Accenture Academy Blog
Everyone has read about the exploits of Florence Nightingale in grade school. But did you realize that she may have been one of the first data miners in history? She was indeed one of the first users of big data. I’ll bet your grade-school textbook didn’t describe Florence Nightingale in those terms.

Florence Nightingale—a nurse assigned to the old Barrack Hospital in Scutari during the Crimean War in 1854—was one of the first users of big data. Scutari is a district of what is now Istanbul, Turkey. She was assigned to nurse soldiers wounded in the war; what she found were men in dirty surroundings waiting to die. Nightingale was not only a devoted nurse who nursed wounded soldiers back to health, but she was also the first woman to be elected a fellow of the Statistical Society of London (the precursor of the Royal Statistical Society). Nightingale was elected because of her innovative approach in examining the plight of the wounded soldiers in much the same manner a modern-day data miner would approach the same problem. What she unearthed undoubtedly saved many more lives than her hands-on care during the war.

She assumed that all the facts she had collected during the war were big data and that useful information in the form of patterns was locked in the data but not viewable in its original form. In one of her monographs, she produced what has become known as Nightingale’s Rose or Nightingale’s Coxcomb (a diagram). In the diagram, the rose displayed the causes of death of soldiers during the Crimean War sorted by category (e.g., preventable diseases, wounds, etc.). By binning the data, a pattern emerged showing far more soldiers died from infection than from the wounds themselves. At the time, this was a revelation. Shortly after publication, she was criticized for being frivolous in presenting pictures instead of simple facts. As it happens, what she had actually done was to distill the facts and present the underlying patterns open for all to view and use. The changes in medical care that resulted from her findings are what resulted in her being referred to as the mother of modern nursing practice.

What Nightingale did is what every firm today hopes to do with the masses of data it has accumulated. Every firm hopes to make some sense of the patterns in the data in a way that the company can benefit. The term big data today is a reference to two characteristics of our data—the volume of data and the fact that everyone has data.

First of all, the sheer volume of data available for analysis today is much larger than the data from only a few years ago. Second, and most importantly, we all have data. Not only do we have a sample of some larger whole, but we actually have all the data there is in a particular category. For instance, consider Netflix, an online movie rental service. Netflix analyzes what movies or tv shows it will recommend to you by using all the data from everyone that has ever watched a movie or tv show on Netflix. All users of Netflix have provided it with preferences by information such as how many stars a movie is given; what the users have actually rented, watched, or streamed; whether or not the impressions of the movie have been shared; how long the movie is kept; whether subsequent movies by the same director or actors are rented; and whether this genre of movie is the one most often selected.

Netflix and Nightingale both made effective use of big data in very different contexts. Although Nightingale did not have access to the type of analytical tools available today, the underlying principles of her process can still be used to discover insights from big data. Please join us for the upcoming webinar Exploring Big Data: Implications for Forecasting and Demand Planning. During the session, we’ll attempt to frame the term big data and give you an opportunity to see exactly how the analysis of this data is done. We’ll review what tools are used, what information is created or revealed, and how your firm or division might begin a data-mining endeavor.

What do you want to know about data mining and how it affects the way you do your job? What particular ideas and questions do you have to contribute to the conversation?

Respond to this blog post with your questions and comments. Your input will help us design the content of the webinar, and we will address your questions during the live session. 

Register for the webinar on June 25 or 26, 2013. I look forward to hearing from you!

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