The Reason Organizations Need Data Translators As Well As Data Scientists

Just as engineers and marketers do not always speak the same language - or value the same outcomes - so as the economy becomes more data-dependent, interpreting meaning grows in importance. JL

Bernard Marr reports in Forbes:

Only 18% of companies believe they can gather and use data insights effectively. Data translators bridge the communication divide that often develops within an organization between data scientists and executive decision-makers to help guide the conversation about using data to inform decisions and point out biases that result by an overreliance or underutilization of data.  The choice is not whether to hire a data scientist or a data translator as you are likely to need both.
You can invest in data technologies and collect all the data you can possibly imagine, but it’s worthless if it’s not analyzed or communicated to decision-makers so that action can be taken from the insights. Some organizations attempt to communicate data findings across departments of an organization, but something typically gets lost in translation as it makes it way from the data scientists to the executive decision-makers. This issue is so prevalent that only 18% of companies believe they can gather and use data insights effectively, according to a McKinsey survey. Due to this reality, some organizations have opted to add a data translator to help bridge the gap.
I have recently been approached by two separate clients about data translators, one wanted me to fulfil the role as part-time chief data translator and the other wanted my help in recruiting and training internal data translators. Given this sudden interest in this new role I thought it would be a good idea to explore what a data translator is and whether your organization needs one.
What is a data translator?
A data translator is a conduit between data scientists and executive decision-makers. They are specifically skilled at understanding the business needs of an organization and are data savvy enough to be able to talk tech and distil it to others in the organization in an easy-to-understand manner.
This professional must be someone who can “talk the talk” of both the executives and the data scientists. They are adept at extracting the business meaning and applications from the information they are provided by the data scientists. They not only respect the functions of the data scientists, but also understand the needs of decision-makers; therefore, successful data translators are typically respected by those entities in return.
How can data translators help organizations?
Data translators bridge the communication divide that often develops within an organization between data scientists and executive decision-makers. They are able to communicate with language that a decision-maker understands.
Data scientists often prefer the independence of assessing data rather than explaining to non-tech people the implications of the data, how it can help support or solve business issues, or being pulled into executive meetings to defend the data insights or why data should be trusted.
Executives can be dismissive of the data which can impact those that are responsible for it within the organization. Decision-makers usually like to know they are in control, but when faced with data they often don’t fully understand, it can make them uncomfortable.
Since they understand the core business objectives of an organization, data translators can identify business actions based on the findings of the data that neither the data scientist or executive are able to extrapolate. They can add value to the data insights that propel the organization toward its goals.
Data translators can help guide the conversation about using data to inform decisions and point out biases that result by an overreliance or underutilization of data. In both cases, a person’s individual perspective can impact the way the data is used; data translators can help identify when your team is heading off track. Even though data is critical input for making business decisions, it’s not the only criteria used.
What skills does a data translator need?
Some organizations might determine they need more than one data translator with different areas of expertise; however, the following skills are typically what a successful data translator would need:

• Desire to ask questions and get a deeper understanding of issues (business and data)
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• Confidence to challenge perceptions and biases of individuals at every level of the organization
• Solid understanding of business requirements and vernacular
• Analytics knowledge or desire to acquire it to be effective communicating with data scientists
• Passion to give others an advantage of understanding by using accessible language

How do you know if your organization needs a data translator?
One of the main reasons that organizations decide to add a data translator is to minimize the frustration and miscommunication and maximize the business returns of the data. If your team is collecting and storing a lot of data, but you can’t see how that data has resulted in business decisions or enhancements, it might signal that you are not using the data to its full potential. The solution might just be in better communication.
You might already have an individual on staff who might be uniquely qualified to step into this role. They just need to have the core communication skills and a desire to bridge the gap between data scientists and executives. Their knowledge about your business operations gives them a head start to being successful as your data translator.
Data translator instead of data scientist?
Let’s be very clear here, the choice is not whether to hire a data scientist or a data translator as you are likely to need both. I have had the pleasure to work with some fantastic data scientists that had both, the analytical skills and the data translation skills, but those are rate and as such have sometimes been called unicorns.
I think the role of data translators is a great new addition to the ever-expanding data-related job market and one that could ensure organizations deliver even more business value for their data.