Results Do Not Speak for Themselves: Data Is Worthless If It Is Not Communicated

Yup, if the tree falls in the forest and no one hears it...or if the insight is potentially market-moving but no one sees it...

We are so focused on the knowledge generated by the analysis of big data that we may forget the equally crucial second and third parts of the equation: interpretation and communication.

Having access to the troves of data provides enormous opportunities for identifying new threats and options. But this presumes the organization with the data has hired people who can effectively analyze and then properly interpret what they have discovered. They must also have the ability to communicate what they have uncovered in ways that resonate with the audiences and markets that matter to them.

Unless the insights big data offers address a need or a desire or a fear in the customers/voters for whom it is intended, its value diminishes exponentially, as the following article explains. Generations of managers have been taught that they do not need to know more than the subject matter experts with whom they work, but they do need to be able to ask the right questions in order to draw out the meaning that leads to increases in sales, profits or less monetary forms of buy-in. This is particularly true in a global economy where the cultural presumptions that accompany certain types of knowledge may not be as 'obvious' to some information consumers as it is to others.

Thinking systemically about the sources, uses and impact of data is as important as having the data itself, assuming the purpose is not just to possess it, but to make something happen with it. JL

Tom Davenport comments in Harvard Business Review:

Managers do not need to become quant jocks. But to fill the alarming need most do need to become better consumers of data, with a better appreciation of quantitative analysis and — just as important — an ability to communicate what the numbers mean.
There is a pressing need for more businesspeople who can think quantitatively and make decisions based on data and analysis, and businesspeople who can do so will become increasingly valuable. According to a McKinsey Global Institute report on big data, we'll need over 1.5 million more data-savvy managers to take advantage of all the data we generate.
But to borrow a phrase from Professor Xiao-Li Meng — formerly the Chair of the Statistics Department at Harvard and now Dean of the Graduate School of Arts and Sciences — you don't need to become a winemaker to become a wine connoisseur.

Too many managers are, with the help of their analyst colleagues, simply compiling vast databases of information that never see the light of day, or that only get disseminated in auto-generated business intelligence reports. As a manager, it's not your job to crunch the numbers; but — as Jinho Kim and I discuss in more detail in Keeping Up with the Quants — it is your job to communicate them. Never make the mistake of assuming that the results will "speak for themselves."
Consider the cautionary tale of Gregor Mendel. Although he discovered the concept of genetic inheritance, his ideas were not adopted during his lifetime because he only published his findings in an obscure Moravian scientific journal, a few reprints of which he mailed to leading scientists. It's said that Darwin, to whom Mendel sent a reprint of his findings, never even cut the pages to read the geneticist's work. Although he carried out his groundbreaking experiments between 1856 and 1863 — eight years of painstaking research — their significance was not recognized until the turn of the 20th century, long after his death. The lesson: if you're going to spend the better part of a decade on a research project, also put some time and effort into disseminating your results.
One person who has done this very well is Dr. John Gottman, the well-known marriage scientist at the University of Washington. Gottman, working with a statistical colleague, developed a "marriage equation" predicting how likely a marriage is to last over the long term. The equation is based on a couple's ratio of positive to negative interactions during a fifteen minute conversation on a "difficult" topic such as money or in-laws. Pairs who showed affection, humor, or happiness while talking about contentious topics were given a maximum number of points, while those who displayed belligerence or contempt received the minimum. Observing several hundred couples, Gottman and his team were able to score couples' interactions and identify the patterns that predict divorce or a happy marriage.
This was great work in itself, but Gottman didn't stop there. He and his wife Julie founded a non-profit research institute and a for-profit organization to apply the results through books, DVDs, workshops, and therapist training. They've influenced exponentially more marriages through these outlets than they could possibly ever have done in their own clinic — or if they'd just issued a press release with their findings.
Similarly, at Intuit, George Roumeliotis heads a data science group that analyzes and creates product features based on the vast amount of online data that Intuit collects. For his projects, he recommends a simple framework for communicating about each analysis:

1. My understanding of the business problem
2. How I will measure the business impact
3. What data is available
4. The initial solution hypothesis
5. The solution
6. The business impact of the solution

Note what's not here: details on statistical methods used, regression coefficients, or logarithmic transformations. Most audiences neither understand nor appreciate those details; they care about results and implications. It may be useful to make such information available in an appendix to a report or presentation, but don't let it get in the way of telling a good story with your data — starting with what your audience really needs to know.