Data analytics seems to be getting a great deal of press lately. The emergence of “tech” as a social norm, the evolution of the internet and the attention of the big four social networking giants (Google, Facebook, Twitter, LinkedIn…although I’ve seen some good arguments about who that last one should be) has brought data and its study (data analytics) to the forefront of our attention.
So what’s the buzz about data analytics?
Well, for one, it’s been around as long as math – pretty much since the beginning of time; and we’re just now starting to realize how “cool” it is to actually look at math as something fun because let’s admit it – that’s what data analytics is – statistics…and statistics is math.
Data analytics is the use of information that can be broken down and compared and contrasted in different ways from which, once analyzed through different lenses or perspectives, conclusions can be drawn about whatever it is that’s being studied (that’s my own definition). Hhmm, that sounds dense. Let’s say that in a different way. Data analytics is the analysis of data for better outcomes.
Let’s Try Something Together
Look at it in terms of your personal finances…do you track your monthly expenditures? This is the easiest way to use data analytics and to learn on your own. Open up MS Excel (yes that program that can with your copy of MS Office with the “X” on it that you haven’t used since college) or whatever spreadsheet software you have, Google Docs is a great alternative, and start by making a spreadsheet that looks like the screenshot above. (When you do this on your own, replace the “X’s” with actual positive or negative numbers where appropriate)
Label the first column Assets/Liabilities, the next 31 columns labeled consecutively from 1-31 and the last column TOTAL. Below those columns enter everything that you spend money on and everything that puts money in your bank account under the corresponding date that the money enters/leaves your bank account. For most of us, our “assets” are our day jobs – call this “work” or “salary” in your spreadsheet. Each asset or liability gets one row and each transaction goes under the corresponding date. If you get interest on your bank account, call that “interest”. Do that for everything you could consider an asset.
Then, whatever you spend money on, track that on your spreadsheet as a negative balance, since those are liabilities or things that cost you money. Do this for an entire month and, WA-LA!, you have data! Do this another month and POOF!, you have some real information for data analytics. And guess what…this stuff actually matters to you (or at least it should)! If you have no idea how to do analytics, just think of it as the digital version of balancing your check book. I realize I shouldn’t take for granted that most people know how to do this, but for the sake of argument I will. Once you’ve built up a couple of months of expenses and income, you can begin to make some real data comparisons regarding your money situation.
Now think about this exercise in terms of what your sports business is doing…probably something much more complicated with many more variables – that’s ok. Statistics takes some time to learn and data collection and analysis doesn’t happen overnight. Also, just like this example, data collection must come before data analysis. You can’t analyze what you don’t have!
Cleanliness is Next to Godliness
Also important to remember is that your data must be “clean”. By that, I mean that you must use the same nomenclature, phrasing, listings, and categories for every piece of information. Everything MUST be consistent. You can make this is as complex as you want, however this goes both ways; the less information you track the less work this will be, but also less meaningful – the more information you track, the more complex and challenging the analysis will be, but also the more meaningful the conclusions will be in your final results. Now this is not something to be intimidated by, but this is how to use data analytics in a very simplified example.
Data Analytics in Sport
In a sports business setting, the work you are using data analytics for is much more complex. For example, you’re using data analytics to figure out athlete health and performance, you’re using it in the box office to track sales and customer buying activity, you’re using it the marketing office to track advertisement and online engagement and effectiveness, you’re using it in the concession stand to track what items are selling the most and at what particular venue locations on what days and at what points during the game and so on and so forth. If you want to learn to use data analytics, I strongly suggest you start by tracking your own financial situation. Once you’ve gotten a few months of worth of data and figured out everything you can on that front, it’s time to advance to more complicated avenues.
If you want help or have any questions or comments, drop me a message on Twitter or LinkedIn and let’s work together and start a conversation. I hope you enjoyed this post and learned something new. My challenge to you is to actually start tracking your own finances. By doing so you’re both taking control of your financial future and learning a useful professional skill! If you’d like to support Bill’s Sports Business Blog, please sign-up for the newsletter and consider using my <a href="Amazon Affiliate link. At no additional cost to you, I’ll make a small commission on your purchase. The proceeds from your use of my affiliate link directly offset the costs associated with this blog. Thank you for your support.