A Simple Taxonomy for Analytics
Because of the multitude of factors related to both needing to make better and faster decisions as well as the availability and affordability of hardware and software technologies, analytics is gaining popularity faster than any trends we have seen in recent history. Will this upward exponential trend continue? Many industry experts think that it will, at least for the foreseeable future. Some of the most respected consultancy companies project analytics to grow three times the rate of other business segments in upcoming years, naming analytics as one of the top business trends of this decade (Robinson, Lewis, and Bennett, 2010). As the interest and adoption of analytics have grown rapidly, a need to characterize analytics into a simple taxonomy has emerged. Along with the top consultancy companies (Accenture, Gartner, IDT, among others), several technologically oriented academic institutions embarked on a mission to create a simple taxonomy for analytics. Such taxonomy, if developed properly and adopted universally, could create a contextual description of analytics, thereby facilitating a common understanding of what analytics is, what is included/excluded in analytics, and how analytics-related terms such as business intelligence, predictive modeling, and data mining would relate to each other. One of the academic institutions that took this challenge was the Institute for Operations Research and Management Science (INFORMS). To reach a wide audience, INFORMS hired Capgemini, a strategic management consulting firm, to carry out the study of characterizing analytics.
The study produced a concise definition of analytics: “Analytics facilitates realization of business objectives through reporting of data to analyze trends, creating predictive models for forecasting and optimizing business processes for enhanced performance.” As this definition implies, one of the key findings from the study was that analytics is seen (by the executives inquired from a wide range of industries) as a core function of businesses that use it and spans many departments and functions within organizations and, in mature organizations, the entire business. As far as identifying the main categories of analytics is concerned, the study identified three hierarchical but sometimes overlapping groupings: descriptive, predictive, and prescriptive analytics. These three groups are hierarchical in terms of the level of analytics maturity of the organization. Most organizations start with descriptive analytics, then move into predictive analytics, and finally reach the top level in analytics hierarchy: prescriptive analytics. Even though these three groupings of analytics are hierarchical in complexity and sophistication, moving from a lower level to a higher level is not clearly separable. That is, a business can be in the descriptive analytics level while at the same time using predictive and even prescriptive analytics capabilities, in a somewhat piecemeal fashion. Therefore, moving from one level to the next essentially means that the maturity at one level is completed and the next level is being widely exploited. Figure 1.4 shows a graphical depiction of the simple taxonomy developed by INFORMS and widely adopted by most industry leaders as well as academic institutions.
FIGURE 1.4 A simple taxonomy for analytics.
Descriptive analytics is the entry level in analytics taxonomy. It is often called business reporting because most of the analytics activities at this level deal with creating a report to summarize business activities to answer the question “What happened?” or “What is happening?” The spectrum of these reports includes static snapshots of business transactions delivered to knowledge workers (decision-makers) on a fixed schedule; dynamic views of business performance indicators delivered to managers and executives in an easily digestible form—often in a dashboard-looking graphical interface—on a continuous manner; and ad-hoc reporting where the decision-maker is given the capability of creating her own specific report (using an intuitive, drag-and-drop graphical user interface) to address a specific or unique decision situation.
Descriptive analytics is also called BI, and predictive and prescriptive analytics are collectively called advanced analytics. The logic behind calling part of the taxonomy advanced analytics is that moving from descriptive to predictive or prescriptive is a significant shift in the level of sophistication, which warrants the label of “advanced.” BI has been one of the most popular technology trends for information systems designed to support managerial decision-making since the start of the century. It was popular until the arrival of the analytics wave. (To some extent, it still is popular in certain business circles.) Analytics characterizes BI as the entrance level to the world of analytics, setting the stage and paving the way toward more sophisticated decision analysis. These descriptive analytics systems usually work off a data warehouse, which is a large database specifically designed and developed to support BI functions and tools.
Predictive analytics comes right after the descriptive analytics in the three-level analytics hierarchy. Organizations that are matured in descriptive analytics move into this level, where they look beyond what happened and try to answer the question “What will happen?” As we will cover the predictive capabilities of these analytics techniques in depth in the following chapters as part of data mining, herein we provide only a short description of main prediction classes. Prediction essentially is the process of making intelligent/scientific estimates about the future values of some variables like customer demand, interest rates, and stock market movements. If what is being predicted is a categorical variable, the act of prediction is called classification; otherwise, it is called regression. If the predicted variable is time-dependent, the prediction process is often called time-series forecasting.
Prescriptive analytics is the highest echelon in analytics hierarchy. It is where the best alternative among many—that are usually created/identified by predictive or descriptive analytics—courses of action is determined using sophisticated mathematical models. Therefore, in a sense, this type of analytics tries to answer the question “What should I do?” Prescriptive analytics uses optimization, simulation, and heuristics-based decision-modeling techniques. Even though prescriptive analytics is at the top of the analytics hierarchy, the methods behind it are not new. Most of the optimization and simulation models that constituted prescriptive analytics were developed during and right after World War II in the 1940s, when there was a dire need to do the best and the most with limited resources. Since then, prescriptive analytics has been used by some businesses for specific problem types, including yield/revenue management, transportation modeling, and scheduling. The new taxonomy of analytics made it popular again, opening its use to an array of business problems and situations.
Figure 1.5 shows the progressive nature of the three hierarchical levels of analytics along with the questions answered and techniques used at each level. As can be seen, the main subject of the book, prescriptive analytics is the top most layer in the analytics hierarchy, one that is closest to the decision being made.
FIGURE 1.5 Three levels of analytics and their enabling techniques.
Business analytics is gaining popularity because it promises to provide decision-makers with information and knowledge that they need to succeed. The effectiveness of business analytics systems, no matter what level in the analytics hierarchy, depends largely on the quality and quantity of the data (volume and representational richness); the accuracy, integrity, and timeliness of the data management system; and the capabilities and sophistication of the analytical tools and procedures used in the process. Understanding the analytics taxonomy helps organizations be smart about selecting and implementing analytics capabilities to efficiently navigate through the maturity continuum. Following is a case study that shows the magnitude of impact that can be obtained from proper implementation of a large-scale analytics project.