Business Analytics Using SAS by Kadre and Venkat: The Best Way To Learn Business Analytics Using S...

Business Analytics Using SAS by Kadre and Venkat:

The Best Way To Learn Business Analytics Using S...: The Best Way To Learn Business Analytics Using SAS Shailendra Kadre, and Venkat Reddy   Feb 1, 2015 Business analytics is all abo...

The Best Way To Learn Business Analytics Using SAS

Shailendra Kadre, and Venkat Reddy  Feb 1, 2015

Business analytics is all about data driven decision making. Many analytical techniques are data intensive and require business decision makers to have an understanding of statistical and various other analytical tools. These techniques invariably require some level of IT and database knowledge. Organizations using business analytics techniques in decision making also need to develop and implement a data-driven approach in their day-to-day operations, planning, and strategy making. However, in a large number of cases, businesses have no other choice but to implement a data-driven decision-making approach because of fierce competition and cost-cutting pressures. This makes business analytics a lucrative and rewarding career choice. This may be the right time for one to enter this field because the business analytics culture is still in its nascent stage in most organizations around the world and is on the verge of exploding with respect to growing opportunities.

The World of Business Analytics 

Business analytics is all about data, methodologies, IT, applications, mathematical, and statistical techniques and skills required to get new business insights and understand business performance. It uses iterative and methodical exploration of past data to support business decisions. Business analytics aims to increase profitability, reduce warranty expenditures, acquire new customers, retain customers, up sell or cross-sell, monitor the supply chain, improve operations, or simply reduce the response time to customer complaints, among others. The applications of business analytics are numerous and across industry verticals, including manufacturing, finance, telecom, and retail. The global banking and financial industry traditionally has been one of the most active users of analytics techniques. The typical applications in the finance vertical are detecting credit card fraud, identifying loan defaulters, acquiring new customers, identifying responders to e-mail campaigns, predicting relationship value or profitability of customers, and designing new financial and insurance products. All these processes use a huge amount of data and fairly involved statistical calculations and interpretations.

Any application of business analytics involves a considerable amount of effort in defining the problem and the methodology to solve it, data collection, data cleansing, model building, model validation, and the interpretation of results. It is an iterative process, and the models might need to be built several times before they are finally accepted. Even an established model needs to be revisited or rebuilt periodically for changes in the input data or changes in the business conditions (and assumptions) that were used in the original model building. Any meaningful decision support system that uses data analytics thus requires development and implementation of a strong data-driven culture within the organization and all the external entities that support it.

Let’s take an example of a popular retail web site that aims to promote an upmarket product. To do that, the retail web site wants to know which segment of customers it needs to target to maximize product sales with minimum promotional dollars. To do this, the web site needs to collect and analyze customer data. The web site may also want to know how many customers visited it and at what time; their gender, income bracket, and demographic data; which sections of the web site they visited and in what frequency; their buying and surfing patterns; the web browser they used; the search strings they used to get into the web site; and other such information.

If analyzed properly, this data presents an enormous opportunity to garner useful business insights about customers, thereby providing a chance to cut promotional costs and improve overall sales. Business analytics techniques are capable of working with multiple and a variety of data sources to build the models that can derive rich business insights that were not possible before. This derived rich fact base can be used to improve customer experiences, streamline operations, and thereby improve overall profitability. In the previous example, it is possible, by applying business analytics techniques, to target the product to a segment of customers who are most likely to buy it, thereby minimizing the promotional costs.

Conventional business performance parameters are based mainly on finance-based indicators such as top-line revenue and bottom-line profit. But there is more to the performance of a company than just financial parameters. Measures such as operational efficiency, employee motivation, average employee salary, working conditions, and so on, may be equally important. Hence, the numbers of parameters that are used to measure or predict the performance of a company have been increased here. These parameters will increase the amount of data and the complexity of analyzing it. This is just one example. The sheer volume of data and number of variables that need to be handled in order to analyze consumer behavior on a social media web site, for instance, is immense. In such a situation, conventional wisdom and reporting tools may fail. Advanced analytics predictive modeling techniques help in such instances.

I and coauthor Venkat Reddy started working on a book titled Practical Business Analytics Using SAS: A Hands-on Guide. We spent a couple years looking at books on business analytics and predictive modeling. Some of them were really good. But most of them were too intense and deep on the theory and mathematics of statistical algorithms, which are an integral part of this subject. Some people like books that take that tack, but most practitioners—even those in the industry—don’t have the deep background in the math required or the interest in learning it. Working professionals, particularly newcomers to the fi eld of business analytics, are not very comfortable with the deep theoretical treatment of statistical algorithms generally provided in most of the books available on analytics. The market need we discerned, therefore, was to simplify the presentation of algorithms for professionals who don’t need to know the details to succeed in their work. Besides, once introduced to the subject, one can always refer to the advanced texts on statistics if such academic rigor is required. The good news is that today’s analytics software, like SAS, is designed to do most of the math. Thus, we strongly felt there was a need for a book like this one, which takes the power of the software into account and, at the same time, simplifies the mathematical concepts involved in the process. With this motivation in mind, we started our work and strongly feel we have been successful in showing you how to use SAS to perform common analytical procedures while providing the basic knowledge of statistics required. The book keeps the theoretical part as simple as possible yet uses numerous real business scenarios to explain the concepts and the way they are used in the industry. Venkat’s working experience with the world’s leading banks and his vast experience working with students as an analytics trainer has come in handy in designing the case studies and examples used in this book.


You can buy this book right now at Amazon. Reviews and suggestions are welcome at shailendrakadre@gmail.com