As we mentioned in our last article, data and analytics skills are becoming increasingly more crucial for accounting and finance professionals. These skills are in high demand across the industry—and by becoming data-fluent now, you can open up bigger career opportunities for yourself and perform your current job more effectively.
But it’s not like you can just spend a summer at Camp Data and learn everything you need in a few months—while sharing spooky campfire tales about the spreadsheet with the hook for a hand.
(Side note: It’s not quite summer camp, but if you’re a new graduate or recently lost your job, you can apply for free access to data science and analytics training courses through the Advancing Data & Analytics Potential Together (ADAPT) program. S’mores not included.)
Becoming data-fluent will require you to shift the way you think about data, allowing you to have a bigger picture view of your work and its effect on the business. Then you’ll take your coveted spreadsheet skills and expand on them to learn modern analytics.
We searched high and low (mostly high, because it was “the floor is lava” day at the office) to find the most important data and analytics skills for accounting and finance professionals. Here’s our list—along with some advice on how to jumpstart your skill-building journey.
Build predictive model forecasting skills
Predictive analytics means taking historical data and analyzing it with algorithms or tools to make educated guesses about future events.
It’s not hard to see how this would be useful in the accounting and finance function, particularly when it comes to forecasts. The ability to accurately predict the outcomes of horse races and sporting events transformed Biff Tannen from a car-washin’, tracksuit-wearin’ loser into a casino-runnin’, hot tub-soakin’ fat cat who owned half of Hill Valley (it also turned him into a murderer, but let’s ignore that part for now).
Unfortunately, most of us can’t rely on a future version of ourselves traveling back in time to give us a sports almanac that helps us cheat our way to fame and fortune. We have to make do with building and using forecasting models that can intelligently predict future market behavior.
And if you’re thinking your fond memories of model airplane club are going to help, you’re in for quite the shock. Depending on what you’re trying to predict, the method for creating your forecasting model will vary, but the process can generally be divided into three steps:
(WARNING: We’re about to get super technical here. If you start to get lost, feel free to skip to the next section. We promise not to tell.)
1. Transform “seasonal” data into “stationary” data. This essentially means removing any time elements that affect your data. For example, if you’re running a car dealership and your sales spike during the annual President’s Day sale (because buying a car is the best way to celebrate freedom, apparently), you might need to strip out that fluctuation in order to achieve stationary data. This step sometimes involves the use of linear regression.
2. Build a time-series model. This means first selecting the appropriate “base model” (options of which include the naive model, the moving average model, and the exponential smoothing model, which sounds like a list of people who routinely swipe left on our dating profiles). From there, you’ll probably need to implement an ARIMA model, which is an acronym for a bunch of words that we don’t understand, but hey, that’s why we’re giving you links.
3. Experiment with different models and determine which one is the most accurate. You’ll actually need to perform Step 2 multiple times, which will leave you holding several different time-series models. So the final step is to figure out which one of them works best and then put it into play. Perhaps the fastest and easiest way to do this is by calculating mean absolute percent error (MAPE), which you may actually remember from your college statistics courses. That six-figure tuition debt you racked up is now totally worth it.
If you’re thinking, “That sounds way too complicated and outside of my skill set,” then congrats, you’re reading the right article. No one said this stuff was easy—but it is way easier than you think and there’s training and technology available to help you come out on top.
Our friends at Alteryx can give you a huge assist, providing all kinds of educational resources and training courses. The Alteryx Designer Knowledgebase offers articles on forecasting models and many other topics, and experts regularly post informative blogs in the Alteryx Community that can be a great help.
Perform advanced revenue analytics
Accounting and finance professionals are using data and analytics tools to look at revenue in new ways—helping to create innovative strategies for increasing cash flow that both make the business stronger and increase their individual importance within the organization.
