Analytics is currently treated as an emerging profession that comes from a convergence of techniques rooted in the fields of statistics, operations research, industrial engineering, computer science, as well as the fields of psychology and decision analysis. A leading professional body, INFORMS, defines analytics as: “The scientific process of transforming data into insight for making better decisions” (Robinson, 2012). We can treat analytics as an emerging profession because the body of knowledge required for analytics has become extensive, and business people have started to designate teams and departments as being specialists in analytics. An ecosystem of service providers has evolved for this profession, including conferences, degrees, professional consulting services, certifications, etc. Analytics technologies support algorithms for forecasting, optimization, visualization, etc. using techniques such as linear regression, machine learning, design of experiments, simplex, queuing, simulation, etc. We should also include Business Intelligence (BI) and Data Warehousing (DW) under the umbrella of analytics technologies. Analytics is best understood as a business asset that is used to improve decision-making and execution. This chapter outlines the analytics landscape and aims to help organizations gain a shared understanding of issues that must be addressed to plan, build and use the analytics asset.

We can treat analytics as a multi-discipline profession because the body of knowledge required for analytics has become extensive, and businesspeople have started to designate teams and departments as being specialists in analytics. An ecosystem of service providers has evolved for this profession, including conferences, degrees, consulting services, certifications, etc. Analytics is best understood as an organizational asset that is used to improve decision making and execution. This chapter outlines the analytics landscape and aims to help organizations gain a shared understanding of issues that must be addressed to plan, build, and use the analytics asset.