Analytics Magazine Analytics Magazine, November/December 2014 - Page 36

g oal - d rive n a n a ly t i c s established along with target performance and its impact on operations? Without the baseline and target, how will success of the analytic initiative be defined, measured and interpreted? • Ability to affect: Does the organization have the willingness and wherewithal to carry out potential model recommendations? If not, we haven’t passed the “so what” test. It is far less expensive to determine in advance of a modeling objective that the organization “can’t handle the truth.” • Decision culture: Does your company drive more from general leadership experience and feel, or evidence-based decisioning? If the former, is leadership open to letting go of one handlebar and allow a pilot to compete in a series of A/B tests? • Cost of status quo: Referencing back to “Buy-in” at the top of the list, considering the ultimate cost of doing nothing is often what gets leadership off the fence. Leadership need not be analytically literate to appreciate that supporting costly big data initiatives does not make sense unless a more capable and purposeful analytic practice is prepared to leverage it. The information amassed from these and many other strategic and tactical considerations is used to prepare a highly 36 | a n a ly t i c s - m a g a z i n e . o r g tailored analytic project design. The resulting process supports agile model development by functional managers and business practitioners. This is the engine required to generate measurable benefit from big data. Goal-Driven Analytics Will Justify Big Data Until leadership grants analytic teams the six to eight weeks to assess and design tailored analytic processes that will rapidly produce analytic models to support specific business targets, data analysis will continue to be a theoretical practice that produces little more than interesting insights and isolated low-value remedies. The vast majority of companies will remain analytically immature and dysfunctional. This creates a significant competitive opportunity for those who invest in formal strategic assessment and design. Here are the primary takeaways: 1. Don’t wait for big data to stand up. It’s a journey and not a destination. Analytics can start bringing value at any stage of a big data implementation and even help justify further big data investment. 2. Get trained. Seek a vendor-neutral trainer that not only provides methods w w w. i n f o r m s . o r g