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Data Translators: Bridging the Gap Between Data Scientists and Executive Decision-Makers

By Aspen Runkel

Leveraging Data-Enabled Insights for Competitive Advantage

Times have changed. We now live in a digital world allowing for seemingly endless opportunities to leverage data for competitive advantage. Andrei Hagiu, professor of information systems at Boston University’s Questrom School of Business, and Julian Wright, professor of economics at the National University of Singapore, wrote about when data creates a competitive advantage in the January–February 2020 Issue of the Harvard Business Review (HBR). Hagiu and Wright pointed out that using data to improve business decisions is “an age-old strategy, but the process used to be slow, limited in scope, and difficult to scale up.” Technological developments have changed this dramatically. It’s no surprise that organizations are doing their best to take advantage of these improvements by implementing advanced analytics and artificial intelligence (AI) to improve decision-making (Hagiu & Wright, 2019). They have the tools and now they are looking to data experts to rapidly make sense of the vast amounts of data and create a competitive advantage with data-enabled insights.

It seems the need to hire the most qualified data experts, such as data scientists, has never been greater. What organizations are beginning to realize though is they need to set their sights on a more diversified crew of data professionals to successfully marry data analytics with strategic decision making. But who are these people? (unsubtle hint: data translators) More on them later.

Need a data expert? Bring in the data scientists (or don’t).

In a 2017 report for IBM and the Business-Higher Education Forum titled “The Quant Crunch: How the Demand for Data Science Skills is Disrupting the Job Market”, Burning Glass Technologies conducted a comprehensive study of the marketplace for data science and analytics (DSA) skills. They found that the 2.35 million job listings for all DSA categories in 2015 were projected to grow by 15% over the next five years to a predicted 2.72 million by 2020. Impressive right? Even more impressive is the predicted 28% increase in demand for Data Scientists and Advanced Analysts by 2020 (Miller & Hughes, 2017).

The increase in demand for professionals with data analytics skills is the reason that I continued at the University of Montana to study business analytics after completing a bachelor’s in marketing in 2018. Not only will my skills be in demand as I enter the job market, but the data also suggests that I should expect a great return on my investment.

According to Burning Glass Technologies, the supply of DSA talent is lagging dangerously behind demand. This drives salaries up to an average annual salary of $80,265—a premium of $8,736 (as of 2017, the time of the report) relative to all bachelor’s and graduate-level jobs. Some DSA jobs, such as Data Scientists and Data Engineers, demand salaries well over $100,000 (Miller & Hughes, 2017).

Clearly there is a demand for data science and analytics experts, and the salaries they command are encouraging. But do I need to be a fully qualified data scientist to work with data? Not necessarily. As I teased earlier, leveraging data to create a competitive advantage requires workers with a mix of disparate technical, analytical skills and domain-specific expertise.

Diversify your crew of data professionals!

In an article published in 2018 by the HBR titled “You Don’t Have to Be a Data Scientist to Fill This Must-Have Analytics Role”, authors Nicolaus Henke, Jordan Levine, and Paul McInerney assert that data scientists are necessary, but they are not the only data experts organizations should be interested in. They wrote,

Certainly, data scientists are required to build the analytics models — including machine learning and, increasingly, deep learning — capable of turning vast amounts of data into insights. More recently, however, companies have widened their aperture, recognizing that success with AI and analytics requires not just data scientists but entire cross-functional, agile teams that include data engineers, data architects, data-visualization experts, and — perhaps most important — translators (Henke, Levine, & McInerney, 2018).

The Quant Crunch report also supports this recommendation to build multi-faceted agile teams. This need for individuals whose skills transcend beyond technical expertise is also consistent with my experience searching for jobs in digital marketing in preparation for my upcoming graduation in May 2020. Marketing professionals are often expected to combine deep marketing knowledge with advanced analytical techniques.

So, we know that demand for data experts is on the rise. We also know that organizations are looking to fill a variety of different data roles to round out their team. But why? There must be a fundamental reason why organizations aren’t just looking to hire the best data scientists they can find. To investigate, let’s figure out what a data translator is and why they are so important.

What is a data translator?

A data translator is exactly what it sounds like. In a 2018 Forbes article titled “Forget Data Scientists And Hire A Data Translator Instead?”, Bernard Marr wrote,

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 distill it to others in the organization in an easy-to-understand manner (Marr, 2018).

Henke, Levine, and McInerney expand on the role of a data translator, emphasizing that data translators are not necessarily dedicated data experts. They write,

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. In their role, translators help ensure that the deep insights generated through sophisticated analytics translate into impact at scale in an organization (Henke, Levine, & McInerney, 2018).

Based on the critical capabilities data translators bring, the McKinsey Global Institute estimates that demand for translators in the United States alone may reach two to four million by 2026 (Henke, Levine, & McInerney, 2018).

Why are data translators so important?

Let’s circle back to the earlier question: Why are organizations looking to fill a variety of different data roles rather than just hiring the best data scientists they can find? It turns out that something typically gets lost in translation as data scientists communicate data findings to the executive decision-makers. According to a McKinsey survey, this issue is so prevalent that only 18% of companies believe they can gather and use data insights effectively (Marr, 2018).

One reason for this gap in using data insights effectively arises from the key differences in the data scientists’ perspectives versus the executive decision-makers’ perspectives. Marr says, “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 (Marr, 2018).” In contrast, executive decision-makers “usually like to know they are in control and may become uncomfortable when faced with data they often don’t fully understand (Marr, 2018).”

