The last couple of posts discussed how we measure business performance and what we mean by analytics. In case you missed it, we measure business performance by quantifying the outcomes that matter for our organizations. Once we have those measurements, we can then use analytics to understand past patterns and predict future trends. These analytics can range from simple descriptive statistics and graphs, all the way up to advanced multivariate statistical modeling.
Right behind the question “what is analytics?” one of the most common questions I get in this domain is “why can’t we just do this in Excel?” This is a valid point given how ubiquitous and powerful Excel is as a spreadsheet. And at the most basic level you can, but pretty quickly we start to see that sticking to Excel not a great idea. To understand why that is the case, we have to understand the actual drive behind that question.
In my experience, the desire to use Excel is not really an expression of attachment to Microsoft’s spreadsheet program. I’ve rarely seen anyone love a particular piece of software that much. This question is usually more an expression of not wanting to invest the resources (i.e., time and money) into purchasing the new tools and then learning how to use them properly. In truth, it is more the second issue where the heart burn comes in.
Let’s face it, Excel is easy because Microsoft designed it to be that way. You can put numbers in, do calculations, and even make basic graphs pretty quickly. There is even a plug-in you can download that allows for some rudimentary univariate statistics. And while those may seem like progress beyond just simple descriptive, they are very basic. Moreover, Excel has some significant size limitations. According to Microsoft’s website, the current limitations for Excel are 1,048,576 rows and 16,384 columns. While that may sound like a lot, it can become too little for most companies pretty quickly.
Let’s say you’re a small pharmaceutical firm that has say only 200 sales representatives (most have thousands). Those reps have to record any calls or interactions with doctors writing scripts. Often there are 30-40 interactions per sales rep per day. If you were just using Excel you would not even be able to capture a full 9 months of data into a single spreadsheet (200 reps * 30 calls * 20 workdays * 9 months = 1,080,000 records). Given that we usually want to look at data across multiple years, and that Excel is not great at analyzing data across multiple spread sheets (it can do it but is very messy), you’re definitely better off going to something more robust.
Why would you want that more robust tool? To put it in terms your CFO would understand… time is money. The number of hours it takes to do any but the most basic analytics in Excel is anywhere from 2x to 10x what it would take to do in a more robust solution. For example, let’s say you need to run some kind of analysis to determine the relationship between some new marketing strategy and resulting sales. Depending upon the size of the data set and type of information this can take as little as ½ a day (i.e., 4 hrs) to run in a robust analytics solution. If you were to try to run this in Excel it could take as much as a full day to a full week to get this done (i.e, 8hrs to 40hrs) depending on quality of data and how much Excel can support. If we assume that average marketing analyst makes somewhere around $71,000, we could be looking as much as $1000 of extra labor costs every time we run this analysis. Given that most robust analytics packages cost less than that per license, the business case to upgrade is pretty strong.
Now don’t get me wrong. I’ve seen people try to do their analytics in Excel, and there are times where it is appropriate. For example, for my little small company it works just great, but we are very small. That is not, however, the case for larger companies. In fact, I can’t think of a single time where a client has said “we’re just going to do this in Excel,” and didn’t come back later when they realized that as great as Excel is, it just isn’t robust enough for real analytics. That being the case, save yourself some trouble on the back end and look for a more robust solution up front.
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About the Author
Jimmy Brown, Ph.D. is a senior level management consultant with eighteen years of experience leading efforts to develop and implement practical strategies for business performance improvement. Dr. Brown has held senior level consulting positions at leading firms such as Booz-Allen & Hamilton, Accenture, and Hewlett-Packard.
He can be reached at www.jimmybrownphd.com or via Twitter @jimmybrownphd