What are the critical success factors, dos and don’ts, for developing a Global Data Strategy to enhance the application of Advanced Analytic Methods?
Analytics is the bedrock on which robust growth engines can be built. However, the question is how does one drive the analytics agenda in a way that it is pragmatic, effective and scalable? Analytics is a spectrum that ranges from basic “what happened?” analyses to “what can and is about to happen?” Lately, we all want to get to the “high end” stuff but do not have much patience nor tolerance to pay attention to the fundamentals. Advanced analytics at scale can generate growth via predictive applications. However, scaling advanced analytic applications are a challenge primarily due to non-existent, at worse, and poor, at best, coherent data strategies. In a nutshell the do’s and don’ts of a Global Data Strategy are as follows:
-
Have a coherent data strategy
-
Don’t base that strategy on the number of zeroes in the spend line; in my experience I have seen zero covariance between the spend and the quality of the outcome. In fact the more spending power you have the more likely you are to not have a “coherent” approach
-
Cross enterprise data assets need to be built with a “reusable from the get-go mindset.” One should not always need to find a Data SME to figure how to leverage these data assets.
-
Figure out a way for businesses to stand up and own Master Data Management. Often times this is the purview of IT functions and this needs to evolve.
-
Finally, use the pareto approach to data strategies and applying advance analytic solutions