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The Role of Statistics in Business Decision Making
The Role of Statistics in Business Decision Making
The use of statistics in business can be traced back hundreds of years. As early as 744 AD, statistics were used by Gerald of Wales to complete the first population census of Wales (1). It wasn’t long before merchants realized that statistics could be used to measure and quantify trade. The first record of this was in Florence. It was recorded in Giovanni Villani’s “Nuova Cronica”, in 1346 (1). Moreover, statistical methods were further adopted to help drive quality and in doing so helped contribute to the advancement of statistics itself. In 1504, William Sealy Gosset, chief brewer for Guinness in Dublin, devised the t-test (2) to measure consistency between batches of stout (1).
With the rise of big data, organizations are looking to extract deep insights from their data using advanced analytical techniques. With big data, new roles like Data Scientists are being developed within organizations. But no matter the title of the role, be it quantitative analyst or data scientist, they all share one thing in common. Mathematical statistics and probability are at the heart of these disciplines and they are seen as critical to the success of a business.
Currently you would be hard pressed to find a business that does not perform some level of statistical analysis on their data. Most of these analyses are performed under the general term of Business Intelligence (BI) (3). BI can mean many things but in general, BI is used to run a company’s day-to-day operations and includes software, process, and technology (4). BI enables organizations to make data driven decisions and effect change.
The term “data driven” is synonymous with companies that leverage their data and analytics to unearth hidden insights that have a real and measurable impact on their business (5).
“FedEx and UPS are well known for using data to compete. UPS’s data led to the realization that, if its drivers took only right turns (limiting left turns), it would see a large improvement in fuel savings and safety, while reducing wasted time. The results were surprising: UPS shaved an astonishing 20.4 million miles off routes in a single year.”(5)
By applying statistical and probabilistic methods to their data, organizations can unlock patterns and insights that otherwise would have gone unnoticed. These insights, as in the case with UPS, can lead to significant increases in revenue while driving down costs to the business.
Statistics use in business is currently undergoing a paradigm shift in its scope and application. Today, data scientists are leading the charge in the application of statistics and probability to help businesses use their most important organizational asset; their data (6).
Above we see a comparison between the work of a statistician and that of a data scientist. A data scientist deals with data in its raw form including structured, semi-structured, and unstructured data. The outputs of the data scientist are generally data applications or data products. Data driven applications are driving how companies are generating revenue, examples include Facebook, LinkedIn, and Google (7). Data driven applications are creating what is known as the “smart enterprise.” Smart enterprises allow not only management, but also rank and file, the ability to have analytics at their fingertips. We see this on LinkedIn with their recommendations for connections, the same for Facebook with friend recommendations. The “data application” is constantly looking for people that may enhance a user’s network.
Above is a comparison between traditional BI and data science. The biggest difference is that BI is generally backwards looking (simple descriptive statistics) and data science is forward looking (inferential statistics). BI will always be a part of the enterprise, traditional EDW’s aren’t going away anytime soon. However, these traditional systems are being complimented with emerging technologies (Hadoop, In-memory databases, plus others) to support big and fast data analytics.
Throughout history, statistics have been recognized as an indispensable tool of business operations. Starting with the population census of Wales in 744 AD, statistics have been applied to many facets of business. The need for quality and consistency have been the major drivers for the adoption of statistics.
Data has been touted as “The New Oil” in the era of Big Data. (8) Companies looking to have a competitive advantage need to embrace statistics and probability in the form of advanced analytics. There is no doubt that data will continue to be the control point of success for business. Mathematical Statistics and Probability will be a critical underpinning to winning data strategy.
 Statslife.org. Timeline of Statistics.
 Kopf, D. The Guinness Brewer Who Revolutionized Statistics.
 Blackman, A. (March, 2015) What is Business Intelligence?
 Heinze, J. (November, 2014). Business Intelligence vs. Business Analytics: What’s the Difference?
 Mason, H. Patil, J. (January, 2015) Data Driven: Creating a Data Culture.
 Smith, D. (January, 2013). Statistics vs. Data Science vs. BI.
 Accel Partners. (Summer, 2013). The Last Mile in Big Data: How Data Driven Software (DDS) Will Empower the Intelligent Enterprise.
 Yonego, J. (July, 2014). Data is the new oil of the digital economy.
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