In part one of this series on Data Driven, How performance analytics delivers extraordinary sales results we introduced you to its author, Jenny Dearborn, a leading authority on sales enablement and training. In this installment, we want to dig a little deeper into data analytics, what it is and what forms it takes.
According to Dearborn, many companies face a common problem—the lack of data to solve problems. There is data to be sure, but too often it remains potential in search of a purpose. Although ample data is often in hand, it sometimes isn't used to advantage. For example, using data in an effort to justify departmental efficiency only masks what may be the real target—sales effectiveness.
The technology to generate data is moving faster than the understanding of how to maximize its use or even the understanding of what that may look like. Plus, this data revolution is complicated by all the other hallmarks of similar groundswells—hype, lack of standardization in terminology and the over promises of those peddling quick fixes.
Nonetheless, big data will reshape how business gets done. To prove the point, Dearborn quotes authorities from Bersin by Deloitte, Microsoft Research and The Economist. And then offers this capstone: "Given these clear statements that big data with its life-changing potential is here to stay, any substantial company that doesn't take advantage of its own geobytes of available data is in danger of eventual extinction." (1)
Lisa Arthur agrees. Writing for Forbes, Arthur offers this working definition: "Big data is a collection of data from traditional and digital sources inside and outside your company that represents a source for ongoing discovery and analysis." She prefaces the definition by admitting that she expects individual companies to tweak it here and there.
"One thing is clear," Arthur writes. "Every enterprise needs to fully understand big data – what it is to them, what is does for them, what it means to them –and the potential of data-driven marketing, starting today. Don't wait. Waiting will only delay the inevitable and make it even more difficult to unravel the confusion."
Dearborn leads readers through four types of data analytics—the process of turning raw data into useable data. They are descriptive, diagnostic, predictive and prescriptive.
"Descriptive analytics asks the question, 'What has happened?' It is the most common type of analytics used by organizations. By mining data to provide trending information on past or current events, it provides decision-making guidance for future actions, often in the form of key performance indicators.
"Diagnostic analytics asks the question, 'Why has this happened?' By utilizing statistical and analytical techniques to identify relationships in data sets and degrees of correlation between variables, it helps pinpoint the causes of problems and formulate corrective solutions.
"Predictive analytics asks the question, 'What could happen?' The term encompasses a variety of techniques, such as statistics, modeling, machine learning, and data mining, which are used for finding correlations within big sets of current and historical facts, in order to make useful predictions about future events.
"Prescriptive analytics asks the question, 'What should we do?' It explores a set of possibilities and suggests optimal course(s) of action based on descriptive and predictive analyses of complex data. Utilizing advanced analytical and mathematical models, it can also provide reasons for its recommendations and possible implications of following them." (2)
As you can imagine, when data starts to paint a highly detailed picture of performance at the individual rep level, the implications for performance coaching are enormous. That's the subject of part three.
(1) Dearborn, Jenny. Data Driven. Hoboken, NJ: John Wiley & Sons, Inc., 2015. Pp. 40-41.
(2) Ibid, p. 46-47.
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