RESEARCH & RESOURCES

Q&A: Analytics in Higher Ed Can Yield Rich Results

Leaders at two universities well-known for their work with analytics -- Arizona State University and the University of Arizona – discuss the challenges and opportunities that the use of analytics presents.

When it comes to BI, some higher education institutions are known and respected for their analytics leadership. In the first half of a two-part interview, BI This Week spoke with Henry "Hank" Childers, senior director of enterprise information and analytics at the University of Arizona, and John Rome, deputy CIO and BI strategist at Arizona State University, about the challenges and rewards of working successfully with analytics and higher education data.

Rome has spent more than 20 years at ASU and is a pioneer in data warehousing in higher education. He built ASU's first data warehouse in the mid-1990s and has since coached both educational institutions and industry on building data warehouses, best practices, and the strategic importance of BI. Rome now focuses on dashboards, design, academic analytics, and most recently, big data.

Hank Childers has served as project director of the enterprise systems replacement project at U of A since 2008, overseeing a campus-wide replacement of most of the university's administrative computer systems. He also oversees the BI solution implementation, expanding the availability of business information to managers and executives. Childers also has a wealth of managerial and technical experience in financial services, hospital information systems, and university administrative data processing.

BI This Week: John, in your 20 years in BI and analytics at ASU, what are some of the things you've done?

John Rome: We started building our first data warehouse at ASU back in 1992. ... We were asked to build an economic development database. We had no idea what "economic development data" was, so we decided to put student data into a relational database, and that kicked it off. We realized that we were on to something back then -- this was when some of the first-generation query tools began showing up. It's been over 20 years, and we've spent most of the time just putting data into this data warehouse. That's the core of our analytics -- using this enterprise warehouse to try to help the university understand that data is a university asset, and that departments and colleges don't own this data. That's where the power is and the integration comes from.

Hank, you've also been working in this field a long time. Can you summarize your background in BI and analytics?

Hank Childers: I got exposed to analytics, which we just called data warehousing at the time, about 20 years ago, like John, but I was in private industry. I was working as a consultant with Pillsbury in Minneapolis. I ended up on a project [in which I] actually engaged Bill Inmon to come in and consult with us for about a week. ... I continued to work with analytics off and on, based on what projects came along. When I started working at [the University of Arizona] 11 years ago, I was immediately involved on the analytics side, although initially as a user more than a supplier of technology.

Analytics in higher education has come a long way, but it seems that it's often not on par with the private sector. Is that fair to say, and if so, why is that?

Childers: For years and years in the private sector, particularly in retail manufacturing, [the focus has really been] to understand consumer behavior and try to get out in front of it. In that sense, analytics is an important marketing tool and has been for a long time.

I'm not sure I completely agree with your statement, but I do agree that there's been something of a delay in higher ed applying analytics to data. That's probably because the profit motive doesn't drive our behavior. We don't always pay as much attention to what's happening in the business world because we think, well, that's about profit. We sometimes miss the important point underneath.

I certainly don't mean to imply that either of you has been behind in this area. You've both clearly been pioneers in analytics in higher ed, and you've garnered attention for that. John, what drew you to realize the value of data, and to begin gathering it into a data warehouse at ASU?

Rome: It started around data access. It was really about that -- we had these assets, this data, that was really hard to get to, or sometimes there was an ownership issue. ... That's what drove much of my interest. I knew there was power in the data, power in integrating it with other systems and other types of data. I took it as a goal -- let's try to get this data exposed because there is clearly value there.

Can each of you offer some advice to schools struggling to capitalize on the potential of BI and analytics?

Rome: [In a recent presentation,] we talked about getting leadership support. Get funding! One thing I've really noticed is that in the successful higher ed implementations, there's typically a champion. You need someone who not only understands technology but also understands the business. That's clearly lacking at some institutions.

It may be that it's just a hard position to fill. ... My advice is, find someone who really cares about data and technology. When you look at successful analytics in higher ed -- the University of Arizonas, the Arizona States, the Rensselaers, the NYUs, the Virginia Techs, the University of Illinois -- there's typically a person with a passion for data driving it.

Childers: It also may be that there's sometimes an uneasy relationship between IT and business intelligence. The data champion needs to be able to work with the IT group, but may not arise directly out of that group, because so much of IT historically has been involved with technology and transaction processing. However, the value of the information is really seen from the business side -- that's where the investment is really going to come from. My advice is to look for support on the business side more than the IT side.

Rome: I agree. It's interesting. With many successful organizations, there's a business driver behind it, or the leader comes from business, like Hank. In our case, lots of our early success was partnering with institutional research. Clearly, we had an understanding of the technology and they had a clear understanding of the data. I think, as Hank said, that a partnership between business and IT happened. Often, it doesn't matter organizationally where the project fits. Sometimes it might be in IT, sometimes it might be a collaboration between two departments.

Hank, has your background in private industry been helpful at the U of A?

Childers: Yes. Actually, I was a consultant at Pillsbury. I worked with a lot of different firms, but that just happened to be the first one. I'd been reading Bill Inmon, and then this particular business opportunity [to work in analytics] came along -- I recognized it and went from there.

In general, I think private industry experience is very useful. As with many things, however, any kind of experience with a single line advancing up through a discipline can be greatly augmented if you have a leg in another camp as well -- broader experience, that is. I don't know that there's magic in private industry in particular... It's just that it's a little bit different. Maybe "synergy" would be the best word.

Can each of you suggest a good starting place for analytics? Many schools are now collecting lots of data, but I'm not sure they often know what to do with it. Do you look for a pain point to get started? Do you look for some good clean data?

Childers: I look for pain points. I think people are more moved by need than by recognizing an advantage, so to speak. ... I'm more needs-driven. I worry that if you start at the other end, saying, "Hey, we've got some really great data quality" in a given area, there may not be value there. In any case, the quality can never be good enough. In fact, it tends to create an opportunity to say, "Well, we need to do more to clean up our data before we try to make use of it."

Rome: I would say that when we look at BI projects, a lot of them focus on student retention -- how do I identify students at risk, how do I help the yield process, how do I grow research, how do I get alumni to give? Lots of that is based around either revenue generation or cost savings.

I also look at history, though. For our data warehouse, it wasn't so much student data; it was financial data. Once we made financial data easy to access, that was one of the tipping points for our warehouse, so even though in my heart I would say, "Let's focus on students," the university was focusing on money.

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