Big Data Has to Make Sense to People

You might have three PhDs and a brain the size of a football, but if you can’t explain things in simple language to others, it will be a struggle to maximize your potential impact on the world. Of course, there are many notable exceptions of academically brilliant individuals who have transformed the world, but people such as Einstein, Newton, Hawking, et al., often developed their theories in the confines of their labs and studies.

Data Science professionals, clever as they may be, have no choice but to work extremely closely with their non-scientifically-minded colleagues. I’d just like to say that intelligence is important in business, but it is far from the only success factor. A practical focus on getting things done and an ability to get on with other people are two things that every great leader has, and what you might call EQ is sometimes more important than IQ.

So, for the data gurus to make a difference, it is essential that they learn to translate data into something that everyone can appreciate. This might involve dazzling visualizations and colorful graphics, and it might even involve the odd bit of humor to help the points to sink in.

If the data is not understood, it won’t be able to effect change.

If the Data Science teams can’t communicate its essence in a simple way, it will stay as a complicated but brilliant footnote in the company’s data archive.

Too many under-communicated projects like this and the effectiveness of Big Data and Data Science will start to be called into question. It has to make a difference. It has to get down and dirty, and no matter how deep the wormhole goes, Data Scientists have a duty to share the “obvious” insights that reside nearer the surface. Once the obvious wins have been sufficiently digested, that is the time to maybe explore things at a deeper level.

Explaining complicated data in a simple way is the art form of the Data Scientist, and it is one of the key questions that we ask when interviewing Data Scientists at Big Cloud.

How do you explain your findings to your non-analytical colleagues?

The moment that anyone hesitates to think is the moment I know they’ll be the one to sit at their desk with their headphones on and get stuck in. But, thinking that the business only needs their brain to crunch the numbers, they don’t see the relevance in trying to explain the numbers to people. This can be limiting to their careers.

On the other hand, there are many candidates who roll their eyes and launch into a monolog about the challenges involved. I’m not saying that it is easy, but the very best Big Data professionals (whatever their role) will have these conversations on a regular basis.

If data can’t be explained to “normal” people in a “normal” way, it is as good as useless.