The Data that Explains Why the Other Data Happened
Big Data is the buzz in business. Big Data is the hot topic in HR circles. It is the hot topic in corporate boardrooms. There are reams of articles projecting the promise of Big Data, predicting how it will transform the competitive landscape of the business world.
“Big Data: The next frontier for innovation, competition, and productivity” “The era of agile analytics and the predictive enterprise is here.” “Big Data can play a significant economic role to the benefit not only of private commerce but also of national economies and their citizens.” “The use of Big Data will become a key basis of competition and growth for individual firms.” “Big Data can generate significant financial value across sectors of the economy.”
Without a doubt, , Big Data has the potential to do all of these things. However, there is a far more fundamental set of data that effectively drives all of the actions, events and decisions captured and analyzed by Big Data. People drive the actions; cause the events; and make the decisions. How and why they do the things they do depends on how they think, learn and behave. That, in turn, depends on hard-wired traits and abilities that do not change over time or with coaching or training. They are the core operational strengths of each individual. When those are clearly understood, it is possible to understand what affects the Big Data, and understanding that, it is possible to strategically and tactically shape the course of actions, events and decisions to achieve the goals of the organization. That is the Biggest DATA.
NOTE : The all capitalized DATA refers to the information available from BestWork, a unique set of psychometric instruments that accurately measures the hard-wired personality traits and cognitive abilities of people. This DATA is then translated into easily understood descriptions of how those hard-wired strengths and abilities affect the thinking, learning and behaviors of the people.
Consider these examples:
Transitioning from product sales to solution sales
A Big Data analysis of customer trends indicates a movement toward buying more integrated solutions rather than separate elements. This means that the sales strategies must evolve to a more complex solutions-oriented strategy. The pivotal question is whether or not the current sales force can execute that strategy. Certainly training will be needed, but even with the training, can the current team deliver the new kind of sale? The Biggest DATA can answer that question. The DATA can identify who can make that transition and who cannot. The DATA can then be used to craft the most effective training program for the new team, and it can make recommendations on how to redeploy those incumbent salespeople who are not suited to the new strategy.
Managing change across a large retail chain
The Big Data from a thousand retail stores has revealed two significant opportunities for increasing efficiency and profitability. One is a procedural change in managing inventory and the other involves redefining the roles within the stores. The financial impact of the proposed changes is substantial, but so too, is the scope of change that is required from virtually all employees in the stores. The Biggest DATA shows the change agility of each employee. This DATA is aggregated into individual stores, the various regions and the total national population in the stores. This DATA describes how easy or how difficult it will be for each store to make the necessary changes. Using the DATA, management can work with their HR team to roll out the changes strategically. The most agile stores lead the way, building momentum and collecting success stories. The second group follows with a bit more preparation and support. Lastly, the least agile stores are engaged, but only after solid experience and a track record of successes. They also have more support and training to make the transition.
Mergers & Acquisitions
An article in the Harvard Business Review stated that studies put the failure 1 rate for mergers and acquisitions at between 70% and 90%. The principal problem is rarely that someone misread the financials or that the capital equipment and other resources of the company were miscounted. The problem usually centers around people. It was thought that the people from Company A could change to Company B’s way of doing things easily, but it turned out to be hard. It was thought that the merger would take 90 days, but after a year, things were still not together. It was thought that Company A’s “smart” and “effective” team would be just as “smart” and “effective” as part of Company B, but it was more like converting the baseball team from Florida into a hockey team in Minnesota. It was the variability of the the people part of the equation that caused the failures.
In a DATA world, there would be an inventory of Company A and of Company B. The change agility of Company A would be known and reasonable timelines projected with the necessary interventions planned. Perhaps the “smart” and “effective” team of Company A would be seen to be quite different than that of Company B. Maybe that unbalances the equation and rules out the M & A. Maybe the information enables Company B to map an alternate plan. All of these options are readily available with the Biggest DATA, since all of these elements become objectively visible. The DATA can be analyzed for the whole enterprise, a specific part of the enterprise, a projected combination of two parts and even down to the individual level. Beyond that, various management combinations can be analyzed, and the best one identified for whatever tactical or strategic strategies are necessary.
Why is this Biggest DATA appearing now? What changed? Technology has opened up unimagined resources for acquiring data. X-rays gave doctors information that enabled them to treat patients more effectively. Today, MRI’s make X-rays seem primitive, and the level of detailed information they offer has revolutionized health care. A similar quantum leap has taken place in the world of psychology and psychometrics. Psychology has long established that people have hard-wired personality traits that are stable and measurable. These traits form the foundation of all human behaviors. Unfortunately, the methods for measuring these traits have never been easily accessible for businesses. Clinical instruments provided sound data but it required interpretation by experts. This limited its use to relatively straightforward hiring decisions. The promise of applying this type of data to businesses on a wider scale spawned a plethora of simplistic personality tools. These were generally based on early theories such as DISC or Myers-Briggs, offering at best, an introduction to personality differences among individuals. Between the extremes of elementary team building exercises and hiring applications, there was little to offer business executives that could truly impact profitability.
The other shortfall for the vast majority of these would-be business tools was the lack of cognitive measurement. For far too long, there has been a paralyzing misunderstanding about intelligence, based on IQ theories and school grading systems. This led people to roughly sort people into two limiting categories: smart and not so smart. The general assumption was that smart was good for everything and smart people could do most anything. People were hired for smart. People were promoted for smart. But smart did not always work out as planned. Cognitive science has a much clearer understanding of intelligence now. The IQ model is not completely wrong, but it is “wrong” enough to be disastrous to some decisions.
Grades in school can also be misleading. A variety of hard-wired characteristics affect a student’s ability to learn in addition to intelligence. A hard working, careful and methodical learner can do exceptionally well in school yet struggle in many jobs. Speed of learning or speed of processing information is a much more meaningful metric for understanding job performance or performance in schools. Given sufficient time, most people can learn most things. However, time in the the business world is limited. In some jobs, there is a premium on speed of processing. In other jobs, that same speed can be a weakness, with a much slower speed being essential for success. Leading businesses and HR professionals are starting to recognize that smart has a lot to do with how well the strengths of the individual or the strengths of the team match the operational strengths needed to execute the operational strategies of the enterprise.
The business world has always been competitive. The dynamic nature of the Internet and the very technology that birthed Big Data have accelerated and intensified that competition. Success demands performance and the agility to adapt to changes in the marketplace quickly and decisively. Lack of information can compromise a company’s agility regardless of the inherent potential of its products and services and regardless of the talent of its employees. It is a game in which the player with the most agile and actionable DATA wins. Big Data can show the way. It can detect opportunities. It can suggest strategies. Big Data can indeed be a powerful ally, but it is the Biggest DATA, the people DATA, that will carry the day.
1 Clayton Christensen, Richard Alton, Curtis Rising and Andrew Waldeck, “The Big Idea: The New M & A 1 Playbook,” Harvard Business Review (March 2011)
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