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SmartData Collective > Big Data > Data Visualization > How to Speak Like a Data Scientist
AnalyticsData Visualization

How to Speak Like a Data Scientist

Brett Stupakevich
Brett Stupakevich
5 Min Read
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how to speak like a data scientist photo (data analytics)A recent blog posted in MIT’s Technology Review explores how organizations are drowning in a sea of dat

how to speak like a data scientist photo (data analytics)A recent blog posted in MIT’s Technology Review explores how organizations are drowning in a sea of data and why it’s critical for data scientists to not only be able to “extract actionable information from this mass of numbers” but also make the results of that data visual for decision makers.

That’s absolutely true, but let’s take the discussion a step further. Business leaders and other executives who are ultimately responsible for acting on these insights need the data to be relevant and to pop out at them. “As the late Steve Jobs showed us, presentation and communication are key factors to putting an idea to work and winning mass adoption. Data scientists, too, need to have some verbal and visual communication skills, lest their insights wither away in the lab.”

Columns of numbers don’t mean a thing unless they’re placed in some context. And visual context means little even you don’t have the ability to identify the business need and communicate a story.

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In order to succeed, analytics professionals must be proficient in three types of skills: technical, business thinking and “intangibles,” according to Kaiser Fung (@junkcharts), Vice President of Strategic Analytics at Sirius XM Radio, and the author of Numbers Rule Your World: The Hidden Influence of Probability and Statistics on Everything You Do.

Fung makes the case that people who aren’t statisticians still have to understand what analytics can accomplish – and it’s up to the data analysts to help them by speaking to them in language they understand.

Meta Brown (@metabrown312) agrees, saying that you have to present the information to decision makers in the right way. If you want more power to engage executives with analytics you have to speak to them the way they like, the way that makes them feel important, confident and above all, powerful.

To do that you have to understand these four things, which she details in this article:

  1. The only numbers that interest executives are numbers with dollar signs in front.
  2. Executives have very short attention spans: get to the point
  3. Beware of details
  4. Strip tease holds attention better than full disclosure

One way for data scientists to “speak” to the business executives is by constructing compelling infographics that tell stories or reveal new trends that are relevant to their businesses. These tools and techniques are more likely to engage business leaders. In fact, decision makers who are able to more easily grasp this information are also more likely to act on it.

Let’s face it: one of the biggest frustrations for analytics professionals is going from the “Aha!” moment of drawing meaningful correlations with data only to later discover that the recipients of that information never bothered to act on it. Some executives are more inclined to process information visually.

Tell a story

Data professionals can use data visualization techniques to tell convincing stories quickly. While creative graphics can be used to help engage and enlighten readers, it’s also important to keep them simple: don’t try to over-engineer the graphics or make the visualization so complicated to follow that you end up confusing the recipient. Data visualization provides data scientists opportunities to shed new light on correlations or the casual relationships among a wide range of variables. Making those types of connections for organizational leaders can lead to significant discoveries and powerful business outcomes.

The effective use of bar charts, scatter graphs, maps and other visual representations can be an imaginative blend of art and science. There’s a massive amount of data that’s now accessible to data scientists. With the tools that are now available, there’s no reason why you can’t be creative with them.

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