Real-World Data Mining: Introduction to Analytics
- 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
Business analytics is a relatively new term that is gaining popularity in the business world like nothing else in recent history. In general terms, analytics is the art and science of discovering insight—by using sophisticated mathematical models along with a variety of data and expert knowledge—to support solid, timely decision making. In a sense, analytics is all about decision making and problem solving. These days, analytics can be defined as simply as “the discovery of meaningful patterns in data.” In this era of abundant data, analytics tends to be used on large quantities and varieties of data. Although analytics tends to be data focused, many applications of analytics involve very little or no data; instead, those analytics projects use mathematical models that rely on process description and expert knowledge (e.g., optimization and simulation models).
Business analytics is the application of the tools, techniques, and principles of analytics to complex business problems. Firms commonly apply analytics to business data to describe, predict, and improve business performance. Firms have used analytics in many ways, including the following:
- To improve their relationships with their customers (encompassing all phases of customer relationship management—acquisition, retention, and enrichment), employees, and other stakeholders
- To identify fraudulent transactions and odd behaviors—and, in doing so, saving money
- To enhance product and service features and their pricing, which would lead to better customer satisfaction and profitability
- To optimize marketing and advertising campaigns so they can reach more customers with the right kind of message and promotions with the least amount of expense
- To minimize operational costs by optimally managing inventories and allocating resources wherever and whenever they are needed by using optimization and simulation modeling
- To empower employees with the information and insight they need to make faster and better decisions while they are working with customers or customer-related issues
The term analytics, perhaps because of its rapidly increasing popularity as a buzzword, is being used to replace several previously popular terms, such as intelligence, mining, and discovery. For example, the term business intelligence has now become business analytics; customer intelligence has become customer analytics, Web mining has become Web analytics, knowledge discovery has become data analytics, etc. Modern-day analytics can require extensive computation because of the volume, variety, and velocity of data (which we call Big Data). Therefore, the tools, techniques, and algorithms used for analytics projects leverage the most current, state-of-the-art methods developed in a variety of fields, including management science, computer science, statistics, data science, and mathematics. Figure 1.1 shows a word cloud that includes concepts related to analytics and Big Data.
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Figure 1.1 Analytics and Big Data Word Cloud
Is There a Difference Between Analytics and Analysis?
Even though the two terms analytics and analysis are often used interchangeably, they are not the same.
Basically, analysis refers to the process of separating a whole problem into its parts so that the parts can be critically examined at the granular level. It is often used when the investigation of a complete system is not feasible or practical, and the system needs to be simplified by being decomposed into more basic components. Once the improvements at the granular level are realized and the examination of the parts is complete, the whole system (either a conceptual or physical system) can then be put together using a process called synthesis.
Analytics, on the other hand, is a variety of methods, technologies, and associated tools for creating new knowledge/insight to solve complex problems and make better and faster decisions. In essence, analytics is a multifaceted and multidisciplinary approach to addressing complex situations. Analytics take advantage of data and mathematical models to make sense of the complicated world we are living in. Even though analytics includes the act of analysis at different stages of the discovery process, it is not just analysis but also includes synthesis and other complementing tasks and processes.