Why Your Non-Traditional Division Needs to Prioritize Its System
How Offering Self-Service Tools Can Take Non-Credit Divisions From Good to Great
Across our industry, the conversation around the need to leverage analytics and data is growing rapidly. It’s a topic that’s been on EDUCAUSE’s Top 10 IT Issues list for years in many forms, but it’s still an area of some confusion for postsecondary leaders. Of course, in the corporate world, data has been at the forefront for years.
In this article, I’m going to draw on some of my personal history—having worked on both sides of the divide—to discuss what colleges and universities can learn from the private sector when it comes to leveraging analytics.
Why Am I Talking About This?
My career has been split roughly 50-50 between higher education and private industry. As a management consultant, I had the opportunity to work with a number of private sector clients, mostly large companies undertaking significant technology projects. At the University of Arizona I’ve been responsible for multiple ERP implementations, and for combining our Business Intelligence and Institutional Research functions. During this time the world of analytics has expanded dramatically, and private industry has been aggressive in its use. We can learn from this.
Leveraging Data to Understand Customers/Students Better
In learning from the private sector, we can start by looking at what analytics are being used for and identifying use cases that would or could apply to higher education. We can also look at the technologies that are being used, and the skill sets associated with those technologies. Finally, we can look at how these use cases and technologies are being approached from an organizational and methodological perspective.
Of course, we are already doing some of this. For example, in the private sector the concept of customer segmentation has evolved towards finer-grained segments, or precision marketing. Those segments are defined by common characteristics, including behavior, so segments are dynamic. We see this in our own lives: in our interactions with social media, search engines, and the like. Many institutions, including ours, are working to bring this approach to bear with students.
In fact, student success might be the most prominent general use case. We can use technology and data science to identify students who are at greater academic risk, then employ various intervention techniques to try to make a difference. A lot has been done here already, with some significant success stories, but there’s much more to be done and more to be learned. Can we bring additional data sources to bear in assessing risk? What kinds of intervention techniques work well for what kinds of students?
Another use case for applying customer segmentation is in development or fundraising. The considerations are similar, in that institutions are increasingly taking a precision approach based on a larger and larger set of information, including past behavior. As with student success, there is the need to analyze not only who we are targeting, but also how we are targeting them, and which approaches work best.
Neither of these applications are new to higher education, but arguably the stakes are higher, segments are smaller and more dynamic, and margins (to borrow an industry term) are thinner.
The Opportunities to Leverage Data are Limitless, But Your Time Isn’t
There are a number of other potential use cases. There is a large and active space in learning analytics to understand how students master material, and to respond dynamically to that. Sentiment analysis can be used to analyze what our various constituencies are expressing, and how they are feeling. Information security is an obvious application for dynamic monitoring and analysis of patterns. Another possibility is employee retention, to see if we can detect patterns of retention risk, and then intervene in a more proactive way. There could be opportunities to detect fraud, again by looking at patterns of behavior. And there are opportunities as well in the analysis of energy usage, and of resource usage in general.
Unfortunately, there is no shortage of opportunities. How does one choose? I’d suggest looking first at the benefit, then the cost, and then the risk. I would try to find the middle ground of realistic estimates, recognizing that this is difficult when things are new, and when people seem to line up either as advocates or skeptics. So I wouldn’t bet the ranch. But I would make a bet, which I’d characterize as an investment: The safe place is not going to move things forward. Arguably, this approach borrows from the business world, but I would also take into account the mission of higher education, the pressures we are under, and our responsibility to try to make it better.
Making Data Work: General Patterns of Use in Higher Ed and the Corporate World
To a point, higher education institutions and corporations tend to use data in similar ways. The technologies and techniques vary, as do the use cases. But the general pattern is:
This is a more complex process that entails more work than it might appear at first. We tend to focus initially on the insight to be gleaned from the analytics, but insight alone is not enough. We need to take effective action, which in turn takes us deeper into the organization—into business processes and the assessment of their effectiveness. It points us towards a culture of more measurement, more analysis, and more management of data. All of this is occurring within a context where we are outsourcing more and more of our data by employing Software-as-a-Service (SaaS), which can pose additional challenges to access and integration.
The Limitations of the Lessons: Where Higher Ed and Corporations Tend to Diverge
Analytics is a lot of work. Private industry, at least in the case of larger firms, can afford to make significant investments to achieve incremental gains. Individual institutions are not generally in a position to do that, because they lack the necessary scale. Institutions will need to choose their battles, and as much as possible, leverage the available technology, the experiences of others, and available tools. If there are any easy gains to be made, they will be made relatively quickly, and the mode will then become one of incremental improvement over time.
Another important issue surrounds the ethics of data privacy and the appropriate use of information. Here, too, we can learn from private industry in terms of recent situations involving some of the bigger players. We will need to pay attention to transparency and informed consent. The GDPR is, of course, in play as well. I think this might be confusing and frustrating for a while, but it will sort out over time. This issue should inform us, but not deter us.
Institutions and individuals are at different places along the continuum of valuing and using data, and all of us work with issues, with technologies, and with people. Our goal is to move all of that in the direction of making more data-informed decisions. It is not a single project or even a single initiative, but an evolution in culture. Analytics is a big part of that, and we can learn from the private sector in doing our work.
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I’m sometimes asked what I think the biggest difference is between working in higher education and working in the private sector. The work is very similar in many respects. Certainly there are differences in terms of the regulatory and market forces at work, and in the ways we measure success, but in my opinion the biggest difference is how we perceive time—in particular, the value of time. The private sector is acutely aware of opportunity costs as they relate to time. In higher education we are certainly aware of those costs, but at nothing like the same level of acuity. But the higher education landscape is changing, and our perception of the value of time will surely change along with that.
How Offering Self-Service Tools Can Take Non-Credit Divisions From Good to Great
Author Perspective: Administrator