200 words agree or disagree Business Analytics is not a

200 words agree or disagree 

 Business Analytics is not a new concept, but how it is employed and executed is. What a single employee is able to accomplish at his/her terminal using a myriad of calculation tools is exponentially vaster than at any previous time in history. As we as a class progress through our graduate program the tools available and the capabilities possessed will no doubt increase even further. Before reading this week’s lesson notes, I was gravitated towards developing a post centered on the math. Focusing in on the “analysis” of what the processes are.  However after reading through the readings and articles from numerous other entities, “analytics” as is now being termed is an all-encompassing descriptor.  Analytics is no longer just the gathering and interpreting of what was, but rather is also geared towards what is to become. Brian Eastwood of Northeastern University defines Enterprise Analytics as “the use of data, analysis, and exploratory and predictive modeling to drive business strategies and actions. Successful enterprise analytics requires applying the techniques of data management, data engineering, and strategy development, as well as the use of analytics techniques that range from forecasting and simulation to linear programming and optimization. 

                There are three main types of analytics; predictive, descriptive, and prescriptive. Utilizing all three across a platform is an organizational challenge experienced by all. Descriptive tells you what is happening or has happened. Predictive is attempting to forecast what will happen next, and prescriptive attempts to find multiple outcome by changing different variables within the process or procedure. How does this relate to investment? Businesses spend large amounts of capital in these three areas attempting to optimize every step of their processes. Maximum efficiency is the aim, as an increase in efficiency is directly related to an increase in the bottom line.  As stated before the analytics is not new, but the how is. This “how” requires the implementation of new teams of individuals with different skill sets and knowledge bases. The return on this expenditure is not in the immediate short term. The efficiency increase may at first be marginal, but just like compound interest, continual marginal improvements will prove to be extremely profitable.  

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