Start with the unit of observation.
What does one row represent: a registered company, a business location, an interview or an economic observation? Those units answer different questions. Counting locations may help plan distribution; counting companies may help map an industry. Treating them as interchangeable can distort an otherwise accurate dataset.
Keep time in the picture.
An observation date, reporting period and publication date can all be different. A recently published file may describe an older period. Keep those dates separate and decide which one matters to the decision you want to make.
Ask what the data cannot see.
Every source has boundaries. A location dataset may cover some cities more closely than others. A survey may represent its sampled population, not everyone in a region. Document those limits before making comparisons.
Make context part of delivery.
Request definitions, units, geographic boundaries and a plain-language methodology alongside the records. These are not extras: they are what allow another person to interpret the same file consistently.