According to many, the analytics translator is the latest "most important job of the future." Why? What is an analytics translator and how does it differ from a data scientist? How do you become an analytics translator? Read on!
Analytics Translator
Lately, we're hearing more and more about a new role: the analytics translator. Harvard Business Review recently published a major article on the topic. McKinsey calls it the new "most important new role in analytics." Others are already shouting "the new sexiest job of the 21st century!" Some even believe the demand for analytics translators will significantly surpass that for data scientists. So where is all the commotion coming from?
Data Scientist
Over the past few years, we've heard a lot about the Data Scientist—considered by many as one of the most important jobs of the future. By now, the role has become more refined, and several branches and specializations have emerged. For instance, we now distinguish between data scientists and citizen data scientists. The latter group is less focused on statistics and algorithms and more on practical analysis and the application of data within a specific business context.
Artificial Intelligence
The value of data analysis and business intelligence has long been proven. In recent years, however, many organizations have also invested in the potential of advanced analytics and artificial intelligence (AI).
Perhaps enticed by the dazzling possibilities of such solutions, as often presented by the media and vendors. And of course, as an organization, you must continue to evolve. Maybe you can easily make a major efficiency gain—or even discover entirely new business models? Organizations need to stay ahead of the competition by continuously developing.
Return on investment often falls short
Of course, there have been many successes. Yet, for many companies, it often remains limited to small, one-time successes or proofs of concept. A clear, tangible 'return on investment' is often still lacking. One reason for this might be that such initiatives are frequently confined to the 'IT department' and 'data/tech' people.
A key reason why companies fail to make progress in Advanced Analytics and AI, in my view, lies in the lack of involvement from the 'business' in the solutions being developed. And that’s exactly where value creation takes place on a daily basis.
Tech and data teams often experiment enthusiastically with advanced analytics and AI solutions without clear involvement from the business side. As a result, the focus is too little on developing practical applications that actually add value for internal or external customers. The business is not involved enough in the process.
That is likely also why the solutions offered are usually not accepted—or even understood—by the business. They often struggle to incorporate them into existing workflows. There is simply no buy-in.
What does an analytics translator do?
An analytics translator can help develop AI solutions that add value to your organization.
First, the analytics translator can bridge the gap between the worlds of 'data specialists' and 'business specialists'.
Data specialists—like data engineers and data scientists—excel at building platforms and extracting insights from data. Business specialists understand their specific processes and have in-depth knowledge of value chains.
The analytics translator has a foundation in both disciplines and uses their broad knowledge to build a bridge between these two worlds.
Second, the analytics translator seeks the right use cases and applications for AI solutions within the organization.
Finally, the analytics translator's task is to make AI an integral part of the organization.
What kind of profile does a good analytics translator have?
An analytics translator has general knowledge of data engineering and data science, along with strong knowledge of business processes and the business side of things. What type of business depends on the organizational context. As an analytics translator, you can even specialize further—whether in healthcare, automotive, construction, finance, etc. Lastly, a good analytics translator has excellent presentation skills and the ability to connect people and departments. They are persistent and don't give up easily, even when facing resistance.
Raymond te Veldhuis
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