Despite the many advanced analytics tools coming to market across industries, it turns out that 76 percent of analysts use Microsoft Excel for their “self-serve” analytics projects. This is according to a new, cross-industry survey Lavastorm Analytics has conducted with 600 analytics users, technologists, managers and executives. It’s an overwhelming number, but it shouldn’t be surprising at all.
You Can’t Beat the ROI
I can say from personal experience that over the years I’ve created literally millions of dollars worth of analytical deliverables with Excel. Especially since 2007, when Microsoft upgraded Excel’s graphical tools and moved away from drop-down menus to palettes, Excel has been great for creating charts and dashboards that one can then drop right into other documents. Once you know how to use it, it’s easy, and it pretty much teaches itself to you over time in logical iterations. Sure, it’s a bit clicky, and it will bog down your processor when dealing with relatively large data sets. But for everyday number crunching and data presentation, it’s the workhorse for analysts everywhere. It’s tough to argue with the economics behind using a $110 bit of software to crank out millions in revenue-generating consultative and analytical reports.
We’re now seeing all kinds of new analytics tools coming to market and plenty of pitches along with them that tout how much more effective and purpose-built they are for analytical reporting, dashboarding, and data manipulation. All of that may be true, but those arguments overlook Excel’s ease-of-use, ubiquity, and mass of users; you pretty much need to know Excel – at least the basics – to qualify for any job that involves using a PC in any way.
Pyramids Were Built with Hammers and Chisels
I’m sure much of the fancy new stuff that’s coming to market is great and some of it is probably vaporware as always seems to be the case. But, let’s put this in the context of history. Would the Eqyptians have built the Great Pyramid at Giza faster if they’d had hydraulic rock drills? Maybe…but only if they had training on how to use them; fuel, oil, and hydraulic fluid to run them; parts to maintain them; back up drilling attachments; and repair crews that knew how to fix them. The fact is, the massive stones and limestone facing used in pyramid construction were cut by hand with hammers and chisels. Those hammers and chisels are kind of like Excel – pretty much anyone can learn how to use them quickly; they are plentiful and widely available; they are easy to implement and to replace; and they are very capable – in practiced hands – of creating beautiful things. They aren’t the fastest and most automated tools for cutting stone, but you really can’t argue with the results. (Granted, it took 40 years to build the Great Pyramid…)
So, my sales pitch for Excel comes to an end here. I’ll admit that I’m a fan. Despite its obvious drawbacks, Excel let’s you experiment with data and create models that, ultimately, you may want to establish within more sophisticated and capable analytics tools. But the survey suggests that 3/4 of the people out in the field, doing hands-on analytics work every day, continue to rely on good ol’ Excel to do their grunt work. That critical mass of users might be impossible to displace or replicate.
Why does this even matter?
Sometimes we forget about Microsoft’s presence in the BSS/OSS space because while it’s partner ecosystem is pretty significant, Microsoft is not by definition a major BSS/OSS supplier. But, tools like Office – not to mention SQL and Windows Server – are ubiquitous. And, as analytics become increasingly important to BSS/OSS, Excel’s role as a standard tool expands and becomes more important for doing day to day analysis and data presentation. So, while I’m not suggesting we devalue more sophisticated approaches to analytics, I do think we need to remember that simple can be great and the right tool isn’t always the one that’s sophisticated, expensive, and requires a PhD to operate.
The article is a good summary of the challenge competting analytical tools for with Excel. Analytics should be done not with a tool that is rigid on procedures and processes but with tools that freedom for creative analysis. This is where the winner – Excel – takes it all.