Building an analytics-driven institution: 7 signs you’re on the right path
- Analytics require a sustained investment, leadership, and a campus-wide buy-in
- There are seven clear signs you're handling analytics correctly
- Most institutions already have what they need to get started
Within higher education, there is near universal recognition that — buried in the mountains of data living and multiplying across our campuses — lie insights that can help solve some of our toughest challenges. How to increase enrolment, improve student success, or better meet the needs of non-traditional students. Or simply how to do more with less.
Analytics is the practice of getting at those insights and getting them into the right hands at the right time in the right format in order to improve decision making. So why doesn't every institution have a robust analytics programme?
Because it's not that easy. It takes more than a one-time implementation or a single technology solution. It requires a fundamental change in the way an institution thinks about, manages, shares, and applies data to achieve its strategic goals. It takes a sustained investment, strong leadership, and campus-wide buy in.
But institutions that have at least started down this path — those that aren't waiting for a silver bullet — will reap significant rewards when it comes to remaining competitive, managing growth amidst constant change, improving learning outcomes, and modernising the student experience.
So if building an analytics-driven institution is a journey, how will you know you're on the right path?
Here are 7 signs that you're building something sustainable:
1. You're Thinking of Data as an Institutional Asset, Not a Departmental One
Every department has important data and, often, their own systems for managing it. They can be reluctant to share because they fear information will be misused or misinterpreted.
But integrated data is far more powerful than siloed data, and institutions struggling to compete can no longer afford barriers between systems, people, and information. Not when technology has made it easier than ever to manage access and identity rights, as well as to turn disparate data into powerful insights.
In the 2017 paper "Finding Insight in an Ocean of Data", research analysts at Ovum noted that — when it comes to the information-sharing that is so key to analytics success — cultural barriers are often far greater than technical. They found that, in many cases, people are overly concerned about data misuse, misunderstand data sharing rules or regulatory requirements, are disproportionately focused on departmental performance over institutional goals, or are simply averse to change.
This is not to say that being concerned about data integrity, security, and privacy isn't valid or even critical. In fact, the more data we have, the more we need solid governance plans to ensure that it's used carefully and correctly.
But a greater openness to information sharing, along with adopting new systems and processes that make this possible, is a key sign that your analytics programme is on the right track.
(For tips on how to change institutional culture, read "Why analytics comes down to people.")
2. You're Empowering End Users
Self-service is the direction analytics is going. It can no longer be a specialised practice, with end users "ordering reports" from a walk-up window run by IT or Institutional Research. Too often users end up with long waits and data that isn't quite what they need. And technical staff end up in permanent reactionary mode, "filling orders" instead of focusing on more strategic activities.
To realise the promise of analytics, every dean, department chair, recruiter, advisor, and other key player on campus should have information at their fingertips, presented in ways that are relevant to their role. Fortunately, technology is headed in this direction as well.
At Ellucian, we're focused on analytics for the everyday user. Users who've gotten accustomed to an "app world," where the experience is friendly and intuitive and you accomplish tasks and goals on your own time.
Why is this important? Look at any area of life, and you'll see more people adopting new technology and ways of doing business than ever before. In most of these cases, it's because older, more rigid systems have been replaced by tools built around the user's goals, interests, and skill level. Modern analytics tools are taking this same approach. They present information in an easy-to-understand, relevant fashion and guide users toward additional information to further enhance their experience. Which leads me to the next sign your analytics programme is on the right path.
3. You're Designing for Real People with Real Problems to Solve
One mistake many institutions make is starting the conversation about analytics with "What reports do we need?" instead of "What business questions are we trying to answer?" But the end goal is not simply more data, it's more relevant data that can enhance decision making by faculty and staff in each role on campus.
For example, the Registrar may need to start each day looking at different numbers than the Director of Admissions. They shouldn't have to comb through the same report or spend a lot of time massaging the numbers to get what they need. With a more mature analytics solution, content is persona-driven. Each user has custom views, and the options for slicing and dicing data are based on the questions — and the follow up questions — they care about most.
Contemporary solutions also have modern search capabilities. Users are guided not only toward the answers they need, but toward suggested or related content that has helped others in their role.
Modern analytics also take into account how each role is connected. While the Director of Admissions may not need student success reports on her daily dashboard, the ability to access certain retention data and integrate it with recruiting analysis can transform her approach to targeting best-fit students.
Again, I'm not suggesting a completely free and open exchange of data across domains. Governance rules and strict access are crucial for making this work. But successful institutions tackle this early on, so they can focus on delivering each decision maker the data they need from across the institution to succeed in their role.
4. You're Getting Your Technical House in Order
In addition to the strategic and cultural shifts required to build an analytics-driven institution, you need the technical infrastructure.
The most important factors are data access and integration. If you're like most institutions, you currently have data spread across multiple business systems, often in incompatible formats. While you can run a limited number of department-centered reports, you have little ability to leverage data across the institution. And even if one department downloads data and shares it with another, you run the risk of multiple conflicting or outdated sets of data in use over time.
