Way back in 2013 I suggested that the data scientist role must be played by a team. Over the last few years this seems to have become a majority view – with the emergence of the data engineer and, most recently, the data translator.
The data translator bridges the gap between business and data science
Last month I came across a case study from Collibra customer DBS Bank in which Chief Data and Transformation Officer, Paul Cobban, shares insights into the bank’s Data First program. This program has identified a critical new role – that of the data translator – to support the data science team.
The data translator translates data science concepts to the business, and ensures that data science outcomes deliver actual business needs. The data translator is the link between the hard core data scientist or data engineer and the business – knowing enough about each to bridge the gap between the technical teams and the business to ensure that:
- Data based programs have are prioritised based on business outcomes
- Data analytics projects have defined, measurable goals
- The results of data analytics projects can be understood by a non-technical audience
- Business acts on the insights provided by the data science team
Why do you need data translators?
In an article published by Harvard Business Review in February 2018, consulting firm McKinsey said translators play a critical role in bridging the technical expertise of data engineers and data scientists with the operational expertise of marketing, supply chain, manufacturing, risk, and other frontline managers. McKinsey predicts demand for translators in the US alone may reach two to four million by 2026.
DNB’s Cobban told Waters Technology that the data translator frees data scientists up to focus on the complex mathematical and statistical models that are their specialty, and brings business context and prioritisation to the mix. “Otherwise, the data scientist is spending 90% of their time doing things other people can do. It’s the same on the translation side, which is why we think data translators are important because data scientists may not necessarily understand the business.”
According to a McKinsey study, disconnects between the business and data science teams are so prevalent that only 18% of companies believe that they gather and use data insights effectively.
Like the data engineer, the data translator brings different skills to round out the data science team and deliver more value.