Since publishing Data Science in Higher Education, my email inbox has been flooded on a regular basis with questions about how predictive analytics can be employed at colleges from all over the world. One question I routinely get is how data science can fit into the college planning process. As it turns out, though, data science is rarely the solution to any problem in higher education.
Ask anyone who has billed clients in the education industry and they’ll tell you the same thing I’m about to tell you: people think data science is magic. This idea is really good for people who are doing the consulting, but it’s really bad for institutions because it causes leaders to fall into a Resource Requirement Swirl (RRS). Time and time again, data science is seen as some sort of product enhancement solution that is a plug-and-play solution to declining enrollment, increased administrative overload, and students who are leaving college to do other things.
Don’t get me wrong: when appropriately planned and implemented, data science can take institutions to the next level. Complex predictive analytic workflows can empower campus leaders to make data-driven decisions in a timely manner that can have positive effects on student success. There’s not even much of a barrier to entry anymore, either. In Data Science in Higher Education, I introduced data science in the most simple way imaginable: with the R statistical programming language. Now anyone with a computer can learn how to take real institutional problems and analyze them with today’s hottest and most efficient learning algorithms.
As with any new tool, great caution must be exercised when deciding to implement anything — especially if it’s going to cost your institution money. I posed a question in my book that I think is skimmed over too often: Do we have the ability to do data science, and if so, how do we know we’re doing it right?
When a data scientist like myself combines a handful of different databases together, wraps it around a procedural framework that fits your college, and then packages it up in a detailed analysis and solutions recommendation, you already know that data science has an measurable value for higher education leaders. But how do you actually measure that value?
The value of data science comes from the person interpreting it. Neither the amount of money you paid nor the complexity of the systems you have implemented will be of any value without the right people translating the data science into actionable intelligence. Data science does not drive decisions; data scientists do. You can buy the fanciest piece of software money can buy, but without the right people to use it you’ll have wasted your money and time.
If you want to invest money in data science but don’t know where to start, just do this: invest in data professionals. No amount of software tools or fancy frameworks can replace a good, competent analyst. Whether you only have the budget for Excel or want to dump hundreds of thousands of dollars on a product, remember: you still need good people to use the tools in your institution’s toolbox.
Pickup a copy of Data Science in Higher Education today.