I’ve always had a keen interest in data. To me, the free flow of data into perfectly normalized tables, with complete integrity between disparate systems, is a thing of beauty (excuse my geek moment).
Learning the Technical
From the technical side, I’ve taken courses in Access, SQL, and Visual Basic. I’ve lost count of the number of different data tables I’ve had to clean up with PivotTables and VLOOKUP’s in Excel. I was also lucky to have exposure to Tableau (a widely used data visualization tool).
One course that I found useful as a CPA was an Intro to Analytics and Big Data from Seneca College. Look out for classes, such as this one, where you get to work with data professionals, not accountants. This provides an opportunity to see different perspectives on the problem, while helping others see things with a business focus.
You quickly learn what variables are impacting what you are doing, and how to disseminate this to others. Data analysts will usually apply assumptions and criteria when performing their queries. I always used to ask them what assumptions/criteria they applied. In one case, the analyst said they excluded any records that had a null result. However, I now knew exactly when null results needed to be excluded; this case not being one.
Data scientists/analysts may have extensive knowledge about databases, algorithms, and statistical techniques, but they may not be well versed in talking to CEO/CFO/CRO level management. As CPA’s, we know what keeps the C-level execs up at night. Plus, providing business advice is in our DNA.
My Three Principles To Remember About Data
1. Garbage in – Garbage out.
As long as humans are involved in designing, implementing, and populating data, there will be potential issues with the information available. This is an issue when different systems need to interact with one another.
For example, a CRM system may define a location as any place a sale takes place (brick and mortar store, pop-up shop, virtual marketplace); while the accounting system only handles locations with a fixed permanent address. Some data just doesn’t transfer between systems at all. This is where being data literate will allow you to be mindful of the factors that can tarnish the data.
2. “So What?”
New tools are great at doing the heavy lifting of sifting, compiling, and summarizing data, but don’t always give the whole picture. It is the questions you ask that will make or break the analysis.
You can have instant reports telling you the most popular items sold, by location, by demographic, but are these the right products you need to sell to grow your business next year? As a CPA that is in tune with the ins and outs of the business, you are the person to turn to when the CEO looks at the report and asks “So what?”
3. You Can’t Have It All
There are just some things that cannot be explained by hard data. You can have the most kick-ass Selfie-GIF-Sharing app that works even on spotty Wi-Fi, but you still end up with terrible market share. Why? Because the “cool” celebs endorse the leading app as “straight up lit” instead of yours. These are the type of things that would make even the smartest AI system scratch its head ¯\_(ツ)_/¯, and that’s okay.