A Primer on Data Analytics from 37Signals

The folks at 37signals have some great software. They also throw up a lot of good content on their blog. I saw this article on data analytics and it resonated with me. Noah has a few great tips for any budding data analytics expert.

I help clients build a lot of spreadsheets for analytical purposes. However, my clients are finance and accounting types working for business enterprises, so they rarely think of things from a data analytics perspective. It’s not my default position either, but as financial analysts we are uniquely situated to bring this skill to the table.

Most financial people realize their purview ranges way beyond the financial statements and the annual audit. They love to be involved in the strategic side of things and they’re always ready for getting their hands dirty with some serious performance management. But sometimes they’re not well-versed in many of the simple tools in MS Office that can be used for analyzing rows and columns of data. This type of data requires a specialized set of tools since it is not as refined as the trial balance and general ledger data that financial people are used to.

I fall into this trap also, but I’ve made a serious effort over the last decade to learn Microsoft Access and understand the tools that Microsoft Excel has to help make sense of this stuff. My feeling is that every finance person needs to think of themselves as being in the business of data analytics. They should be excited when they get a text file with two years of production data or get a data table with general ledger detail dumped into it. Getting actionable information out of this data should be second nature, much like analyzing the variances between this year and last year is.

The start is Pivot Tables. Nothing can help a finance person make sense of raw data more efficiently than Pivot Tables. But we’re not rooting through Pivot Tables now. Please just take a read on Noah’s three tips to wet your appetite for analyzing some data.

I was especially struck by his statement that you should “Memorize your database schema.” This also applies to doing some heavy data analysis in Excel. I know I’ve really gotten in tune with my data when I can just type a quick reference from memory in an Excel formula without having to find it in another sheet and point to it. That’s analytical nirvana, at least for a finance nerd like myself.

We’ll revisit Pivot Tables and this topic frequently as we learn (together) how to round out our financial skill set with a solid base of data analytics.