Why Understanding the Breadth of Analytics is Crucial
Using quantitative methods is rapidly becoming not an option for competitive advantage, but rather – at the very least – barely enough to keep up. Everyone needs to understand what’s involved in analytics, what your particular organization needs, and how to do it.
Few people are comfortable with the concepts of advanced analytic methods. In fact, most people cannot explain the difference between a mean, a median and a sample mean. The misapplication of statistics is widespread, but today’s explosion of data sources and intriguing technologies to deal with them have changed the calculus. Embedded quantitative methods may relieve analysts of the actual construction of predictive models, but applying those models correctly requires an understanding of the different analytical types, roles and skill.
“Analytics” is a critical component of enterprise architecture capabilities, though most organizations have only recently begun to develop experience using quantitative methods. As Information Technology emerges from a scarcity-based mentality of constrained and costly resources to a commodity consumption model of data, processors and tools, analytics is quickly becoming table stakes for competition.
This report is the first of a two-part series. (Part II will cover analytic functionality and matching the right technology to the proper analytic tools and best practices.) It discusses the importance of understanding the role of analytics, why it is a difficult topic for many, and what actions you should take. It will explore the various meanings of analytics, provide a framework for aligning various types of analytics with associated roles and skill sets needed.
