It’s the era of the analytics translator

It’s the Era of the Analytics Translator


Organizations that are looking to be successful with data must not fixate on data scientists only. Analytics translators are essential to get real value from data. 

Only a few years ago, data scientist was hailed as the sexiest job of the 21st century. Blinded by the promise of AI and analytics, major organizations employed entire armies of data scientists. When we take stock of the situation, we can clearly argue that this approach has not always led to success. Particularly lack of business knowledge and limited consulting skills tend to limit the added value of data scientists. All too often, their approach is too technology driven, which sees them struggle to make a difference. It is therefore time for organizations to start investing in another kind of employee, in addition to data scientists, who has one foot in the business and one foot in the world of AI and analytics: the analytics translator 

Logical addition to data scientists 
Let it be clear from the onset that this is by no means a plea against data scientists. On the contrary: they are crucial for performing analyses and developing models in order to transform data into value-adding insights and decisions. Higher management, however, often has unrealistic expectations: they demand things that data scientists, when acting alone, cannot deliver and, frankly, do not make them all to happy either. Data scientists cannot be expected to build entirely integrated analytics solutions that perfectly meet the needs and wishes of a specific department, without having the required business and domain knowledge. Data scientists have unique skills, but they are rarely ‘unicorns’.  

Analytics translators are therefore considered a logical and a much-needed addition to data scientists. Since analytics translators are familiar with a given business domain, they are able to formulate business problems in such a way that they can be solved by applying data science and collaborating with a data scientist. And by starting from the business problem you ensure AI and analytics will not become an end in itself, but simply a tool to solve a problem.   

This is what an analytics translator does 
Analytics translators can best be described as bridge builders, forming the link between data scientists on the one hand and business domains, such as sales, marketing, finance, HR and procurement, on the other. Ideally speaking, analytics translators have extensive experience in one of these business domains, so they have thorough insight in their specific business problems and challenges. Together with data scientists, they explore the best way to solve these problems and identify the insights that might help. It is important that analytics translators understand how data scientists think and work, but they must also be able to challenge them and even stop them when needed. This, in the end, will lead to technology serving the business, rather than the other way around. After all, the primary goal of analytics translators is to create business impact, not to come up with the most complex technical solution. 

The skills of an analytics translator  
To start with, the ideal analytics translator will be thoroughly acquainted with a particular field of expertise, business domain or department, as outlined above. They must also be creative and capable of effecting change. Resistance can arise on both sides: data scientists may stubbornly keep doing things in his own way and the sales department will have to be convinced of a new way of working based on predictions and data. It is up to the analytics translator to connect these seemingly unconnected worlds. It is important to emphasize that they do not need to be mathematicians or coders themselves, but they should understand the possibilities (and impossibilities) of analytics for the business, have a basic understanding of certain models, and be able to identify the challenges in data projects. Someone with a quantitative aptitude, interest and drive would be well capable of learning these basic analytical and mathematical skills. The best analytics translators are, with the proper training, therefore, to be found in your very own organization. 

This is the ideal environment for an analytics translator  
We recommend that in the coming years organizations with data-driven ambitions invest in analytics translators. However, this call should not prompt CEOs, CIOs and CDOs to suddenly recruit and set up entire teams of analytics translators, like they did with data scientists the past few years. Creating a new-style, central IT department is by no means an effective way to get analytics translators to yield optimal returns and obtain real value from data. Our message to boards of directors is the following: do not make the same mistake as thirty years ago, when IT departments were set up as separate departments that drew too much power to themselves. Do not fall into the same trap and do not turn analytics into an IT-only affair. Rather, leave the initiative to the departments, by embedding analytics translators into your organizational departments. In fact, take a careful look at those departments before starting a large-scale recruitment process. Chances are that the analytics translators you’re looking for are within an arm’s reach. 

Edited article in English.

First published in Dutch on CIO:

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Patrick Hennen and Robert Monné
Patrick Hennen combines integrity, energy and a passion for mathematics in his current role as managing partner of Data Science & Consulting at ORTEC. Patrick studied econometrics at VU Amsterdam and works at ORTEC since 2002. Robert Monné is data-driven professional with an entrepreneurial mindset. He is a Business Consultant at ORTEC and manager of The Analytics Academy: a cooperation between ORTEC and the University of Amsterdam.

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