Data Scientists Draw Pictures and Tell Short Stories
Brevity is the soul of wit. I have three (3) rules of effective business communication: 1) be brief; 2) be blunt; 3) be gone. This is good counsel for data scientists.
The goal of data science is to make life, business and government better. This means communicating data science effectively and data scientists need to learn to draw pictures and tell stories so laypeople can understand actionable insights quickly and easily to make better decisions.
Communicating insights in a timely and understandable manner to decision makers, at all levels, is a learned skill. Many data scientists feel more comfortable - due to years of academic training - communicating valuable information in long white papers filled with mathematical equations or in long memos filled with sophisticated jargon. While this may work in academia or research environments, this will not suffice in the fast pace business and government world where decisions must be made fast by the consumers of data science.
For example, I received a call from the leadership of a large retail client complaining that a data scientist was unable to communicate results in a way that was understood by the consumers of the data science (a team of sixty plus marketing professionals). We examined the data science and found it to be excellent - full of actionable, valuable insights. Yet the data scientist attempted to communicate the results in a ten (10) page memo full of math equations. The equations were beautiful - but worthless considering that: 1) nobody understood the math; 2) nobody had the time to read a long memo - they needed to make a series of decisions in a short time; and 3) nobody could understand what the data scientist was communicating - they had no clue what insights could help them make better decisions.
Data scientists must learn how to communicate the meaning contained in data in short stories with data visualization. We solved the problem by training the data scientist to use data visualization and short storytelling. We worked with the marketing team to learn what they needed to make better decisions, how to best and most efficiently communicate data science results, and understand decision making processes. No more math equations or long memos full of jargon.
The old saw that a picture is worth a thousand words is even more true in data science. Data visualization is a powerful tool to simplify complexity. Data visualization is visual representation of data: the goal is to communicate information clearly and effectively through graphical means. The picture should provide insights by communicating key-aspects in a more intuitive way. It helps if the picture is beautiful, yet data scientists should avoid the trap of creating gorgeous data visualizations that fail to communicate critical information in a fast, easy and intuitive way.
A good book to help learn how to create effective data visualizations is "Visualize This: The FlowingData Guide to Design, Visualization, and Statistics", by Nathan Yau. Although we are constantly exposed to graphics that lack context and provide little actionable insight, Yau separates the signal from the noise and explains the tools to create better data graphics. He shows how to explore data through visual metaphors that tell short stories. Click here to find downloadable data files, interactive examples of how visualization works and code samples to use as the basis for your own visual experimentation.
Short storytelling is also important. People love stories and can often better understand meaning via storytelling than simple fact-telling. Explaining the meaning of data with a good story helps hide and manage its complexity. Communicating actionable, valuable insights with storytelling helps folks better understand data context and complexity in a short time. An example of presenting data that tells a story is Steve Wexler's story of STD, HIV and AIDS rates in Texas and how those trends have changed over time. See: http://bit.ly/qCpGai.
In data science, pairing different data sets will often provide valuable insights. For example, integrating genetic data, body sensor data and electronic health records can tell a story that both physicians and patients understand. Another example of data storytelling regarding health entitlement spending is told in six (6) charts @ http://bit.ly/12hh5sR.
The future of communicating data science is not in long memos full of equations and fancy jargon, or in extensive whitepapers, but in shorter storytelling format using data visualization to make data science results understood easily and quickly.