Business Analytics

                                                    Business Analytics

Business analytics refers to the practices, skills, applications, and technologies for continuous qualitative investigation and exploration of past performance of the business in order to gain insight, and improve planning. The understanding and new insights developed through business analytics as regards business performance are based on statistical methods and data. Unlike business intelligence, the approach used here is not necessarily focusing only on a set of consistent metric values to scale past business performance and drive planning. However, both primarily rely on statistical methods and data.

Business analytics, therefore, uses data extensively, employing quantitative and statistical analysis. They also use simple predictive modeling and management based on facts to influence decision making. To a greater extent, it is similar to Management Science. Indeed, analytics can be used as human decisions input or in driving entirely automated decisions (Laursen & Thorlund, 2010). Querying, reporting, and alert tools are used to answer questions like how many, how frequent, what happened, what if, and what actions need to be taken. By answering such questions, business analytics helps to predict trends and make best decisions that optimize performance (Jank, 2011).

Depending on the techniques employed and their primary purpose, analytics is divided into categories. Predictive analytics refers to predictive modeling based on machine and statistical learning techniques. On the other hand, descriptive analytics uses historical data obtained from clustering, scorecards, or reporting to give insight. In prescriptive analytics, parameters like simulation and optimization are used in recommending decisions. Lastly, decisive analytics involves visuals modeled by the user to reflect reasoning and, therefore, support human decisions. In light of their significance in the business setting, analytics is widely applied by well performing firms, examples of which are given here. Banks like Capital One use analytics to uniquely identify their customers, one from the other, based on usage, credit risk, and other features. They then use such characteristics to match their customers with appropriate products, depending on what they are offering and what the customers’ preferences are.

Another excellent example of the application of analytics is observed in Harrys, the gaming firm. Here, they are used in customer loyalty programs to give insight into customers’ history, tastes, and frequency. Through them, they are able to, among other things, be informed on customer loyalty to determine decisions that may affect processes like rewarding loyalty. They can predict future trends too. Through analytics, the E and J Gallo Winery carried out a quantitative analysis in predicting the appeal and liking of its wines. Besides, Deere & Company employed a new analytical tool between 2002 and 2005 to optimize inventory. This enabled it to save $1 billion (Jank, 2011).

It is important to note that business analytics has its own shortcomings and challenges. It relies on a relatively large volume of data which must be of high quality. Maintaining this high quality is a challenge as it (the data) has to be integrated and reconciled across different systems before deciding which bits to be made available for use. Some types of data like that of a warehouse may also require a lot of storage space (Bartlett, 2013).

Nevertheless, there has been a big data explosion recently and more sophisticated processing tools have been invented. This implies that professionals and managers have more access to data than never before. There is an opportunity now to make better decisions using this technically generated information. This could increase revenue and decrease cost by building better products. Fraud can be stopped before it happens, and customer experience can be improved (Bartlett, 2013). More companies are now enlightening their employees on Business Analytics.

References

Bartlett, R. (2013). A practitioner’s guide to business analytics: Using data analysis tools to improve your organization’s decision making and strategy. New York: McGraw-Hill.

Jank, W. (2011). Business analytics for managers. New York: Springer.

Laursen, G. H. N., & Thorlund, J. (2010). Business analytics for managers: Taking business intelligence beyond reporting. Hoboken, N.J: Wiley.

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