background
Google's research program operations had grown organically over the years, and the infrastructure hadn't kept pace. The team was managing 400+ research projects across 13 product areas—all tracked in a sprawling network of Google Sheets.
It worked when the program was small. But as it grew... we knew we needed a different approach.
the problem
The Sheets-based system was chaos:
- Cmd+F was our search engine — Finding a project meant scrolling through massive spreadsheets and hoping you spelled the name right
- Anyone could edit anything — No role-based permissions meant recruiting could accidentally overwrite coordinator data, and vice versa
- No audit trail — When data was wrong, we couldn't trace who changed what or when
- Reporting was a nightmare — Pulling reports for leadership meant hours of manual aggregation across multiple sheets
Leadership needed accurate, real-time visibility into program performance. The Sheets-based system couldn't deliver that.
discovery & analysis
Before building a migration plan, I needed to understand the full scope of what we were dealing with. I conducted:
- Stakeholder interviews with leadership, vendors, and program managers to understand requirements and pain points
- Workflow mapping to document how data currently flowed through the system (and where it broke down)
- Data audit to assess the quality, completeness, and structure of existing project data
Key insight: The problem wasn't just the tool—it was the lack of standardized processes around data entry. Any new system would fail without addressing the underlying workflow issues.
the solution
I led the migration from planning through implementation:
- Automated intake — New projects flowed directly into Salesforce from the intake form. No manual entry on the coordinator side.
- Structured search — Projects retrievable instantly by unique ID, host name, or student name. No more Cmd+F.
- Role-based access controls — Recruiting couldn't edit coordinator records. My team's data stayed protected.
- Audit trails — Every change tracked. When errors occurred, we could trace them back and correct at the source.
- Training & adoption — Developed materials and facilitated sessions to ensure the team actually used the new system correctly.
results
The migration was completed on schedule. We went from a manual, error-prone, anyone-can-edit-anything spreadsheet mess to a system with automated intake, instant retrieval, and proper access controls. Data accuracy improved, error rates dropped, and leadership finally had the visibility they needed.
lessons learned
Process before platform. The migration would have failed if we'd just lifted-and-shifted bad data into a new system. Cleaning up workflows and standardizing data entry practices was just as important as the technical migration.
Training is adoption. A system is only as good as its adoption rate. I invested heavily in training—not just "how to use Salesforce" but "why this matters for your work."
Monitor after launch. The project didn't end at go-live. Tracking adoption metrics in the weeks after launch helped us catch issues early and provide targeted support where needed.