Taking the fear out of Data Science
  • 22nd January, 2019
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Taking the fear out of Data Science

By Dr Taryn Morris

Data and Science. Two little words that when each is spoken alone, conjure up images of a black box with puffs of indecipherable jargon escaping from it every now and then. When put together, these two little words can make even the keenest of colleagues glaze over and back away towards the door in panic.

As the world of data science advances rapidly, it becomes an increasingly scary black box that many would prefer to leave unopened. If, however, we as data scientists could convince people to lift the lid and take a peek, we could take the fear out of data science and open up a fascinating way to explore the everyday world around us. 

In many instances, it is not that the world of data science is too hard to understand, but rather that as data scientists we are more adept at communicating with our computers than we are with our colleagues, clients or CEOs. Thus there is a clear need to work on how we communicate the huge benefits of data science.

Easier said than done you say! Well, let’s start with the obvious in defining data science.

According to Wikipedia: “Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured, similar to data mining.”

Errrrrr … that seems quite complex. So let’s try to break it down to the bare essentials instead. According to the Cambridge English dictionary, data is: “information, especially facts or numbers, collected to be examined and considered and used to help decision making, or information in an electronic form that can be stored and used by a computer". Okay, WOAH! Hold on! That just seems like a really complicated way of saying data is just  … information. Next up is the definition of science, which thankfully, is stated far more concisely as “the state of knowing”.

 So simply put, data science is really just the art of gaining knowing (or knowledge) from information (or data).

Not so bad right? If we cut out some unnecessary words and jargon from the above Wiki definition, we’ll see that they are really just trying to say the same thing in a highly complicated way: Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured, similar to data mining.

 Lesson 1) When in doubt … Break it down.

Here are a few more tips on how to make data science a little less scary and a little more merry:

2) Don’t speak Jargonese! People probably already know that you are pretty smart. They don't need you to spout undecipherable acronyms and tech-speak to show off your immensely impressive knowledge. From several years of working at Universities, probably the most important thing that I learned is that the most successful scientists are the ones that can explain really complex topics in a way that everybody can understand. Less really is more.

3) Know your audience and use examples that people can relate to. For example, if you are dealing with marketing or social media departments instead of telling them about "customer churn using predictive analyses or customer segmentation", perhaps tell them about how data science can be used to look at customer interactions by looking at what people say on Twitter. (Note - In Jargonese this would be translated to: "how data science can be used for sentiment analyses by scraping of twitter data”.

4) Show and tell. Wherever possible, show people rather than tell them. Give a lunchtime presentation, send around an article or provide them with an interactive experience. For example, a free basic online tool can be used to show people how the Twitterverse is engaging with any topic of their choice. This shows the user what people are saying, the tone of their tweet, what time they are engaging, where they are based etc. This would allow your marketing department to really engage with what you are trying to explain rather than just blankly watching your mouth move until it finally stops.

5) "So that"! Explain to people not only what you can do but also why it might be important or useful to them. For example:  "I can analyze what people are saying about the new self-tying shoes so that we know how many we might want to order. Or so that we know that we might need some PR crisis management etc.

Lastly, you may ask yourself - why even bother?

Well, getting those that we work with to be a little less afraid of data science is hugely beneficial for you, your team and ultimately the goals of your organisation. By enabling people to understand what it is that you as a data scientist do, you are more likely to enable partnerships and relationships with colleagues. This in turn allows you to do your job faster and better proving greater value to your company and to your clients. Now who doesn’t want that?

Photo by M.T ElGassier on Unsplash

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About Author

Dr. Morris has extensive experience in research, teaching, communication and policy, which equips her to approach problem solving with multiple lenses to find implementable solutions to any given problem. Taryn brings strong a statistical background, nifty data skills and sharp critical thinking to her work. She has a keen interest in using data science for good, particularly to address the increasing environmental problems facing our society today.

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