Get Started

Need a Data Translator? Try an Analyst.

Scaling AI Robert Kelley

 There’s a new Harvard Business Review article (You Don’t Have to Be a Data Scientist to Fill This Must-Have Analytics Rolethat’s getting hundreds to shares on social media, and it makes some great points. But what does it mean for the future of data analysts?

man and women face icons art

The article makes the argument for “data translators,” who “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.” It makes a lot of sense, and the role sounds like a lot of fun.

The audience for the HBR article is managers, but we think it’s equally as relevant for data and business analysts, especially because analysts are the people already in an organization who are best prepared to step up and become translators right away.

Why Analysts Are Perfect Translators

The HBR article lists the skills for a perfect translator: domain knowledge, technical fluency, project management skills, and an entrepreneurial spirit. Let’s start with the first, domain knowledge. Business and data analysts often have the best perspective of anyone of the whole business because they see the full scope of operations through data, and they also know the details that drive success and failure. Especially if they’ve spent a bit of time in the organization, they’re knowledgeable about the business and the industry.

data fluency culture cartoon

Choosing an analyst as a data translator might be the step your organization needs to get to a data-fluent culture.

The next skill, technical fluency, is clearly the strongest match for analysts. At a minimum, data and business analysts are proficient in Excel analysis, and many know SQL and even R and Python or other languages used for statistical analysis. They tend to understand how to “speak data,” to converse about schemas and containers and transformations.

The last two skills, project management and entrepreneurial skills, while not being so clearly associated with analysts, would seem to be relatively more common among them. How so?

Well, analysts’ time is typically spent on two types of work: repeated work and one-off work. The repeated work probably doesn’t need much structuring, because the process is predefined, but the one-off work is a project: identifying the users and contributors, defining the output, deciding which data to use, figuring out how to get it, and executing the analysis.

two business men talking

Analysts do both repeated and one-off work, and these time- and project management skills make them great data translators. 

And the more unique the one-off request is, the more creative and resourceful the analyst has to be in order to fulfill the request. Creativity and resourcefulness… sounds like some of the key entrepreneurial skills required of data translators.

Which Analysts Make the Best Translators?

In our Analyst of the Future guidebook, we defined three future analyst roles: the Data Explorer, the Data Modeler, and the Data Product Owner. With respect to the translator role, the Data Product Manager is the closest match. We define the Data Product Manager as the link between the analysis team and the end users.

The Data Product Manager needs to know the data and the analytical toolkit, but he or she also needs to have the domain expertise and business acumen to design and deploy analytics products that will bring the most value to the whole organization. This sounds a lot like the definition of translator provided above by HBR.

data analyst personality

Analysts wear so many different hats no matter what their primary focus that ultimately, they are all excellent choices for data translators.

Still, translating would only be one part of the Data Product Manager’s role, because the translator does not have to be actively involved in execution. But by definition, the Data Product Manager needs to be a translator.

The other roles, the Data Explorer (who “owns” the data, connects to it, and prepares it) and the Data Modeler (who brings to bear the full analytical toolkit on the problems at hand), are also great fits for the translator role. The Data Explorer needs to know the data, where it comes from, who uses it, and how it’s used. And the Data Modeler knows which analytical methodologies are used where, and he or she is in a perfect explanation to explain to non-technical profiles the ways that data and analytics can help them achieve their goals.

How to Become a Data Translator

If you’re an analyst right now, you’re in a great spot to move into the role of data translator. The key is understanding the perspective of your end users (which means understanding their problems and how data and analytics can help them) and becoming an expert on some of the newer analytics tools. For example, if you’re a skilled SQL analyst, you might want to start learning Python.

If you’re able to translate data and analytics to your less technical colleagues, we can guarantee that they’ll appreciate it.

You May Also Like

Bring Everyday AI to Your Organization: Leveraging Gartner® Insights to Push Further

Read More

Mitigating Data Bias by Implementing Responsible AI Practices

Read More

Smart Manufacturing: How We’ll Get There and How We Won’t

Read More

Data Transformation: How to Transform Data More Efficiently

Read More