OThot

“Data! Data! Data!” he cried impatiently. “I can’t make bricks without clay.”
-Sherlock Holmes, The Adventure of Copper Beeches

 

Sir Arthur Conan Doyle’s timeless character and hero of deductive reasoning, Sherlock Holmes, was a vocal proponent of acquiring all the facts first—he realized the importance of the data, the details. When it comes to solving a mystery, the data surrounding the case is a detective’s primary tool for shaping conclusions, or in Sherlock’s words, the clay that makes the bricks.

Holmes once remarked to a fellow inspector, “The temptation to form premature theories upon insufficient data is the bane of our profession.” Here, he was referring to the problem of not having enough data. Even if multiple intriguing clues arise, the case will remain unresolved if the lines between the clues cannot be drawn.

If we fast forward from the era of Sherlock Holmes to today’s “Big Data” world, the problem is reversed. Rather than having too little data, the challenge is effectively managing the avalanche of too much data from disparate sources. Big Data consists of a myriad of data sets and sources and finding answers to problems within them is no easy feat. Thankfully, predictive models and analytics algorithms are used to build engines that help us make sense of this influx of information, which assist in our detective work. These engines can sort through all of the data sets and determine which variables are the most influential on a certain outcome—which clues are the strongest links to cracking the case. Implementing predictive analytics and finding important correlations in the data can answer some of the more difficult questions—solve the more difficult mysteries—that an organization may have.

If we could pay a visit to 221B Baker Street and still find Holmes there today, we could surely expect him to be an expert in data science and predictive analytics, availing himself to the technologies that would enable real time assessment and predictive analysis and solving mysteries faster than you can say “Big Data.”