjustments can be made prior to any further analysis or recalculation of figures. This methodical approach saves time and effort since recalculating with faulty data or seeking new methods based on incorrect premises will not yield reliable results. Understanding the integrity of the data is a key step in any analytic process before drawing conclusions or making decisions based on it.