Back in 2012, the role of Data Scientist was looking like the sexiest job of the 21st century, but it’s going to have to handover its Harvard Review bestowed title before the end of the decade.
The knowledge and skills to turn data into valuable insights is still highly sought-after, but many organisations are coming to the conclusion that the highly specialised field of data is too complex for day to day operations. A crucial knowledge gap is preventing the impact of techniques such as NLP, Deep Learning, and (Un)Supervised Learning from reaching its full potential.
That’s why, last year, consultancy firm McKinsey introduced the role of Analytics Translator as the vital link between data specialists, data engineers and business stakeholders.
How does an analytics translator make a difference?
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 prioritise use cases and strategic applications to get the most value out of the data science capability of an organisation. 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 organisation.
The back and forth of balancing the business and technical is where project management skills are essential. 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.
Analytics translators fulfill different roles in organisations. In most organisations, 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.
Who gets into this role?
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.
A strong business sense also helps to focus on delivering actual business value and guide the solution from a concept, development phase to productionising the model and end user adoption. 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.
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 the new, 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 specialised and demanding. Additionally, as the value of data is accepted across industries, organisations will feel increasing pressure to incorporate data into their corporate strategy.
Breaking down the strategy to actionable topics and further down to new products or process improvements will put pressure on the knowledge gap between the analytics and business teams. Strong analytics translators will help organisations to mitigate this pressure and support the transition to a data driven future.
Are you ready for your next sexy career move? We are looking for an analytics translator to join our Xomnia team. Check out our vacancy, here. If it sounds like this role is for you, get in touch with us today!