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I've always disliked the term data cleaning for the reasons you mention - it doesn't tell me anything about what is meant by "cleaning".
by throw_away_777 10y ago
I've always disliked the term data cleaning for the reasons you mention - it doesn't tell me anything about what is meant by "cleaning".
- mcrad 10y agoBesides, cleaning implies some entry level gig, something for a QA hack not someone experienced in complex systems. Marketing types love this kinda twist as a way to maintain control (and ensuring failure) of the project. It's like a scapegoat. Just got out of meeting where marketing claims that data "hygiene" is gonna be a priority in 2017.
- zgramana 10y agoI have spent a lot of time talking with customers/prospects about this topic, but I use "data remediation" which I feel brings more accurate and precise connotations, to wit: * Implies that the data is deficient/falls short of expectations. * Implies that the shortcoming currently makes it ineligible to graduate to the next level. * Implies that with hard work and additional time likely it can be made sufficient though still not ideal. * Implies that someone failed to help the data to meet expectations. * Implies that you need special outside expertise, namely someone with the knowledge needed to assess the shortfall, possibly help you clarify your standards, design steps that when followed should result in "good enough" data, and who is able to articulate the remaining weakness(es) which need to be accounted when assessing future suitably of that dataset for a given purpose. * Implies that your data will be stuck in school all summer while their friends are out having so much fun.