Big Data. It seems like you can’t go anywhere without hearing about it. As more and more devices become connected via the IoT, companies everywhere are scrambling to collect, structure, and organize all of the data they can about their customers’ habits and desires. The only question: who is helping them use that data to maximize its power for their company or industry?

In the past, the answer was simple: no one. Most businesses—if they were advanced enough to have a dedicated data or analytics team—had data collectors and decision-makers, with a wide gulf between them. The data team loved creating algorithms and bending the data to see what they could find out. But face it: in general, they aren’t business or marketing people. Meanwhile, the C-Suite execs weren’t quite won over by the concept of data-backed decision-making—let alone data-backed strategy building.

Enter: the analytics translator. Part peace-maker, part-techy creative, part visionary, the translator’s job is take a good hard look at the company’s objectives and find ways to work with data engineers to get the results they’re looking for. They think outside the computer screen. They know the company and the industry. And most importantly, they can get executives to act on data findings.

What’s a Data Translator—And Why Do You Need One?

A report from McKinsey shows that by 2026, the demand for data translators will be 2 to 4 million. Clearly, there’s something to it. In fact, companies are creating their own academies to train data translators because there just isn’t enough talent to match the demand. That’s because data translators / analytics translators do more than read numbers. They create a winning strategy behind them.

For instance, in the past, I’ve shared that when starting with analytics, it’s important to start small. Too much data can overwhelm everyone and quickly turns into a pile of nothing useful. When starting out, companies should figure out what their goals are, what they need to find out, and what they plan to with that information when they do. In the past, this role was sometimes left to marketing—sometimes to IT. But the data translator becomes the bridge that brings those silos together.

What Do They Do … Exactly?

In short, they’re strategists. Data or analytics translators are all about structured problem-solving. They’re creative enough to imagine new possibilities for your company, but also technical enough to explain those goals to the data team. They look at the data presented and think—what if? What if we looked at it a different way? Does this data support our current business strategy? What product or update should we be making to maximize the future of our company? And they’re equally comfortable asking these questions in the data den or the C-Suite.

Who Should I Hire?

Right about now, you’re feeling tempted to go post a job opening for a data translator. My advice: don’t. In general, your translator should be someone who knows your company already—someone with a deep knowledge base of the company’s mission, vision, and goals, and who is capable of thinking in new ways. That won’t be a new college grad or someone you find on the street. It will be someone within your company who has an entrepreneurial spirit and the ability to think from both the left and right sides of their brain. It should be someone as amped as you are (or should be) about using data to its full potential for the good of your company and customers both.

The data translator should also be a good communicator. Indeed, to me, one of the hidden benefits of using a data translator is that it also helps eliminate bias and ego—two things that can get in the way when it comes to making data-backed decisions. The data scientists feel strongly about their methods and algorithms. The executives feel strongly about staying the course and doing things the way they’ve always been done. But the data translator serves as the “Switzerland” of the data debate. They look at the numbers, the goals, and help both sides understand the best way to move forward and can convince everyone to get on the same train.

Yes, it can be overwhelming seeing how many new positions are being generated by tech today—even just in the data sect alone. But the good news is that even if you don’t have a dedicated data translator, you can still make a concentrated effort to consider the issues of the great data divide and name someone responsible for bridging it. After all, not all of us have an entire team dedicated to data. But that doesn’t mean we can’t learn from those who do or work t to bring a data-driven culture to our organization.

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