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Is everyone’s data a mess?
Recently, I came across a post in the data engineering subreddit that asked the question.
The answer is yes, but no.
As someone who has seen data infrastructure at FAANGs, Enterprises, start-ups, and every other company in between, all companies need to make some concessions that can build up and become messy over a long period of time.
So let’s discuss some of the causes of data infrastructure becoming messy and how some companies are trying to deal with it.
Also, I forgot to cover a very important topic!
That is all of the mess often starts at the data source.
You can read the fuller version of this topic here
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About me:
I have spent my career focused on all forms of data. I have focused on developing algorithms to detect fraud, reduce patient readmission and redesign insurance provider policy to help reduce the overall cost of healthcare. I have also helped develop analytics for marketing and IT operations in order to optimize limited resources such as employees and budget. I privately consult on data science and engineering problems both solo as well as with a company called Acheron Analytics. I have experience both working hands-on with technical problems as well as helping leadership teams develop strategies to maximize their data.
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