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By Ken Harris and Nicole Dvorak, Cadent Consulting Group, LLC

There has been a lot written about Big Data in recent months – perhaps to the point of overkill. As one industry expert observed when he said of Big Data — with apologies to Sir Winston Churchill — “…rarely in the course of human existence has so much buzz been created by so many, to be used by so few with so little to show for it.”

The antidote to the Big Data hype is using it to generate real, useful answers. The way to realize these answers is through advanced analytics and practical insight. The term “advanced analytics” has not achieved the same high-profile status as Big Data, but think of it as the way to tame the prolific data beast that Big Data creates.

To that end, understanding advanced analytics and creating insight requires love, common sense and big thoughts.

All You Need is Love

Advanced analytics is a lot like love; you know it when you see it but it can get complicated, and sometimes it’s hard to understand. Advanced analytics lives in the answers that are generated when a pattern of behavior is discovered and attribution is applied to the facts leading to insight. It can become complicated because there is no road map to certainty on the “whys” even if the “what” is clear. Practical experience is critical to interpreting the meaning of the data patterns. Teasing out pertinent trends and relevant metrics requires trained statisticians and knowledgeable interpreters of data that are detail-oriented, but are also able to think strategically and consider the big picture.

Focus on Common Sense

In more concrete terms, advanced analytics is often executed by statisticians and interpreted by experts to predict, optimize and forecast outcomes with the intent of discovering insights to make better decisions. Savvy manufacturers are taking this approach to inform their strategies and go-to-market plans. For instance, one commodity manufacturer harnessed its historical sales data to hone its price spread, brand share and retailer profit tradeoff understanding. The company worked with a supplier to develop a model that identified the pattern between the price spread of its products, brand share and retailer profit. The larger the price spread the greater the consumer trade-down; the smaller the price spread the greater the profit for the company and the retailer. The company took into account multiple factors such as time of year, competitor prices, inventory levels, retailer size and category trends to develop the common sense approach to the model.

The answers provided by advanced analytics are only useful if they can be translated into insights and ultimately, action. In the commodity model example, the pure analytics delivered complex algorithms and statistics. The first step was to visualize the data into an easy to read chart. The second step was to create an easy-to-use matrix to operationalize the concept for use with retailers. The goal was to show the retailers where they were plotted on a simple matrix and have them understand action steps needed to maximize profit. The company was able to increase its topline sales by providing simple, clear direction to retailers. The bottom line? The results from complex mathematical models are by themselves inadequate; they must be translated into practical application to be useful.

Explain Big Thoughts

Sometimes, practical application involves more than just a user-friendly matrix; it requires strategic positioning and forethought. In another example, a chemical company optimized its return on trade investment by using advanced analytics techniques to identify key trade investment levers for its top-selling products. But rather than immediately implementing the trade levers with the highest ROI, the company considered the strategic role played by each of the products in its portfolio. Products that generated positive brand sentiment were also contributing to incremental purchases beyond the immediate sale. These products acted as a gateway purchase to additional brand purchases in months, or even years later. The initial findings identified by the Big Data analytical model were powerful, but bringing them to life in a narrative selling story that explained the texture and context of the data was essential to gain maximum effect and provide the illuminating answers to otherwise unsolved questions.

The Path to Big Answers

It’s easy to miss seeing the Big Data forest through the analytical trees. Harnessing the power of advanced analytics starts by moving down a simple path to success.

First, begin by asking the right questions. Identify the specific decisions or problems that advanced analytics will address, such as how to optimize price spread, how to best allocate trade, etc.

Second, identify the data sets required to find the answers. This means employing the expertise either internally or externally to slice the data in ways that will produce the right answers.

Third, apply experience to interpret the data to make sure that both strategic and tactical implications of the findings are being addressed and simplified for maximum use — in a chart, a matrix or a selling story.

Finding meaningful answers from Big Data is complex, like love. Following a path created by advanced analytics to develop a common sense approach, and using big thoughts and experience to explain the strategic and tactical implications of the answers, can create a successful path to leverage the potential that Big Data promises.

Ken Harris is managing partner and Nicole Dvorak is a senior business analyst for Cadent Consulting Group, LLC.

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