I teach part-time at a community college. As an accountant by training, I have always liked managerial accounting and the analytic nature of the numbers that can be created. Applying logic and processes to data has stuck with me from my stats class in college through my auditing risk assessment with DCAA and my roles as accounting and compliance manager along the way. I have always liked to think about the ways in which seemingly disparate data can be combined and a useful set of information can be created – not to mention driving innovation. I just blogged separately about how something like that can be done, but I also wanted to take a moment to talk about the big data trend in general.
Last night as I was teaching I went into what I like to call “guidance counselor” mode. I see myself not just as a purveyor of knowledge to my students, but also as someone that can give a little nudge here or there to the trends that I see as a working professional. Being the Washington, DC area makes it very easy to relate the government contracting world to my students – they live it. So, I started the class with the discussion of what big data is – something that Wolters Kluwer has been pushing for a while internally.
Big Data is defined in many different terms, but potentially the best I have found can be paraphrased as follows:
Big Data -n- information, normally in electronic format, that can be synthesized and analyzed in new ways to create useful output for decision making.
Notice I didn’t talk about size. In my humble opinion, it doesn’t matter here. If the information that is being accumulated has a use, it could be 20 lines or 2 million lines of data and still have some value to the big data analyst.
I have always prided myself as someone who can take in a set of information, analyze it relatively rapidly, and expand and improve upon that information. I mentioned this to my students who responded with “How?” Through STEM training, this type of professional will be formed to regularly do this in a methodical way. “Why?” was the next question.
I provided an example of Big Data for a retail organization (I used Target, but any will do). If you stop to think about the amount of information that circulates the computer systems of a large retail business in a given day, the idea of Big Data starts to come to life. Every credit card swipe = data. Every package scanned at the loading docks (incoming or outgoing) = data. Every kiosk with a button = potential data. Every scanned item at a “price checker” = data. So, what do we do with it? Peak purchasing times. Inventory control. Buyer interest. Many of these are no-brainer, “Business 101” concepts, but value-added analysis can make it transformative. If someone scans an item on a price check kiosk and that item is not then purchased within a certain period of time, perhaps the price is not reasonable or something was wrong with the product. The customer couldn’t justify following through with the purchase… what else can be gleaned from this and other data?
The classroom discussion went on for about 45 minutes.
By applying deep data analytics to the ever-expanding amount of big data, my students could be creating some of the linkages to things like medical data, driving data, transaction history, tax payments, contract awards… (you get the picture) that could truly make it a “small world after all.”
How have you used Big Data lately or how have you thought about using it?