- Is There a Difference Between Analytics and Analysis?
- Where Does Data Mining Fit In?
- Why the Sudden Popularity of Analytics?
- The Application Areas of Analytics
- The Main Challenges of Analytics
- A Longitudinal View of Analytics
- A Simple Taxonomy for Analytics
- The Cutting Edge of Analytics: IBM Watson
- References
This chapter is from the book
This chapter is from the book
This chapter is from the book
A Simple Taxonomy for Analytics
Because of the multitude of factors related to both the need to make better and faster decisions and the availability and affordability of hardware and software technologies, analytics is gaining popularity faster than any other trends we have seen in recent history. Will this upward exponential trend continue? Many industry experts think it will, at least for the foreseeable future. Some of the most respected consulting companies are projecting that analytics will grow at three times the rate of other business segments in upcoming years; they have also named analytics as one of the top business trends of this decade (Robinson et al., 2010). As interest in and adoption of analytics have grown rapidly, a need to characterize analytics into a simple taxonomy has emerged. The top consulting companies (e.g., Accenture, Gartner, and IDT) and several technologically oriented academic institutions have embarked on a mission to create a simple taxonomy for analytics. Such a taxonomy, if developed properly and adopted universally, could create a contextual description of analytics, thereby facilitating a common understanding of what analytics is, including what is included in analytics and how analytics-related terms (e.g., business intelligence, predictive modeling, data mining) relate to each other. One of the academic institutions involved in this challenge is INFORMS (Institute for Operations Research and Management Science). In order to reach a wide audience, INFORMS hired Capgemini, a strategic management consulting firm, to carry out a study and characterize analytics.
The Capgemini 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 is that executives see analytics as a core function of businesses that use it. It spans many departments and functions within organizations, and in mature organizations, it spans the entire business. The study identified three hierarchical but sometimes overlapping groupings for analytics categories: 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 prescriptive analytics, the top level in the analytics hierarchy. 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 1.3 shows a graphical depiction of the simple taxonomy developed by INFORMS and widely adopted by most industry leaders as well as academic institutions.
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Figure 1.3 A Simple Taxonomy for Analytics
Descriptive analytics is the entry level in analytics taxonomy. It is often called business reporting because of the fact that most of the analytics activities at this level deal with creating reports to summarize business activities in order to answer questions such as “What happened?” and “What is happening?” The spectrum of these reports includes static snapshots of business transactions delivered to knowledge workers (i.e., decision makers) on a fixed schedule (e.g., daily, weekly, quarterly); dynamic views of business performance indicators delivered to managers and executives in a 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 his or 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 business intelligence (BI), and predictive and prescriptive analytics are collectively called advanced analytics. The logic here is that moving from descriptive to predictive and/or prescriptive analytics is a significant shift in the level of sophistication and therefore warrants the label 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 (to some extent, it still is in some business circles) until the arrival of the analytics wave. BI is the entrance to the world of analytics, setting the stage and paving the way toward more sophisticated decision analysis. 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 descriptive analytics in the three-level analytics hierarchy. Organizations that are mature in descriptive analytics move to this level, where they look beyond what happened and try to answer the question “What will happen?” In the following chapters, we will cover the predictive capabilities of these analytics techniques in depth as part of data mining; here we provide only a very short description of the main predictive analytics classes. Prediction essentially is the process of making intelligent/scientific estimates about the future values of some variables, like customer demand, interest rates, stock market movements, etc. 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 courses of action—that are usually created/identified by predictive and/or descriptive analytics—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 constitute prescriptive analytics were developed during and right after World War II, when there was a dire need for a lot with limited resources. Since then, some businesses have used these models for some very specific problem types, including yield/revenue management, transportation modeling, scheduling, etc. The new taxonomy of analytics has made them popular again, opening their use to a wide array of business problems and situations.
Figure 1.4 shows a tabular representation of the three hierarchical levels of analytics, along with the questions answered and techniques used at each level. As can be seen data mining is the key enabler of predictive analytics.
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Figure 1.4 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. Effectiveness of business analytics systems, no matter the 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 to be smart about selecting and implementing analytics capabilities to efficiently navigate through the maturity continuum.