Thursday, 26 October 2017

Technical Debt in Analytics

I was lucky enough to attend the Spark Summit Europe this week, held in the Convention Centre, Dublin - a really good venue.

One of the concepts that appeared in several presentations (to which, of course, Spark based solutions are a natural solution!) was the idea of Technical Debt. The image that accompanied all the presentations was taken from a paper entitled Hidden Technical Debt in Machine Learning Systems, a paper authored by several Google employees:


The concept is very familiar to me from many years of selling QlikView. The debt there arises from the famous SIB - Seeing Is Believing (or just plain-old Proof-of-Concept) where we would go into a prospect company, take some of their data, hack together an impressive dashboard, and wow them with how quickly we could work our magic with this wonderful tool.

The debt, of course, arose when the prospect turned into a customer and wanted the POC put into production!

Eh, er, em, perhaps, oh... - that difficult conversation where we have to explain exactly how much work is needed to make this wonderful dashboard actually production ready.

Technical Debt is not a new concept. It was described as far back as 1992 by Ward Cunningham, (founder of the famous Hillside Group,  developer of Wiki, and one of the original signatories to the Agile Manifesto). It is unsurprising to find it described in Machine Learning. The extent of it may be a bit of a surprise.

Taking on debt is something that a business may accept as it may lead to growth opportunity. However, the business needs to understand the terms of the debt before they agree to it. This Google paper is worth reading and understanding.

Businesses need to understand that implementing "AI" and "Machine Learning" may lead to gold, but the debts will need to be paid. You wouldn't jump into a finance agreement without consulting an adviser, don't jump into analytics without talking to someone who know what they are talking about.



As well as holding a Master's Degree in Data Analytics, Stephen Redmond is a practicing Data Professional of over 20 years experience. He is author of Mastering QlikView, QlikView Server and Publisher and the QlikView for Developer's Cookbook
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