Objective clear, challenge accepted. A two-year research project lay ahead of us, with data collection taking two distinct approaches, all aiming to draw comparisons between event occurrences.
Challenge 1: Tailoring Two Divergent Interfaces into One Deployment
The seemingly straightforward path most solutions offered was to run two studies on a SaaS platform, incurring costs for each, even if slightly discounted.
Our initial arithmetic was basic but ballooned quickly: number of subjects multiplied by time, and then doubled, providing a ballpark figure, a rough initial estimate for the scale and budget of our project.
Spotlight:
Quick and dirty estimation models tend to inflate numbers but provide a crucial starting point for budgeting, though scalability often takes a hit.
Diving deeper, we filtered through the study design, zeroing in on the indispensable data collection needs. That meant trimming down our forms to the bare essentials and reevaluating the real throughput required for data collection. The fresh figures were far more amicable, allowing us to comfortably run two separate databases within the same budget tier.
(Throughput, in a nutshell, relates to how many individuals utilize the system simultaneously to input data. In our case, not many – a revelation that allowed us to reduce needed resources significantly.)
Challenge 2: Navigating the Seas of Data Reconciliation
Operating two distinct databases didn’t necessarily mean double trouble. By utilizing an API connection, we turned potentially days of data reconciliation between two systems into a concise few hours of development, unveiling a wave of savings on hourly costs.
Conclusion: The Hidden Gems in Custom Solutions
Navigating through custom solutions, especially in smaller projects, can initially be daunting. Yet, our recent use case showcased not only how personalization in deployment met our specific needs but also safeguarded data precision and reliability, all while keeping the budget in check, laying down a path towards impactful and innovative research findings.
