 We've reached a tipping point. For many of our institutional leaders, the wealth of data and the maturation of analytics tools are creating a critical mass to engage in data-informed solutions. Analytics can help shed light on questions surrounding a host of complex issues, like the uncertainty of funding, the difficulty of student retention, the problem of college affordability, and more. Analytics is the use of data, statistical analysis, and explanatory and predictive models to gain insights and act on complex issues. Analytics can provide insights to a wide variety of uncertainties for an institution. For this reason, analytics must start with a question or hypothesis. Once you understand what it is you want to answer, it's relatively easy to understand what data you might need to answer that question. So that's the easy step. The real challenge, I think, with analytics, and it relates to benchmarking as well, is you need to build systems within member institutions to actually collect that data and collect that data in a consistent manner. And so the challenge for many institutions is to see what data is available, what data is being produced, and then being able to capture that near real time so that you can do analysis on it. Once the data is collected and put into a data warehouse, it's time to decide what sort of analysis best suits the questions asked and the data collected. So, budget, you know, annual budget stuff. You can do that out of a transactional system. You could probably pull it into Excel and slice and dice it and do some level of analysis. But analytics, for me, it's really kind of taking that all and moving it to a very, very different level. And in order to get it to that very, very different level, about thinking, about, you know, trending and what if-ing, and you need to bring the data into a format where you can literally kind of take the data and move it around in certain ways. And patterns emerge. Predictive modeling allows institutions to create a model of their data in order to predict the probability of an outcome. These models are being used on a number of campuses to drive innovation. At Carnegie Mellon University, the Open Learning Initiative collects analytics data to drive feedback loops to help students, instructors, course designers, and learning science researchers. At Austin P. State University, they have developed Degree Compass, which uses predictive analytics to rank course choices according to how well each course might help the student progress through a chosen program. Analytics very much has a part to play because it really can go back and use the wealth of data that's sitting out there. Institutions are sitting on terabytes of historical data, and then we're able to then mobilize that data to really be able to make those decisions informed. While analytics are being used more widely than ever before in higher education, the process is still maturing. It's important to recognize that really any system within a university that generates data is a potential marketplace for analytics, which means that right down to student activity, to activity within a library, to even administrative activity, you know, where are we allocating, how are we allocating our resources, what's the impact. And I think one of the dangers with analytics is if we don't solve the problem of maturing the process, of collecting the data, and actually even measuring the data, we're going to start making decisions, and institutional leaders will start making decisions based on data that's bad. Being analytical is not new at all, and people, philosophers and others, have thought for a long time about what it means to be analytical and to be very good at that. The tools of analytics are what's changed lately, how things like big data and now business intelligence kinds of things, and now learning analytics that's coming along. That's what's kind of new and different, I think. Humans tend to make decisions based on intuition most of the time. It's effortful to make decisions based on analysis and data, but I think the industry experience is such that it can be a game changer, it can be a differentiator in higher ed is really picking up on that.