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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.
Data Science – A Profession of Specialties
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.
Read full article here. (By Bill Vorhies)