Back in 2012, the role of a data scientist looked like the sexiest job of the 21st century. However, it had long since handed over its Harvard-Review-bestowed title to a new role: the analytics translator.
The knowledge and skills to turn data into valuable insights is still highly sought-after, but many organizations are coming to the conclusion that the highly specialized field of data is too complex for day to day operations. This means that a crucial knowledge gap in those organizations is preventing the impact of techniques ,such as NLP, Deep Learning, and (Un)Supervised Learning, from reaching its full potential.
This is where the role of an analytics translator comes into play, which consultancy firm McKinsey introduced as the vital link between data specialists, data engineers and business stakeholders.
What is an analytics translator?
Analytics translators fulfill different roles in organizations. In most organizations, they work in teams as a product owner or business analyst which develops data driven products or in a more strategic managerial role as product manager.
The main skills of an analytics translator are general technical fluency, project management skills, an entrepreneurial spirit and domain knowledge. That last one looks familiar – it’s the overlapping skill between the analytics translator and data scientists.
This skill set enables the analytics translator to prioritize use cases and strategic applications to get the most value out of the data science capability of an organization. Once use cases are identified, the analytics translator is the go-to person to discuss the feasibility of these topics with the scientists and engineers in the organization.
The back and forth of balancing the business and technical is where project management skills are essential for an analytics translator. In addition, these translators must excel at stakeholder and expectation management, and also have a knack for navigating corporate politics to drive forward strategic priorities and operational opportunities.
How to become an analytics translator?
There is no typical background for an analytics translator. In practice, we see that successful analytics translators have strong business backgrounds, which helps them to quickly understand the client or business and find the true cause for a use case.
On the one hand, A strong business sense helps to focus on delivering actual business value and guide the solution from a concept, development phase to productionizing the model and end user adoption. On the other hand, General tech savviness helps them to formulate the problem at hand into a solvable problem statement (that is actually an ML/AI problem) for the data science team.
Nevertheless, Analytics translators do not necessarily have in-depth training in programming or modeling. A common misconception is that an analytics translator is also a scrum master, but that is not an area of expertise related to analytics translating.
So, is this just a sexy career move, or a truly crucial role?
The future will bring greater urgency to training or hiring strong analytics translators. As the field of data science evolves, the role of data scientists and engineers will become more specialized and demanding. Additionally, as the value of data is further acknowledged across industries, organizations will feel increasing pressure to incorporate data into their corporate strategy.
Strong analytics translators will help organizations bridge the knowledge gap between the analytics and business teams. By breaking down a strategy to actionable topics and further down to new products or process improvements, analytics translators will mitigate pressure and support organizations transition to a data driven future.