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Raw data problems

Cleaning and preparing data is always the most difficult part of an analysis.

There are so many ways that data can be messy. Here are just a few:

  • Don’t rely on a “first order date” column in the raw data. Just calculate it using MIN()
  • Different business units
  • Transactions that are returns but not marked as negative numbers
  • How do columns in raw data relate to each other? one-to-one? one-to-many?
  • Duplicated id’s
  • Duplicated rows - same as above
  • Transaction detail inserted into same table as rolled-up transaction info
  • Yearly contracts sometimes need to be split into 12 monthly transactions for analysis purposes
  • Outsized transaction amounts, much higher than should be possible
  • Dual key tables
  • Lack of proper id’s
  • Missing data
  • Slightly “off” data that can be merged/corrected
  • Product names change over time, causing errors of confusion in the data, making it look like customes switched products but did not
  • Product bundles change over time, making it look like customers changed plans significantly but it was the company’s change, not the customer’s.
  • Data structures change over time - e.g. they used to lump up-sell revenue in with primary products now they split it out into another table
  • Currency is in multiple foreign denominations
  • Trial customers are sometimes counted, sometimes not counted in outputs
  • The transaction files are roll-ups. They are not actual transactions on a timeline.
  • Some rows missing data in required columns