Skip to main content

Game of Data - A Story of Muffins and Metrics

I’ll begin this post with a confession: I don’t have the best dietary habits (though I swear I’m working on it). My office has a self-service cantina stocked with a variety of foods including candy bars, soft drinks, frozen meals, and, stored in a round wicker basket, the most addictive muffins on the planet. Many mornings, despite my best efforts, I cannot fight the urge to grab my $1.80 muffin from downstairs before plowing into the depths of spreadsheets and ad hoc reports. It’s an addiction, but at least it’s not crack… right?
Addictions and self-control aside, nothing frustrates me more than when that little wicker basket is empty when I’ve already committed to quenching my muffin madness. What’s even more frustrating is when I see the vendor’s truck parked outside the loading dock and the rep inside the cantina early in the morning only for the muffin basket to be empty… again. I should take that as sign that it’s time for me to squash my sweet tooth once and for all, but I can’t help but wonder why there aren’t more muffins loaded on the back of that truck just ready to find their way to my gut. If the muffin basket is constantly empty, shouldn’t that be a sign to someone at the cantina operator that the muffins are a top seller and need to constantly be in stock?
Let’s look at this from the perspective of an analyst for the cantina operator. On Monday and Tuesday, the reports from my point-of-sale tell me that I sold four muffins each day, then on Wednesday and Thursday, I sold none. What the analyst is missing, is the empty wicker basket and the sound of my grumbling tummy despite the fact that the locked storeroom ten feet away is filled with 12 more with delicious chocolate-chocolate chip and blueberry muffins. All she sees is that a sales rep stocked 20 muffins this week and only sold 8 and that it may be time to bring our muffin sales forecast down and quit reordering muffins.
And yet, be it muffins or mufflers, many businesses are guilty of the same big-data sin: allowing your data to dictate decisions and creating a negative feedback loop.
What can be done to improve our interpretation of data?
In short, a little hands-on experience can help. While finance professionals tend to be “behind the scenes” team members, the reality is finance needs to be in the trenches with their business partners. While our customer representatives and sales managers are making their routes and satisfying their customers, Finance, as a business partner, can watch the business processes unfold and witness how the operation runs. Finance should act as the interpreter of the metadata that is coming out of each transaction with our customers, machinists, housekeepers, or other people who are, in my words, turning the wrenches and keeping the business thriving.
While getting out into the trade on a regular basis may be challenging, there are other things we can do to calibrate on what our data tells us:
  • Create forecasts and budgets and review variances to forecast and budget at regular intervals. One of the key reasons to build a budget is to create benchmark against which we measure performance. Though even the most well constructed budget will have errors in assumptions, but the key here isn’t so much the accuracy of the budget, but the baseline that it creates from which Finance and Operations can discuss and debate. And it’s through those discussions that we discover issues in our data.
  • Compare the data to industry standards and benchmarks. In the same vain as creating a budget, industry standards can help us calibrate and ask the question “why are we different?” It will still take some work to understand differences between our business and the industry, but again, the idea here is to spark a conversation and find opportunity for improvement.
  • Make metrics a part of the culture. Develop simple, easy-to-read metrics and reports and incorporate it into all layers of the organization. Everyone from the CEO to the front-line hourly associate needs to know what the key metrics of the company are, and at least have some idea of what influences those metrics. When metrics are widely available and understood, everyone in the organization should be able to understand what needs to happen to move the metrics forward. In the case of the muffins, if inventory turn-over is a key metric in the business, then the front-line sales rep and merchandisers should know that they need to put more muffins on the shelf.
My only word of caution is that while metrics are powerful indications of performance, an organization can become so hyper-focused on those metrics that leadership will make decisions entirely focused on driving a favorable metric while ignoring the big picture influence on the business.
Whether you realize it or not, your business creates a wealth of information each day, and organizations that can harness that information will continue to succeed and grow. However, learning how to interpret the data and make accurate conclusions from your data is a crucial step in the process, and that learning cannot occur without discussion and constant recalibration.


What big data sins have you seen? In what ways have you or your organization learned how to calibrate your interpretations of data? Let us know in the comments section below.

Comments

Popular posts from this blog

Explaining Variances

As FP&A professionals, we’re tasked with relating the story of the business to the numbers in the P&L. Often, however, the story has many twists and turns that can be hard to break down. Seeing average cost per unit rising drastically over a short period of time can be alarming, but breaking it into the component (i.e. rate, volume, mix, etc.) can help us understand whether the change is a matter of poor management or favorable shift in business volume.   One of my favorite ways to present variance analysis is by breaking the variances down to their components – or drivers. For instance, in a manufacturing scenario, your drivers of expense might be wages & benefits, material costs, and hours worked. I’ve broken such a scenario down for you below which can explain the approach.   In this scenario, a manufacturer ran production for an order of one of its products   Budget Production Cost $73,000  Actual Production Cost $71,100  Cost Higher/(Lower) ($1,900)   On the surface, p

Driver-Based Modeling

  Driver-based Modeling Driver-based modeling presents major advantages when budgeting, forecasting, and running scenario analysis. A driver-based model allows you to test out individual assumptions and see how minor (or major) changes may affect your business. Further, a driver-based model allows for more detailed variance analysis and dynamic forecasting. For instance, if you are a hotelier and your hotel’s occupancy changes, then you would expect variable expenses such as housekeeping to change. A great driver of expense in this scenario would be rooms occupied. If your occupancy changes, then the difference in rooms occupied times the cost per housekeeping clean is the amount of variance you would expect to see at month-end.     How do you build a driver-based model? First, identify the cost and revenue drivers in your business. What data can you get ahold of, and what impact does that data have on your business. While initial drivers may be statistics captured as part of your mont