“Advanced revenue analytics” is a broad category that may encompass many different algorithms, models, and tools—so it’s pretty much impossible to explain “how to do it” within the confines of this article. (We might be able to explain it if we wrote a whole book on the topic, but we’re way too busy with our wildly unpopular fanfiction series about “What If Spider-Man Married Black Cat Instead of Mary Jane” to have time for that.)
What we can do here is show you some areas where advanced revenue analytics are currently being applied. From there, you can work backward to learn how to use analytics to perform each of these three tasks—or whatever revenue-related metric or forecast you want to create.
1. Smarter geographical pricing. If you work at a larger company that spans multiple regions or countries, you may have experienced the difficulties of setting appropriate prices in relation to geography. Advanced revenue analytics can help solve that problem, allowing you to dynamically adjust prices for different locations based on a wide variety of factors—and do so with minimal risk to profitability.
2. Data-driven promotions. It’s a given fact that promotions and marketing have the power to increase revenue. But, for most of human history, measuring the direct impact of a specific promotion or campaign has been the equivalent of playing “Guess Who?” blindfolded—you can’t be sure if you’re winning, and even if you are, you have no clue how you’re doing it. Advanced revenue analytics can bring more clarity to promotion and marketing metrics, allowing the business to examine the effect of certain strategies from a wide variety of angles—and then optimize campaigns accordingly.
3. Optimizing physical and digital shelf-space. Putting your best-selling products and services front and center is common sense in the business world, whether that means a supermarket giving Cap’n Crunch the most shelf space (even though Cinnamon Toast Crunch is so obviously superior) or a nerdy news site putting all the Star Wars articles above-the-fold. It’s often considered too dangerous to put newer or less-popular items in these coveted spaces—but advanced revenue analytics changes the equation. Using certain tools and algorithms, your organization can move its physical and digital products around and create small-scale tests to measure the change in revenue with little risk—or it can even go zero risk by running these tests in simulated environments.
Alteryx again offers many great resources on this and other related topics, helping you master a variety of advanced revenue analytics skills through training courses and content like this slide share on accelerating revenue growth.
(Extra credit) Learn basic SQL programming
Take a deep breath—you don’t need to learn code to use modern analytics. Today’s tools automate the gathering, prepping, blending, analyzing, and auditing of data for you, transforming what would’ve been hours and hours of coding work (not to mention hours of clumsy data manipulation using spreadsheets) into just a few clicks.
So we’re not suggesting that you join five poorly-groomed dudes to ignite a billion-dollar compression algorithm war in Silicon Valley. But picking up a basic understanding of SQL—the most popular language used to connect and facilitate communication between databases—will help you understand data better and do wonders for your career.
There are about a gazillion resources out there that can teach you basic SQL programming, so we won’t insult your Googling abilities by providing any links. But while you’re out there, can you do us a favor and figure out if most people pronounce it “es-cue-el” or “sequel?” We’re still working on our official stance, and we want to get to the point where we can be all militant and scornful about it like we are with GIF. (We won’t reveal our opinion outright, but know not to expect any Christmas jifts from us anytime soon.)
Taking the next step
Predictive model forecasting, advanced revenue analytics, and making the shift from spreadsheets to analytics workflows will put you way ahead of your accounting and finance peers—but in case that’s not enough homework for you, here are a few other skills you might want to look into:
- Cost optimization
- Real-time model development
- Data visualization
Throughout this article, we’ve talked about how Alteryx can help you build these skills and advance your career. But the best way to really get your data and analytics motor running is to encourage your organization to start using the Alteryx platform.
The Alteryx platform delivers end-to-end automation of analytics, machine learning, and data science processes, allowing you to learn data and analytics skills faster and then do more with your newfound abilities than you ever would’ve thought possible.
Be sure to take advantage of Alteryx’s full library of resources and apply for free access to training courses and more through the ADAPT program. To learn even more about how Alteryx can make your job easier and help you use data and analytics to take your career to the next level, check out these two webinars:
Webinar: Reshaping Demand Forecasts for the New Normal >
Webinar: Financial Planning & Analysis (FP&A) Automation >