EVERYTHING BIG DATA WAS AND EVERYTHING IT WILL EVER BECOME
In Data Teams, author Jesse Anderson writes from a tipping point in time: Anderson describes a technological discipline that has matured enough to have explicit criteria for its ideal setup. However to really perform, this discipline is also to be practiced in a technological reality that, at the point of writing, is losing its momentum; The on premises data centers.
The technological discipline is big data, and the tipping point from which Anderson writes about big data is 2020, the publication year of Data Teams. The decade that had come to an end was the ten golden years for big data.
As the big chief of big data, Anderson draws the lessons learned from working with the best and biggest in big data — all of the leading industries in the world. Delicately, Anderson does not detail the preferred bits and bolts of the big data distributed systems technologies. Instead, Anderson reveals the insights into the acquired taste of the organizational dimension of big data that he calls data teams. It’s an acquired taste because it takes realization — even disappointment — to understand that the initial fuzz of hiring data scientists is not enough to succeed with big data.
Looking back, Andersons incomparable experience testifies that what makes or breaks a big data ambition is the ability for a company to organize around not a team of data scientists, but around three teams, namely: The Data Science Team, The Data Engineering Team and the Operations Team.
The purpose of those teams and the roles that should staff them is not something that most industries understand, hence Andersons purpose of writing the book. Technology, it seems, is bought eagerly, but the willingness to buy the human capacity to run it, takes far more subtle persuasion, by the few that have the experience to actually explain it.
Extracting the best practices of the lessons learned from over a decade, Anderson points not only towards the past by explaining everything by big data was, but, likewise, points towards the future. But one has to look beyond terminology, and towards methodology, to properly see that.
The technological terminology fades at a speed that — paradoxically — manifests its importance to a field that by default disregards the importance of exactly that: terminology. In tech, the level of linguistic oblivion is monumental. This is a paradox, as many problems and ideas remain the same, but are renamed and performed with younger technologies. Hence, in Anderson’s case, big data as a term faded with the movement from on premises to cloud.
Accordingly, in recent years, big data has been declared dead. Also by the author of this review — but giving Data Teams a close read unfolds that it is the terminology of big data that died. The methodology however, remains, and remarkably few companies are good at it. It still takes an organizational setup that only few organizations will understand. It still takes the three data teams pointed out by Anderson, to succeed with an advanced data strategy. If you want to work with data at gigantic scale, then Anderson’s book is the key, regardless of what you may call your data activities.
That is how you will learn everything big data ever was and everything it will ever become in Data Teams.
