Data is essential for decision making on campus. College and university leaders often consult data from the student information system or learning management system, in addition to facilities or budget data. Adding labor market data to this mix can provide a valuable external perspective.
But data-informed decisions are not without their pitfalls, especially when incorporating labor market information (LMI). There are many inherent tensions in making decisions with data, which can catch leaders by surprise if they’re not prepared.
By recognizing the signs of these common data pitfalls, you can reframe and position yourself or your team to make better data-informed decisions.
Pitfall #1: Relying on data to provide direction
What it sounds like: “Having data means I don’t have to make hard decisions”
So often, college and university leaders face big decisions. None of these are easy — involving the complexities of budget, personnel, policy, and student learning. Making decisions in this kind of environment is tough. And let’s not even think about the criticism and disappointment that decisions elicit (no matter how justifiable and thoughtfully produced).
In the face of all that, it’s understandable to look for the crystal ball that will reveal the right decision. “Big data” has acquired an almost mythic status in some circles. And certainly, putting a focus on descriptive, reality-based statistics can help decision makers get a clear view of what’s happening. But despite what data can add to your decision-making arsenal (which is considerable), data can’t actually make decisions for you. Labor market data is only one piece of the puzzle.
A good decision will also take into account your vision or mission, the needs of your community, other types of data (SIS data, for example), and specific constraints.
Solution: Data is a tool, not a destination — so keep decisions focused on your mission, goals, and values.
…
Pitfall #2: Data perfectionism
What it sounds like: “I have to use data perfectly or I can’t use it at all”
In our modern data-saturated environment, there’s a lot of pressure to “do data right.” Any person who’s even somewhat data-literate knows that there are things you can and cannot do when implementing data. Governance and security. Bias. Sample size. Not to mention displaying and discussing the data.
The pressure to have the perfect, centralized, rational, top-down framework of implementation right from the start is real — but it doesn’t need to be. The truth is, it takes time to build capacity. Even our dataset didn’t become what it is overnight: we started with the core LMI and added over time. And that’s okay.
The key is starting small, but impactful. Try a pilot with one forward-thinking administrative unit. Or start with one license and scale out as you prove the value of data to other stakeholders on campus via good results.
Solution: Start using data where you can, and work to build capacity.
…
Pitfall #3: Using data to validate (or enforce) preconceived notions
What it sounds like: “I already know what the problem is, and now I have the data to prove it”
When you’re ruminating over a complex problem, it can be easy to get tunnel vision. Based on our experiences, or the observations of others, sometimes it seems like we KNOW what the problems are — if only we could validate them with data so that everybody else could see, too.
While having a vision is important (as discussed in #1), it helps nobody when that vision obscures what data tells us about what’s actually happening “on the ground.” If you view data as a way to justify your own plans, without being humble enough to learn from it or to change your mind, you’ll turn what could be the greatest asset to your organization into yet another tool for polarization.
As one of our customers says, “Data is not a weapon. Data helps us ask better questions. And so if we’re delivering data you don’t like, what question can we ask about this?”
Solution: Approach data with a hypothesis, but be willing to learn.
…
Pitfall #4: Unquestioningly relying on data
What it sounds like: this pitfall often takes the form of silence, such as nondisclosure of details or lack of corroboration.
Trustable data can be so difficult to get that it’s easy to treat it like a precious, perfect commodity. Especially when you’re trying to hype yourself and others up to be willing to learn from data (#3) it can be easy to miss the limitations of any data set.
Data has its flaws and limitations, just like every other method of research. For labor market data specifically, there are some factors to consider. For example, because it’s collected from official government sources, LMI collection timelines can sometimes be challenging to match up with other sources. Or, the language that employers use to craft job postings often doesn’t overlap with the language that job seekers use in their online resumes, which makes a skill taxonomy harder to parse.
This doesn’t mean that the data is “bad,” but it does mean that you need to take some time to understand the limits of the data, and validate it against other sources of information.
One common way to do this is by cross-checking data with real-life conversations. Feedback from a workforce working group, student advisory board, or community forum can be a valuable source of insight.
Solution: Trust the data, but verify with other sources of information.
…
Pitfall #5: Assuming that higher education exists separately from the economy
What it sounds like: “Labor market data doesn’t apply to my students”
When we consider the institution of higher education as a producer of knowledge, it’s common to mentally situate the university in the “knowledge economy” rather than the dollars-and-cents economy. And in many ways that’s true: academic impact is measured more by peer-reviewed publications and citations than by revenue or ROI. Often, there are many on campus who think that labor market data doesn’t apply to you or your students.
But no matter how you slice it, a college or university is an integral part of the regional economy — universities are often hubs of hiring, and students go to school specifically to pick up the skills and knowledge they need to succeed in their careers.
Our economists measure college economic impact in terms of increased economic base in the state, which is attributed to higher student earnings and increased business output. Impact can also be seen in operational spending like an institution’s payroll and its purchases of supplies and services, or in the investments made by the public sector in higher education.
Solution: Adding LMI to your data mix helps situate your institution in the context of your regional economy.
…
Conclusion
A data-informed approach empowers good decisions. And alongside student data, labor market data is an equally powerful tool to have in any higher education decision maker’s arsenal. LMI can help contextualize a college or university in the larger economy, and provide a much-needed workforce perspective on educational pathways. But like any approach, a data-informed approach has its pitfalls.
Smart leaders know how to avoid those pitfalls, and bring the power of LMI to campus.
Next, check out our Guide to Market Research for Colleges and Universities.
The post 5 Data Pitfalls Higher Education Professionals Must Avoid appeared first on Emsi.
