Thirty carefully marked rows—thirty distinct hybrids of corn and soybeans—line the southern edge of our property. To a casual observer, it’s just another field. But for us, these rows represent something more: a live, data-rich testing ground where decisions take root in real-world conditions.
The Value of Real-World Testing
For over a decade, our farm has partnered with a local grain company to host these seed plots. They’re more than demonstrations—they’re controlled experiments. Each plot gets the same care and inputs. The only variable: the genetics.
The parallels to enterprise technology evaluation are striking. Just as no seed catalog can predict how a hybrid will perform in your specific soil, no vendor pitch or industry benchmark can guarantee how a technology will function in your unique operational environment.
When we evaluated updates to our Identity and Access Management (IAM) processes and systems at Michigan State, we applied the same principle. We created isolated testing environments, used real data, and involved real users. It wasn’t enough for a solution to succeed elsewhere—it had to prove itself here.
Hyperlocal Insights Matter
Our test plots are valuable precisely because they are local. A high-performing hybrid in another state may falter in our soil. A soybean bred for general disease resistance may still fail under the unique pathogen pressure of our microclimate.
Technology behaves the same way. Institutional structure, user expectations, legacy systems, and campus culture all shape how a new solution performs. What works seamlessly at one university might disrupt workflows at another.
That’s why pilots, sandboxing, and controlled rollouts are critical—not just to test functionality, but to surface unexpected friction points that only emerge in your actual context.
The Partnership Element
Our relationship with the grain company is not transactional—it’s collaborative. We bring land, equipment, and management. They bring genetics, technical support, and data analysis. The outcome is insight neither party could generate alone.
The most successful tech projects follow this same principle. We don’t just evaluate vendors—we look for partners. In our IAM modernization, we sought those who listened to our concerns, responded to our constraints, and showed commitment to evolving with us—not just selling to us.
Great partners, like great seed suppliers, engage in a mutual process of refinement, learning, and continuous improvement.
Measuring What Matters
At harvest, we look beyond yield. We measure disease resistance, standability, grain quality, and stress resilience. These multidimensional assessments give us the full picture.
Enterprise technology demands the same. It’s not about the most features—it’s about the right outcomes. We track user adoption, support needs, error rates, integration success, and long-term satisfaction. The system that “checks all the boxes” on paper might underperform in practice. True value is revealed in use, not in specs.
The Long View
One season isn’t enough. We track data year over year, across droughts and downpours, early frosts and late harvests. Only through long-term observation can we identify which varieties are resilient.
In technology, too, initial success doesn’t guarantee longevity. We assess not just the launch, but how solutions evolve with shifting demands, scale with institutional growth, and integrate with new tools. A platform that’s great today but obsolete tomorrow isn’t worth the investment.
From Seeds to Systems: The Common Thread
The method is timeless: test in your environment, measure what matters, collaborate deeply, and think long term.
When approaching technology change or modernization, it’s tempting to chase innovation for its own sake. But meaningful progress comes from balancing curiosity with discipline, aspiration with validation.
So next time you see a test plot on the side of the road, know you’re looking at more than farming. You’re witnessing a model for thoughtful adoption—whether in the field or the enterprise.