Integration is paramount. But for integration to work, there needs to be a common understanding of the data across people and systems. Shared definitions, everyone speaking the same language. In this universal language, data is defined not by the application or department in which it's housed, but by where it fits within the interconnected business processes and decisions that take place every day across your campus.
Integration also requires movement. Data needs to flow between the people, applications, and analytical tools that work cross-functionally to achieve institutional goals. And that means breaking down technical and operational silos. An analytics-driven campus is a connected campus.
Other factors to consider are scale and flexibility. As the amount of data grows, you'll need more storage, along with greater processing power in order to analyse it in a timely fashion. And as the nature of the data changes (think images, speech, social media), you'll need more sophisticated tools to manage and interpret it. Building and maintaining all of this capacity in house will not only be costly, it may take you too far away from your core mission. That's why many institutions, across both higher education and other industries, are choosing cloud-based analytics solutions. The cloud allows you to scale bandwidth as needed, as well as take advantage of innovation with lower cost and disruption. (Read a recent Q&A with Becker College on why they chose a cloud-based analytics solution.)
And finally, an adequate commitment to ongoing training is critical to ensure your staff are equipped to manage and grow the effective use of analytics throughout the institution.
5. You're Dipping Your Toe into Next-Gen Analytics
"Next-gen analytics" means a lot of different things to a lot of different people. But it's basically about moving beyond basic business intelligence and reporting to deeper methods of data exploration and discovery. This can include machine learning, the Internet of Things, and the mining of "unstructured data" like images or social media.
For most institutions, these things are probably a few years off — the primary goal still being to get better insights out of existing data. There is one area of next-gen analytics, though, that is certainly within reach: predictive analytics.
Predictive analytics are about moving from "What happened and why?" to "How can we use this knowledge to shape and improve the future?" Predictive analytics can be applied to any domain, but perhaps the most exciting promise lies in using historical data to improve student outcomes, including retention and graduation. For example, Georgia State University analysed two and a half million grades earned by students over ten years to create a list of factors that predict which students are less likely to graduate. The university then used the data to transform its advising system, close achievement gaps, and improve graduation rates dramatically.
The barriers to getting predictive analytics off the ground include the practical (the time and skills required), as well as the technical (the ability to gather, process, and integrate the large amounts of data needed to produce meaningful results). But the emergence of more flexible, scalable cloud-based solutions, as well as more user-friendly tools, are finally alleviating some of these challenges.
I would urge institutions to focus first and foremost on building a solid, integrated, role-based analytics programme before worrying too much about next-gen analytics. But dipping your toe into predictive analytics sooner rather than later will not only produce significant insights, it will help the institution envision the ultimate power of analytics to drive change.
6. You're Making Ongoing, Sustained Investments
As I said earlier, analytics is not a one and done implementation or investment. Similar to HR, finance, and other cross-functional areas of the business, it should have its own set of strategic goals, budget, performance measures, and dedicated staff time. If you're not large enough to have full time staff dedicated solely to analytics, at least make sure it's an official part of someone's job duties — preferably several people, representing the different areas of the business using the insights.
If you're just starting a conversation about analytics, make sure executive leaders are at the table. Given the significant cultural shifts required, top down leadership is key. And like I said earlier, don't start by asking, "What analytics or reports do we need?" Begin with "What are our strategic goals, and what insights would improve our ability to achieve them?"
At its earliest stages, department heads should own the conversation and then partner with IT or IR all throughout the strategic, practical, and technical implementation.
7. You're Acting on Insights
Perhaps the most important sign that you're succeeding at building an analytics-driven institution is that you're acting on the new insights being generated.
This may seem obvious, but it's actually a step that gets ignored. As you put processes in place for gathering new information, make sure you initiate formal processes for reviewing and acting on that information. Particularly if the data reveals difficult or uncomfortable facts. How will you communicate these facts? Who will be part of the conversation? If the data suggests the need for strategic or operational changes, what resources will you require and how will you get them?
That's why institutional leaders are so critical to the conversation. They may be asked to make bold moves based on new and richer insights.
For so long, institutions were hindered by a lack of data. Now we have mountains of data, but are just starting to understand what it will take to harness it on behalf of higher education's toughest challenges.
The good news is that most institutions already have many of the tools and resources they need to get started. They're collecting and storing a rich array of data and generating meaningful insights at the departmental level. And research shows that faculty and staff across higher education believe in the importance and promise of analytics.
But there is no silver bullet. The key is to just get started. Begin the hard work of shifting your culture and technology toward integration. Focus on empowering people, not designing reports. And commit to making an ongoing investment in the people and technology required to ensure that quality data drives decision making at every level of the institution. In the end, the goal of analytics is to turn data into knowledge and empower end users with actionable insights.
For more info on building an analytics-driven institution, check out our analytics resources.