The Emergence of Analytics in the World of Business Decisionmaking
Aric LaBarr is an assistant professor of analytics at the Institute for Advanced Analytics. He teaches courses in statistics, mathematics, and operations research for the nation’s first Master of Science in Analytics (MSA) degree program, at North Carolina State University.
Analytics is an emerging field of modern data analysis used to help companies and people drive important decisions. Analytics revolves around the data that exist on a daily basis.
Many people subscribe to customer loyalty programs or at least have seen them offered. Companies are rewarding loyal customers with discounts and sales, which hopefully bring these customers back to the store to purchase more. However, smart companies are starting to realize that these customer loyalty programs can help a company evaluate the purchasing habits of their customers. Every transaction on a customer loyalty account is recorded and analyzed in aggregate to try and determine common purchasing patterns across individuals. Millions of transactions need to be collected and analyzed with modern data analysis techniques to accurately mine information about the customers’ purchasing habits. Once common purchasing patterns are identified, companies can make more accurate and useful suggestions and rewards for individual customers.
With the growing availability of online purchasing, companies are applying the same tracking of customer purchasing habits to items bought online. With online data, companies can design customer-specific advertisements that would increase the probability of a customer making a purchase. Companies even monitor how much of the transaction process customers complete on certain products. If customers abruptly stop the transaction near the end of the process, these companies can analyze this data and directly market online through paid advertisements the exact product that the customer almost purchased. Online transactions provide enormous amounts of data for companies to analyze to better adjust their marketing strategies.
Although odds are that most people have not committed medical fraud in their lifetimes, the threat of medical fraud is easier to solve with the help of analytical methods. In millions of people, only a small percentage will actually commit fraud. However, detecting these couple hundred people in a population of millions is extremely difficult and time consuming, especially with the fact that these few hundred people committing fraud are trying not to be found. Fraudsters have become too advanced for companies to leave their detection up to people searching manually through records trying to develop patterns. With modern data analysis techniques, millions of people can be analyzed simultaneously to try and decipher patterns of behavior that could lead to investigation. This saves the company time and money in handling fraudulent cases.
In these examples, we can see that the emerging field of analytics involves a combination of collecting and integrating data properly, developing statistical and mathematical models for analysis using advanced techniques and analytical software, optimizing these models and forecasts, and using the results to assist in making sound, strategic decisions. This is an exciting field for people to strongly consider as a career path.
Why Is Analytics Important?
The common denominator in the previous examples is enormous amounts of data. This big data “problem” is highlighted in a recent study published in the IDC Digital Universe Study (April 2011), which estimated that the amount of digital data produced worldwide in 2011 at 1.8 zettabytes (ZB). A zettabyte is approximately one billion terabytes (TB), or one trillion gigabytes (GB). Although this amount of data seems rather large, it represents only the tip of the iceberg. The same study projected 35 ZB of digital data produced worldwide by 2020.
What Makes a Good Analyst?
With the increase in the amount and availability of data, people with the talent to analyze it find themselves increasingly pursued by companies hoping to derive value from it. The McKinsey Global Institute Report (May 2011) projects a shortfall in the United States of 140,000–190,000 people with the necessary deep analytical skills by the year 2018.
People with statistical and mathematical backgrounds have a great foundation for this field. Unfortunately, most traditional academic institutions do not adequately prepare people for this rapidly changing field. Companies are looking for more than just analytical background and statistical methodology. A quick look at job-posting sites reveals that employers also want analysts who have training in industry-standard statistical software, strong communication skills, teamwork facility, and business savvy.
Statistical software comes in many flavors, and understanding industry-standard software is appealing to potential employers who would rather not invest extensive time and resources in training new employees. However, employers do not want to hire programmers who blindly believe the output of a piece of software. A true analyst understands how the output was calculated, the assumptions behind the output, and the inferences that can be made from it.
The need for communication skills has been highlighted frequently in Amstat News. Communication skills connect analysts to customers and decisionmakers and explain statistical results so people without advanced training can quickly comprehend and make an actionable decision on the results of the analysis. Training in communication skills should extend beyond the lecture by taking students out of their comfort zones and giving them extensive practice in communication of results to real-world problems.
These communication skills also increase one’s ability to work in a group setting. Analysts in modern corporations and federal agencies must be able to thrive in interdisciplinary teams. Like communication, teamwork is best learned by doing: immersing students in team settings and coaching to elicit desired and appropriate behaviors.
Finally, employers seek analysts who can solve their problems using business and statistical sense. The biggest lesson I had to learn as an analyst was that a fancy statistical model wasn’t always the best solution from a business point of view. Too often, traditionally trained statisticians try to find a beautiful, perfect answer instead of understanding how to tie the available data together in a creative way that adequately solves a specific business problem. Model-building is just as much art as science, and not taking the business problem into account is akin to an artist not knowing the medium they are using for their masterpiece.
New programs are in development all over the country to prepare professionals for the new field of analytics. Several—including programs at North Carolina State, Northwestern, the University of Tennessee-Knoxville, DePaul, Louisiana State University, and the University of San Francisco—are notable ones.
The Future of Analytics
Analytics is only beginning to grow as a field, with more industries joining the wave of data analysis every year. Banking, insurance, finance, and retail are major players in the game. However, analytics careers are emerging in health care, gaming and entertainment, sports, government, and nonprofit organizations. The data deluge will not go away; it will only get bigger. Analysts with proper training are becoming sought-after employees who help make data-driven decisions. Will you be one?