Analytics Translator – The Most Important New Role in Analytics
Summary: The role of Analytics Translator was recently identified by McKinsey as the most important new role in analytics, and a key factor in the failure of analytic programs when the role is absent.
The role of Analytics Translator was recently identified by McKinsey as the most important new role in analytics, and a key factor in the failure of analytic programs when the role is absent.
As our profession of data science has evolved, any number of authors including myself has offered different taxonomies to describe the differences among the different ‘tribes’ of data scientists. We may disagree on the categories but we agree that we’re not all alike.
Ten years ago, around the time that Hadoop and Big Data went open source there was still a perception that data scientists should be capable of performing every task in the analytics lifecycle.
The obvious skills were model creation and deployment, and data blending and munging. Other important skills in this bucket would have included setting up data infrastructure (data lakes, streaming architectures, Big Data NoSQL DBs, etc.). And finally the skills that were just assumed to come with seniority, storytelling (explaining it to executive sponsors), and great project management skills.
Frankly, when I entered the profession, this was true and for the most part, in those early projects, I did indeed do it all.
It’s fair to say that today nobody expects this. Ours is rapidly becoming a field of specialists, defined by data types (NLP, image, streaming, classic static data), role (data engineer, junior data scientist, senior data scientist), or by use cases (predictive maintenance, inventory forecasting, personalized marketing, fraud detection, chatbot UIs, etc.). These aren’t rigid boundaries and a good data scientist may bridge several of these, but not all.
With about 65% of folks with the title ‘data scientist’ having entered the profession in the last three years, clearly these new data experts also have learned to slot themselves into roles for which they’re trained and presumably enjoy.
We’ve always had a problem with the consistency of job titles. Everyone still wants to be a ‘data scientist’. But it’s fair to say that our understanding of different roles has matured just as these roles have proliferated.
The most recent major change in the way that DS roles are defined comes from the growing importance and penetration of analytics in business. Analytics has become a team sport requiring not only data scientists, but also data engineers, IT, LOB managers, and classic analysts (aka citizen data scientists).
If you’ve watched the way analytic platform developers have addressed the market over the last many years, clearly their move was to make more room at the technical data table (data scientists, data engineers, analysts). Now that advanced analytics has become decidedly mainstream among the leaders in all industry segments, the table is getting even larger.