A data translator can work magic by bridging this gap that often develops in an organization between data scientists and executive decision-makers. They can help guide the conversation about using data to identify business opportunities, adding value to the data insights and propelling the organization toward its goals. This data expert role requires a unique skill set that includes understanding and respecting both the data scientists’ and the executive decision-makers’ needs.

What skills do data translators need?

Based on the 2018 HBR article by Henke, Levine, and McInerney and a 2019 HBR article by Scott Berinato title “Data Science and the Art of Persuasion,” I have gathered several key skills data translators need.

Project Management

Data translators must have project management skills and “be able to direct an analytics initiative from ideation through production and adoption and have an understanding of the life cycle of an analytics initiative and the common pitfalls (Henke, Levine, & McInerney, 2018).” As the person who is communicating the technical expertise of data engineers and data scientists to the operational expertise of executive decision-makers, data translators must possess this skill because they will play a key role in successfully managing a project from start to finish. To reinforce this idea, Berinato relates project management to data translators saying, “a good project manager will have great organizational abilities and strong diplomacy skills, helping to bridge cultural gaps by bringing disparate talents together (Berinato, 2019).”

Technical Fluency

In addition to project management skills, data translators must have general technical fluency including the ability to make good judgments and quick decisions in quantitative analytics and structured problem-solving (Henke, Levine, & McInerney, 2018). Berinato describes this talent as “data wrangling” and “data analysis”. Data translators must be able to gather and structure data for implementation and processes and be able to develop and test hypotheses to inform business decisions (Berinato, 2019).

Although data translators don’t necessarily need to be experts in building quantitative models, they must know what models to apply to particular business problems, interpret the results, and identify potential errors. This aligned with my experience as a grad student in business analytics. While some of the more technical aspects of analytics have been more challenging, I understand the theory and can apply it as necessary.

Domain Knowledge

Data translators must also have domain knowledge. This means that data translators must have a clear and comprehensive understanding of the company they work for, the industry, and the specific function(s) data are being applied to (Henke, Levine, & McInerney, 2018). Berinato calls this “subject expertise” describing it as knowledge of the business and strategy to help focus insights on business outcomes (Berinato, 2019). Having domain knowledge allows data translators to effectively identify the value of AI and analytics in a business context.

Design

Data translators must also have an eye for design to effectively communicate in a format many prefer – visual.  Berinato says that this skills requires data translators to “understand how to create and edit visuals to focus on an audience and distill ideas (Berinato, 2019).”

Storytelling

As a marketing student, there was a huge emphasis on storytelling to communicate value to and connect with a particular audience. Berinato relates storytelling to the role of a data translator writing, “Narrative is an extremely powerful human contrivance and one of the most underutilized in data science. The ability to present data insights as a story will, more than anything else, help close the communication gap between algorithms and executives (Berinato, 2019).” Bridging the communication gap that exists between data insights and executive decision-makers is the ultimate goal of a data translator.

In comparing this list of skills described in the HBR to a list of skills required of a data translator by Chris Brady, Mike Forde, and Simon Chadwick in their MIT Sloan Management Review article “Why Your Company Needs Data Translators,” commonalities include a deep understanding of the business and analytics, a curious and confident approach, and the ability to communicate with varying perspectives.

Data Translators Work Magic

Organizations are met with a myriad of data, and they are looking to hire data experts to turn that data into their own competitive advantage. Fortunately for me, I don’t have to be a technical data scientist to be valuable to an organization. A data translator can work magic by bridging the communication divide that often develops in an organization between data scientists and executive decision-makers.

As I have progressed through my grad program in business analytics, the data translator role is the one I expect to thrive in as a data-fluent marketing professional. I will improve my marketing expertise and ability to understand insights from data and articulate its meaning for decision making as I gain experience in “the real world”. Learning about the demand for data analytics skills and understanding the need for a diverse team of data professionals has reassured me that I am on a thriving career path. IBM can add me to its statistics. I’m going to go get hired.

Works Cited

Berinato, S. (2019, November 19). Data Science and the Art of Persuasion. Retrieved from https://hbr.org/2019/01/data-science-and-the-art-of-persuasion

Brady, C., Forde, M., & Chadwick, S. (2016, December 5). Why Your Company Needs Data Translators. Retrieved from https://sloanreview.mit.edu/article/why-your-company-needs-data-translators/

Hagiu, A., & Wright, J. (2019, December 17). When Data Creates Competitive Advantage. Retrieved from https://hbr.org/2020/01/when-data-creates-competitive-advantage

Henke, N., Levine, J., & McInerney, P. (2018, February 5). You Don’t Have to Be a Data Scientist to Fill This Must-Have Analytics Role. Retrieved from https://hbr.org/2018/02/you-dont-have-to-be-a-data-scientist-to-fill-this-must-have-analytics-role

Marr, B. (2018, March 12). Forget Data Scientists And Hire A Data Translator Instead? Retrieved from https://www.forbes.com/sites/bernardmarr/2018/03/12/forget-data-scientists-and-hire-a-data-translator-instead/#163e717c848a

Miller, S., & Hughes, D. (2017). The quant crunch: How the demand for data science skills is disrupting the job market. Burning Glass Technologies.

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