What you need to get started

If perfect data and a fully formed hypothesis are not needed to begin using predictive modeling, what is needed?

“You need to understand the metrics you’ll want to be gathering and the outcomes you’re looking to achieve, and [you need to know] the data required to track [these] so you can show measurable progress,” Seaman says.

That, he says, requires a highly collaborative effort, with participants “from the central office all the way out to the individual schools” or departments.

Schools that have invested in building out a more robust data offering, and that have begun aggregating student components of data records, test scores, and other information, will benefit from having all this information pulled together. But even schools that haven’t had the resources to do that can begin to use predictive analytics.

“There’s no prescriptive that says you have to have a certain amount of data before you’ll be able to get value,” says Gold.

“You can have five years of longitudinal data in one area, two years of data here, eight years there, and that’s great. Just start,” he suggests.

Another important element in getting started with predictive analysis is a group of people with advanced analytics training. “Finding individuals with these skills is a prerequisite for doing these things at an institutional level,” Gold says.

In terms of getting buy-in from the top, it can help to start small, says Amos. Rather than say you’re going to predict student achievement across the board and come up with a prescription for improving success, pick a specific area on which to focus.

You might measure on-time arrival of buses, for example: Choose key performance indicators to measure bus arrival time; determine what’s good, fair, or poor in terms of bus arrivals; and begin to analyze whether and how that has an effect on student performance. Then, you can begin making decisions on how to improve those numbers–after which you can move on to another area of focus.

“These are relatively short processes,” Amos says. “They’re not multiyear projects. It does a lot to show small successes early to bring the cultural environment along. And these are projects that can be funded in small increments.”

Another good place to start is driving cost savings.

Using advanced analytics often starts on the business side, looking at strategic areas of an institution’s mission to see where improvements can be made. “Cost savings are the low-hanging fruit,” says Amos–and top-level decision makers are more likely to budget for a new software program if it can show immediate financial benefits.

Ways to use predictive modeling

The uses of predictive modeling can be endlessly varied, and they can run the gamut from improving efficiencies to saving money to enhancing student achievement.

Just a few examples include: