In the early 2000s, ABC Network struck ratings gold by offering millions of sympathetic TV viewers riveting accounts of extreme home building on Extreme Makeover: Home Edition. In the show, designers teamed up with big-box home improvement retailers and community sponsors to build uniquely high-tech, high-design homes for families in crisis. The reality for us more pragmatic folks is that there are better home remodeling shows now available on TV today. These shorter programs promote not biting off more than homeowners can chew, with much more constructive guidelines that, in the end, create more solid foundations for future projects. The same is true for Higher Education institutions facing the latest challenges involved in getting their own technology house in order in the new age of big data and analytics.
An institution of higher learning is an exceptionally unique organizational culture. And, in many ways, the best Higher-Ed institutions share similar characteristics that reside within a healthy family dynamic. Anyone who’s spent time on a university campus will tell you it’s a living, breathing entity that evolves over time, with the one constant being specific, time-honored obligations toward students, faculty, parents, alumni and its surrounding community. In today’s high-tech world, the schools that most effectively leverage information with a strong data analytics foundation will have the most success, and improve the college experience for all concerned.
Sharing the top four lessons learned from university work. Virtually all Higher-Ed institutions have existing information technology assets with basic analytics in place. Universities are continually challenged to expand their technology assets and develop new analytical approaches to improve their educational mission, given the increasing competition for quality students, quality faculty and endowment resources. Success in this endeavor requires an in-depth understanding of what makes Higher-Ed learning communities tick.
First, it’s important to recognize the culture of consensus-building within Higher Education. Universities are nothing if not a large community governed by small committees. Second, keep in mind that multiple requirements will support multiple solutions. Third, expedite solutions with quick proof-of-value endeavors to manage expectations. As educators who must answer to many groups—students, faculty, alumni, board, and governing agencies—when feet are held to the fire, be prepared to get singed. Last, as a learning environment, understand the lifecycle of the student and the implications to each different department.
Higher Education-based organizations have a wealth of new analytic opportunities. Below are some hot-button issues that are currently receiving a lot of attention in Higher-Ed analytics today:
Advancement, Development, Endowment
Help universities find more donors and increase their endowment through better relationship management
Student Acquisition, Retention, and Reducing Attrition
Help universities recruit the right students, retain those students, and support their graduation on-time
Help universities understand the true discount rate with insights to tuition, grants, aid and scholarships
Help universities with automated, sustainable and scalable IPEDs reporting functions
These solvable analytic issues have enormous potential for providing Higher-Ed institutions with invaluable information and business process tools to thrive in the 21st century and beyond. The key is to avoid trying to achieve them all at once. From our experience, college institutions will actually get more benefit from taking on these programs in small doses if they hope to achieve better functionality at less cost.
Avoid the extreme, enterprise-wide plan that’s too large in size and scope. In construction, the smaller projects involving new bathrooms, kitchens, or basement build-outs are better suited to achieving the number one goal, which is to build a foundation for success.
Again, take the premise behind Extreme Makeover: Home Edition, a marketing juggernaut that was must-see viewing on Sunday nights until the show ended its run in 2011. As an “altruistic-reality TV show,” what made the show’s model so brilliant was its exploitation of both philanthropy and suspense to effectively keep viewers tuned in, with its “can’t look away” combination of unique family financial/health circumstances, down-to-the-wire impossible deadlines and unexpected, sometimes rather costly issues that arose during construction.
Great TV? Sure. Was it a solid project management recipe for long-term success? Not remotely. In most cases, more practical solutions were sacrificed in the name of extravagance and hype. Many homeowners were forced to give up homes because they couldn’t bear the increased cost in property taxes or the high-tech nature of the homes was ultimately too expensive to maintain.
Regardless of whether it involves a house or data warehouse, the overriding mantra in any type of building project is, “Construction is full of unexpected events.” Believe it, even for smaller projects. Giving in to the temptation to fit everything you want into an all-encompassing project that consists of Phase One and only Phase One is painful viewing indeed. Building a good foundation is just what it sounds like,and our keys to success are three-fold: a solid infrastructure that is well-suited for growth; well-defined metrics; and a realistic knowledge of what your data can and can’t do for you.
Take the time to establish your vision and build a Roadmap. This can’t be overstated. Solid planning behind smaller, phased additions are what pave the way for enhancing analytical capabilities, and when properly set, form a more durable foundation for future endeavors.
Roadmaps are first and foremost a team exercise incorporating stakeholders from all areas of your organization (from Applications and Admissions to Alumni relations).
- Do you want to attract more of a certain type of student to your university?
- Do you want to engage your alumni and elevate their participation into your organization?
Determine the key drivers that will aid your organization in accomplishing that vision. Key points in building out your roadmap include starting small (quality over quantity), avoiding the all-encompassing first phase and embarking on a short sprint-phase that focuses on delivering specific key drivers well.
It’s crucial that roadmaps include an accurate assessment of assets, including resources, data, infrastructure, and current analytical capabilities. Often colleges have a tenuous grasp on their actual vs. perceived data assets, lacking clarity in how they can use these assets in new ways to everyone’s best advantage. Further, if a Higher-Ed information system is also fundamentally rocky or outdated, attempting to incorporate a new process to it would be unjustified both in terms of wasted time and expense.
Building a sound analytical structure requires the right balance of components. Just as a bathroom remodeling still requires mechanical, electrical, plumbing and incorporates sink, tub and shower fixtures whether it’s 60 square feet or 600, a best-in-class analytical foundation is always based on six specific principles:
- and Technical
Even small projects use all of those guidelines to create successful outcomes.
Verify gaps in your assets, and then create a tactical data acquisition wish list that fulfills a longer-term analytical strategy. This discovery process will determine whether valuable data currently exists within your institution’s data structure. If not, this exercise can provide both the means to get it and where it should reside. For example, if you’re pouring a foundation for a new bedroom addition and already have plans for a future adjacent family room in two years, by all means get that slab in now while the concrete mixer is there and use it as a patio.
Create the ability to rapidly incorporate new sources of data when it becomes available. If your goal is to attract a certain type of student, what are the attributes that make this candidate desirable? Within the many systems of your organization, it’s probable that your collegiate data warehouse stores a wealth of information about these types of students. It simply becomes a logistics exercise to uncover it. When joining data from different areas of a Higher-Ed organization, departments also sometimes find they can fill perceived gaps. The trick is to ensure that data governance rules maintain the integrity of the data during this process. Removing data silos within an organization can often enrich your analytical capabilities simply by giving more context and meaning to the data you already have and currently utilize for other tasks.
Move that mouse. The biggest houses aren’t necessarily the best houses.
Extreme Makeover: Home Edition’s host Ty Pennington was also really loud. Many of the show’s design solutions were wrong for the site and surrounding neighborhood and ultimately failed to address long-term needs. These are good lessons to remember within the context of any type of improvement scheme.
Technology provides great opportunities for Higher Education to create and advance progress within its educational mission. With regard to how Higher-Ed technology can achieve more progressive analytic solutions, streamlined warehousing, integration and governance, smaller-scale initiatives are much more practical bets, carrying low risk and reaping higher rewards in terms of stronger foundations and overall returns on investment. For active learning communities that are highly invested in re-energizing and growing their campus communities for future generations, they can build on these best-practice successes in all departmental areas, from Application Processing, Registration, Alumni, Grants, to IPEDS and high-level University Advancement and Development programs.
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