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Friday, July 13, 2018

Analytics Data Catalogs, Approaches, not new

Yes, we know this, and just because some call it AI, does not mean we won't have to gather the data consistently and continually to solve real problems. 

Analytics Industrial Revolution- From The Occult to the Ordinary
By  Snehamoy (Sneh) Mukherjee In Linkedin

Senior Director - Delivering Data Science, Big Data, Machine Learning and Analytics projects for Fortune 500 companies

There is a quiet revolution taking place in the Analytics industry that has the potential to completely turn the industry on its head and the way work gets done in this space. Doomsday pundits have already summoned the evil spirit called AI to put an end to the misery of our uneven paychecks, to be replaced by an Universal Basic Income and some of us have reconciled ourselves to that cruel fate. But before the Apocalypse happens, there is another subtle and continuous tectonic movement happening right under our feet, which if gone unnoticed for long, can catch us in a tidal wave of upheaval in the analytics /machine learning industry.

The typical Analytics (often very eruditely rechristened by brilliant marketers and/or the academia as Machine Learning and a lot would break their heads to prove that the two are different) or a Machine Learning project gets delivered in the following atypical manner in most firms: -

·        Data is pulled from one/multiple tables from a database(s) (by someone who may either be from the client side or by the analytics vendor). It may be a onetime data extraction or if data is needed on a periodic basis, an ETL (Extract Transfer Load or in some cases ELT) process is created to do a batch fetching and processing of files (e.g. weekly transaction data from retail stores, monthly/weekly call data in telecom firms, weekly/daily transaction data in banks) .... " 

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