Achieving data maturity (a mini-series)

Part 1: Clean data ≠ the solution

‘Data cleaning’ is a term or concept you will manage often as a data professional. In fact, the first step in data analysis is cleaning dirty data — nulls, missing values, duplicates, etc.

As I mature as a data analyst, I believe that dirty data will not always be the primary issue. What happens when you have the perfect dataset? (not sure it exists, but a girl can dream.)

There is a lot of talk around “data maturity”, building systems, dashboards, and pipelines, all very necessary, but they don’t mean much if the questions coming in are vague, shifting, or built on assumptions.

For beginners, data cleaning is the foundation of analytics. As you grow, process cleaning becomes your kryptonite. You still clean data, but maturing is realizing that you should clean requests.

Every analyst I know spends time cleaning datasets, debugging dashboards, automating reports, or tracking down someone who renamed a column from “User ID” to “UID_20…

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