Have you ever taken a close look at your dashboard when the car’s computer displays key performance indicators (KPIs)? No? Yes, but not really? I am confident in saying that 99.9% of readers will answer with a “not really” type of response, as there are many misleading, so-called KPIs that don’t provide guidance to make the right decision. I can’t understand why car drivers have not yet complained about being misled. Here are some rules to follow to keep your KPIs from going wrong.
Make sure that your KPI is sufficient to guide a decision
I recently took a look at the mileage on my truck and noticed how the MPG rocketed up when I took my foot off the gas. So if I see MPG as a leading indicator to optimize my trip, I would never arrive at my destination, as I’d stop to max out on MPG.
So, in financial taxonomy, this would translate into something like the saying, “zero budget is not an option.” Don’t exclusively focus on cost without having the broader goal (like margins improvement) in mind. You can’t cannibalize outcome with cost reduction; at least you’d have to achieve the same outcome at reduced costs.
Your analytics have to provide insight into the root cause for your indicators to optimize. In this case, it’s margins in the means of a decision tree, a value map, or the like, so you can see the immediate outcome of any planned action. Simulation and prediction would be needed, combined with visualization of the context, in order to make it understandable for your executives and stakeholders.
Make sure your KPI is taking all known information into consideration
To stick with the road trip example, I don’t understand why GPS producers don’t see the value of including some kind of data mining in their offerings. The GPS knows the distance, the type of roads followed, the time of the day, and the season you’re in (like wintry conditions that might influence the trip).
It could know how many miles you can go per gallon in which conditions – or even pull this information from the car computer if it’s an integrated system. It could measure how much time you’d take to fill your car up at the gas station. Since it can measure how long you’re there, it can even deduce if your stop is for gas or just to pick up a six-pack on your way home from office.
So, assuming you want to go on a longer trip, say from San Francisco to Austin, Texas, why can’t the GPS guide you to the optimal speed to arrive at your next stop as soon as possible? This would take typical “bio breaks” into consideration (info available when you usually stop), gas stations to fill the car, projected traffic jams due to rush hour in metropolitan areas (Los Angeles!), and the like. It could even run simulations like, “If you go 70 mph instead of 85 mph, you’d manage to get to your stop with this one tank.”
Sound familiar? So, let’s translate this into finance, using the planning process for example. You have all long-term planning information available, including the company’s strategic plan and the related KPIs (hopefully clear and leading ones as mentioned before), and all good information from any kind of ERP-like system. Also, you might have the plans from other areas like product sales plans, workforce plans, production plans (if applicable), and cost center plans. This would all be needed to arrive at an integrated business plan, driven by the long-term financial plan.
You now would have almost all the ingredients to simulate outcomes based on different distributions of funds available for the current planning period. You won’t get trapped into pitfalls like having to pull additional funds into this planning period that are saved for later use (e.g., having to stop at the gas station). You’d see how budgetary decisions would influence achievement of your company’s targets and would uncover potential correlations between driving indicators and outcomes (like HR development versus hiring of external people going through the value chain arriving at optimized investment in your workforce).
Don’t omit these factors, since they’re contributing to your KPIs. Even worse, there are correlations between factors that you can’t easily figure out but would have to use statistical algorithms. For example, what makes a certain customer pay on schedule vs. being an “overdue receivable”? This is not as easy to understand as the famous example about the correlation between sales of ice cream and shark attacks. But to find a causation and guide the way, you need tens or even hundreds of dimensions correlated.
What does this mean for you?
Things that are obvious to take into consideration when planning your road trip are not as easy to uncover in your professional life as a finance expert, as many more dimensions are affecting business performance. Given that the additional charter of any mature finance organization is to provide excellent service to the other business functions within your organization, it’s your duty to support the cost center manager, the sales executive, and last but not least, every employee by providing relevant and contextual finance data that enables better and fact-based decisions.
In addition, sophisticated finance analytics uses the support of visualization and predictive functionality to guide the way through the core finance tasks around financial planning and analysis, accounting, treasury, operations, and even risk management, compliance, and audit functions. It helps achieve more with less – operational excellence at reduced cost by supporting every finance function to deliver on the promise of simple data and intelligence provision for the whole organization.
This means that the finance function of tomorrow has a new credo: Be a partner to the company and support to differentiate from your peers, add value to the bottom line, and strategically consult the executive leadership team of your company to achieve sustainable growth.
To learn more about how finance executives can empower themselves with the right tools and play a vital role in business innovation and value chain, review Thriving in the Digital Economy.