When it comes to the importance of communicating the results of data analysis, the case of Gregor Mendel, the scientist who discovered the concept of genetic inheritance, provides a dire warning for business.
Mendel’s findings weren’t accepted until long after his death because he only published his findings in an obscure scientific journal, notes Thomas Davenport, Harvard Business School professor and author of several books on analytics.
“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,” Davenport notes.
“Managers do not need to become quant jocks,” he adds. “But . . . 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.
But what are the best practices to communicate the value of big data and the insights distilled from analytics?
Data alone has no value. Big data includes a lot of unstructured data, data that doesn’t fit into a database, such as social media comments, likes, texts and online browsing patterns. Finding the relevance within this data deluge is where the payback from data analytics is greatest, he notes.
Litmus test: Does the analysis provide value? If data analysis is not effectively folded into a strategic plan, it can lead to missed opportunities and even increased organizational risk.
“Waste, fraud, bribery, abuse – these represent significant problems for many organizations, especially large, global ones,” Williams says. “Limiting these exposures can translate to more profitable operations. A credit card company that could reduce fraud, waste and abuse by even one percent of charged volume could significantly improve its bottom line.”
Invest in data scientists. The best ROI from data analysis comes from taking large reams of data and predicting information – sales forecasts, consumer behavior and business trends. But building the models to capitalize on the promise of predictive analytics requires investing in data scientists.
“Big math requires specialized, analytical talent – data scientists with creativity and judgment, along with deep statistical and computer science knowledge,” William notes. “Building bench strength in this area should be on every CIO’s talent agenda.”
Start with what you have. While some executives may struggle to successfully mine the data they have, many still are charging ahead with attempts to analyze or acquire much larger data sets.
“Get to know your data close up, and master the process of finding insights within it that support your organization’s mission and goals,” according to Williams. “The information you already own may be all that’s needed. That’s an important message for your CEO to hear.”
In addition, start with a few small problems that matter most to the CEO and other stakeholders and solve them, ensuring that scope creep doesn’t impede the success of these smaller projects. Consider running pilots to garner early wins, he adds.
Follow data’s lead. Big data cannot be considered a strategy; instead, ensure that the problems you’re trying to solve are at the center of a broader, analytical strategy.
“A narrow search for precise, predefined outcomes may cause you to miss the big aha!” Williams says. “Big data has hidden in it top- and bottom-line results, but it’s folly to precisely plan for those results – or worse, justify in advance investments based on hoped for outcomes.”
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