Data Science vs. BI vs Statistics: What's the Difference?

Data Science vs. BI vs Statistics: What’s the Difference?

Data Science vs. BI vs Statistics: What’s the Difference?

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.

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Currentlyyou 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 makedata drivendecisions 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)

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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.

 



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