How to Apply Comparative Analytics to Make Better Decisions and Forecasts
American computer scientist Kurt Bollacker once said that, “Data that is loved tends to survive.”
While many of us might find it difficult to “love” data, it is the lifeline of the organizations we run. Data tells us how we’ve performed in the past, what is likely to happen in the future, and how we can alter the path forward, for better or worse. So while we may prefer to ignore data than love it, we do so at our own peril.
In fact, too often, leaders of public institutions like schools, districts and municipalities either willingly or through benign neglect fail to apply a necessary love of data, and in doing so, fail to maximize the potential to make better decisions and more accurate forecasts.
Too many times, it is tempting to believe that you have all of the data you need, when in reality, there is data out there…keeping secrets from you…causing you to “not know what you don’t know”…and as a result, potentially leading you to draw inaccurate conclusions and make under-informed decisions. Not only can leaders fail to see the big picture, they may be looking at the wrong picture altogether.
After the past two years, and the uncertainty of what lies ahead, it’s never been more critical to have the most complete data set possible so that we are making the most informed decisions and the best possible prognostications.
Comparative Analytics and Errors of Omission
The future of planning and budgeting is something called “comparative analytics.” Put simply, comparative analytics refers to the process of examining your own organization’s data and performance against those of your peers and competitors to draw more informed conclusions and to make better decisions. It’s a methodology for avoiding one of the greatest perils to critical decision-making: thinking we have all of the information we need and omitting a potentially decision-changing data set.
The good news: For schools and municipalities, the performance and budgeting data for all of your competitors and benchmarked peers is publicly available. All you have to do is go find it.
If you can view your own historical data and forward-looking projections against those of neighboring entities with whom you may be competing, won’t you be able to make more confident decisions and more strategic allocations of time, treasure and talent? Anything less, and you’re making critical decisions in a vacuum.
To provide just one illustrative example, let’s say you’re examining a school district’s expenditures on instructional investments for the past three years, and the data show that the district is accounting for 10% annual increases in that budget category. Sounds promising. The district appears to be investing in educational outcomes, the chief metric for educational excellence.
Not so fast. Unfortunately, all that data set provides is an insular, backward-looking reporting of past events — and only the district’s own. Now imagine a scenario in which district leadership could instantly compare their own budget allocations against a neighboring district. Suppose the neighboring district is reporting student achievement data that far surpasses the first district. And then imagine that, through just a few clicks, we can see that the outperforming district is allocating a much larger budget to instructional line items, likely accounting for (at least in part) the better achievement outcomes. Despite the scheduled 10% increases, the spending gap remains large between districts, and so does the disparity in student achievement.
In other words, while District A at first blush appears to be investing in educational priorities, when we look at the big picture, we discover that District B is far outpacing District A in student achievement and taking an entirely different approach to prioritizing instructional spending.
How that might change District A’s assessment of its own budget allocations, and how might that data inform a decision about how to allocate public funding going forward?
Failing to “love” all of the data, in this example, is how an error of omission can quickly lead to errors of commission.
How to Avoid the Most Common “Errors of Omission”
If getting access to the complete set of all available data were difficult, we’d understand why someone might be tempted to take shortcuts or expedite decision making. But practically everything a public employee or leader needs to equip themselves for optimized decision making is publicly available, much of it required by mandate to be made readily accessible. So given that the “secrets” are out there, let’s examine what some of the most common self-inflicted errors of omission tend to be:
(If you would like any of the following reports pulled for your district or municipality and its relevant peers, contact us and we will send you a PDF report within 48 hours at no cost or obligation).
Error #1: Not Going Far Enough Back
The more historical perspective you have, the better you are able to draw conclusions as to what’s driving trends, and what’s likely to change or continue. Too often, data analysts look only one or two years back, when there could be some revelatory trend data hiding in the entity’s more distant past that would prove informative and applicable to the present and the future.
Error #2: Missing the Comparative Intelligence
Looking at your own data only is an error of omission that can, as illustrated above, lead to errors of commission. Here we look at comparative analytics that demonstrate how a sample school district allocates its own budget categorically relative to how all districts within that region do. Now we know if we are overspending, underspending, or misallocating resources, based on how our peers are doing their own budgeting. Look at the disparities and gaps, and consider what that data is trying to tell you.
Error #3: Failing to Connect the Dots
How is budgeting data aligned with performance metrics? Isn’t that the key to truly understanding whether our budgeting decisions and forecasts are aligned (or misaligned) with outcomes? With just a few clicks, you should be able to overlay key performance indicators with budgeting inputs to see if you’re getting sufficient return on investment, or whether you need to redirect funds and either double down or reallocate resources.
Error #4: Not Seeing How Small Data Drives Big Trends
If you’re not examining historically significant data sets, and you’re not comparing both past performance and future forecasting against competitors or peers, you can’t possibly draw accurate correlations and conclusions relative to drivers of big-picture trends. One of the most critical trend data sets for schools and districts, for example, is enrollment. Enrollment data not only reflects success metrics of all kinds, it directly impacts per-pupil funding. The small discrete data points all come together to influence the larger trends we all hold dear and measure ultimate success or failure by. Failure to put the pieces together and see the entire puzzle is perilous, and an unforced error in the modern age.
Love the Data that Survives
Perhaps it’s too heavy a lift to expect everyone within leadership at public institutions to truly “love” data. But we do urge that those in positions of critical decision-making at least embrace all that data has to offer…and all that lies in wait to undermine decisions if leaders neglect to listen to the secrets data is keeping from them.
That “loved” data will not only survive, as Bollacker suggests…it will help government institutions thrive.
Unlock the best-kept secrets today! If you would like any of the referenced reports above (or others) pulled for your district or municipality and its peers, contact us and we will send you a PDF report within 48 hours at no cost or obligation.