TYPE A AND TYPE B
There are two major groups of Analytics Translator: Type A (for Analytics) and Type B (for Business). The main difference is their background. The Type A Analytics Translator has a background in Data & Analytics and a focus on the Business, the Type B Analytics Translator has a background in the Business and a focus on Data & Analytics. Simply said, they do more or less the same thing but come from a different angle.
The differentiation makes perfect sense. Analytics Translator is a bridge function. And just like in real life, you can enter a bridge from two sides. Analytics Translators bridge the gap between Data & Analytics on one hand and the Business Community on the other hand. So, you either come from the Data & Analytics side (the Type A) or from the Business side (the Type B).
If you are familiar with Berkeley University professor Edward Ashford Lee’s book “Plato and the Nerd“, it’s the diference between a Literate Technologist (Type A) and a Numerate Humanist (Type B).
ROLE OR FUNCTION
You hardly see job openings for Analytics Translators or people with Analytics Translator as their job title. Today. And although we expect that to change soon, currently Analytics Translator is more a role than a function.
As stated above, typical functions in the Analytics Translator role are:
- Chief Data Officer
- Chief Digital Officer
- Full Stack (or Unicorn) Data Scientist
- Data Scientist (with domain knowledge)
- Business Manager (with datascience knowledge)
- Business Development Manager
- (Data) Product Owner/Manager
- Project Manager
- (Technology Driven) Change Manager
- Data Consultant
- BI Expert / Data Analyst
- Data Visualizer
We truly believe that Analytics Translator is an excellent career path towards a CEO or COO position in a data-driven organization (which is the organizational model of the 21st Century, anyway).
An Analytics Translator career will typically follow the above mentioned three key business result areas:
- Making stories and decision with data
- Translating analytics to business
- Building a data-driven organization
An example of building-up your career as an Analytics Translator is:
- As an INTERN – Clean-up datasets or work as a database administrator, get your hands “data dirty”
- As a JUNIOR – Start with making stories and decisions with data, eg. as a Data Analyst, Junior Data Consultant or Data Visualizer.
- As a MEDIOR – Start with parts of or small products or services as a Data Product Manager or Data Consultant
- As a SENIOR – Move towards groups of data-driven products and services as a Data Product Owner
- As a MANAGER – Be a Department Manager with focus on data-driven products and services or be a Data & Analytics Team Lead with focus on business results
- As an DIRECTOR – Be a Business (Unit) Manager with focus on data-driven products and sevices or be a Chief Data Officer transforming your company into a data-driven organization
- As an EXECUTIVE – Be the COO or CEO of your data-driven organization
WHY? AI, ROBOTICS & BIG DATA CHANGE EVERYTHING
We have entered the era of Big Data. Data is “the new oil”, and Analytics is the way to “extract the oil”, ie. build a profitable Data-Driven business. Disruptive technologies like AI (Artificial Intelligence), Robotics, Internet-of-Things and Blockchain all heavily lean on Data and Analytics.
Not-engaging in Data and Analytics is seriously risking the future of your company. Smart business leaders understand this. Data Scientists are the key professionals who can make Data and Analytics work. So smart business leaders have already started building up a Data Science team.
DATA SCIENCE DOESN’T WORK (WELL ENOUGH)
But there’s a problem! Companies hire scarce PhD’s into prominent Data Science roles, often with excellent analytical skills. Unfortunately, most of them have limited experience in managing people and change, or little expertise in your industry.
For the maturing profession of Data Science to be effective in an organization you need both excellent analytical skills and excellent people & change skills and excellent function or industry skills. You need to “extract the oil and sell the petrol”.
So, today’s data science role is like asking someone to juggle three bowling balls! It’s too broad. The data scientist has become a jack of all trades.
In reality, once dubbed the sexiest job of the 21ste Century, data scientists spend most of their time collecting and processing data rather than finding business insights. Let alone, turning them into profitable products.
Often, the result is a frustrated data science team and frustration with the rest of the organization: data scientists blaming the rest of the organization not to understand the “absolutely brilliant things” they do for the business; and the rest of the organization clueless about how “these high-paid big data wizards” actually contribute to the business at all.