Data-Driven Decision Making

There's been a lot of talk about using data to "make" decisions, but I prefer to think about using data to "inform"  the decision making process.

Take for example the process of buying a car. You could just buy the first car you see, the one that's most popular, the same kind you always buy or the one the salesman recommends. Or you can start by collecting some data. You may look up information online, solicit advice from friends or relatives, visit a dealer, research auto loans. Once you have gathered some data, you have to evaluate it and decide what is most likely true, what is most valuable in making a decision, whether you need more data and if so, what kind of additional data and where can you get it. The data doesn't necessarily make the decision, it informs the decision making process.

The same process can be applied in schools. For example, instead of blindly using the same textbook year after year, you can examine the data related to the use of the textbook like test score is that subject area, research alternative course materials like online educational resources, compare them to current standards, research best practices in other schools and districts and then make an informed decision on whether or not to continue to use that textbook.

My school is currently in the midst of the open enrollment application period - a critical process that will determine how many students we will have next year and consequently what our budget will be and how many teachers, laptops, server space, online textbook licenses, etc. we will need. Since we cannot wait until the application period is over to do our budgeting and staffing for next year, we analyze the available data on the number of applications that are coming in compared to prior years to inform our decision making.

The blue line indicates the number of applications received to date this year. At this point in the application period, we are ahead of pace compared to last year, but slightly behind compared to 2010 and 2009. We had to make some assumptions and adjustments to this year's projections because in previous years, the application period was only three weeks long and this year it is three months. Even so, analyzing this information will help us make some projections about enrollment numbers that will really help our planning for next year. It will also help us make midstream adjustments to our marketing efforts. If application levels go up compared to other years, we can cut back on our Internet marketing expenditures. If application lag behind, we can shift some funds to pay for an additional direct mail campaign. In either case, the data informs the decision making.