Operationalizing Data and Analytics
First and foremost, organizations will need to cultivate an analytical workforce.
"Executives need to find ways to encourage their workforce to be more analytical in their decision making," says Brad Fisher, D&A leader for KPMG in the U.S. "From there, organizations should quickly start to see analytics driving their overall people performance, their operational performance, their customer performance and their selling performance."
Likewise, organizations need to break down their data silos to get an enterprise-wide, 360-degree view of their data, whether it resides inside or outside the organization. And that is not necessarily a technology play.
"The challenge is to leverage your internal data and your big data by using analytics to drive the systems," says Eddie Short, EMA leader for D&A at KPMG. "But the reality is that operationalizing D&A does not necessarily mean new systems and tools; rather it is about blending old-school business intelligence with new-world big data and analytics to drive both your legacy and your emerging IT strategies."
Perhaps most important, the key to successfully undertaking any data and analytics initiative is to start by asking yourself exactly what you're trying to achieve.
"Zeroing in on the business problems and identifying key hypotheses may not be the easiest thing to achieve up front, but it is far more efficient and effective in the longer term," says Anthony Coops, partner, KPMG in Australia. "Throwing all available data into a pot and hoping for a tasty stew of insights will rarely - if ever-deliver meaningful results."
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