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Data Cleansing: Enforcing Data Quality
How many times have you opened your mailbox to find two or three identical pieces of advertising addressed to you from the same organization? Or a piece of mail addressed to someone that has moved from the address years ago. Those are examples of dirty data. You’ve been entered multiple times into their database and they haven’t realized that there are duplicate records.
Poor data quality isn’t a problem limited to bulk advertisers. It can affect any business and any category of software.
Data cleansing or scrubbing is an attempt to detect, and remove or correct problems with data. Data can go stale or change, be out of date, redundant, incomplete, be incorrectly formatted, or have a variety of other types of problems.
How to fix problems with data? Here are just a few tips:
– Don’t Trust Human Data Entry. Data entry is the point where most dirty data problems originate. Wherever possible force a fixed data format and use dropdowns with pre-populated data selections.
– Break it down. Parse data strings into smaller strings, looking for possible inconsistencies or inaccuracies.
– Eliminate Nonsense. Check for the reasonableness of content. Try to define the characteristics of a data field and devise potential checks as to its reasonableness.
– Eliminate Duplicates. Identify matches and eliminate the duplicates.
Improving data quality can save money, improve customer perception, provide better information for making business decisions, and help ensure compliance.
