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Introduction to Business Analytics

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Dr. Hokey Min offers a practical, easy-to-understand introduction to business analytics including: the origin and evolution; developing analytical thinking; operationalizing Big Data from global perspectives; extracting useful information from Big Data; unique challenges for business analytics; capitalizing on business analytics for building a winning global strategy.

This chapter is from the book

1.1 The Origin and Evolution of Business Analytics

In the era of knowledge economy, getting the right information to decision makers at the right time is critical to their business success. One such attempt includes the growing use of business analytics. Generally speaking, business analytics refers to a broad use of various quantitative techniques such as statistics, data mining, optimization tools, and simulation supported by the query and reporting mechanism to assist decision makers in making more informed decisions within a closed-loop framework seeking continuous process improvement through monitoring and learning. Business analytics also helps the decision maker predict the future business activities based on the analysis of historical patterns of past business activities. For example, your nearby grocery chain, such as Kroger, might frequently issue discount coupons tailored for each customer based on his past shopping patterns. This practice encourages the customer to consider buying the discounted but favorite items repeatedly, while building customer loyalty. This practice is possible, since a smart use of business analytics allows the grocery store to figure out which items are likely to be purchased by which customer in his next grocery shopping trip. Likewise, application potentials of business analytics are enormous given the abundant data available from the digital and mobile data sources.

Although business analytics has been rapidly gaining popularity among practitioners and academicians alike in the recent past, its conceptual foundation has existed for centuries. One of the first forms of business analytics may be statistics whose uses can be traced back at least to the biblical times in ancient Egypt, Babylon, and Rome. Regardless of historical facts, its longevity may be attributed to its usefulness for helping the policy maker (including ancient rulers or kings) make a better decision. In other words, whatever the form of business analytics may be, it would help us answer the following fundamental questions critical for decision making:

  1. What happened?

    • What did the data tell us?
  2. Why did a certain event take place?

    • Why did it happen?
    • What are the sources of problems?
  3. Will the same event take place?

    • Will the problem recur?
    • Are there any noticeable patterns of the problem?
  4. What will happen if we change what we used to do?

    • How can we deal with the recurring problem?
    • What is the value the change will bring?
  5. How can we ensure that our changed practices actually work?

    • Is there scientific evidence indicating the validity and usefulness of our changed practices?

By answering the preceding questions, business analytics aims to accomplish these various goals:

  • Gaining insights into business practices and customer behaviors: Business analytics is designed to transform unstructured, nonstandardized big data originated from multiple sources into meaningful information helpful for a better business decision.
  • Improving predictability: By deriving insights into customer behavioral patterns and market trends, business analytics can improve the organization’s ability to make demand forecast more accurately.
  • Identifying risk: With growing complexity and uncertainty resulting from the globalization of business activities, many organizations encounter the daunting tasks of managing risk. Risk cannot be managed without identifying it and then preparing for it. Business analytics can function as an early warning system for detecting the signs or symptoms of potential troubles by dissecting the business patterns (e.g., shrinking market share, a higher rate of customer defection, declining stock price).
  • Improving the effectiveness of communication: With the query and reporting mechanism of business analytics, it can not only speed up the reporting procedures, but also provide user-friendly reports including “what-if” scenarios. Such reports can be a valuable communication tool among the decision makers and thus would help the management team make more timely and accurate business decisions.
  • Enhancing operating efficiency: By aiding the decision maker in understanding the way business works and where the greatest business opportunities are, business analytics can decrease the chances of making poor investment decisions and misallocating the company’s resources and thus would help improve the company’s operating efficiency.
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