Project Overview
This research ops initiative focused on making evidence easier to find, reuse, and apply across product work. After reviewing how the team was currently conducting research, organizing data, and sharing findings, I designed a Dovetail rollout centered on taxonomy, shared synthesis workflows, and team enablement so research could scale without losing rigor.
Of the team’s work was qualitative, which made consistent synthesis and coding workflows especially important.
Of researchers reported not using any coding system to organize and analyze qualitative data.
Built into the Dovetail taxonomy so findings could be organized in a way the team could actually reuse.
Research was already happening across the organization, but the practice was fragmented. Teams were relying on a mix of Google Meet, Sheets, Slides, and FigJam, and coding practices varied widely from person to person. That made synthesis harder to scale, past work harder to discover, and evidence harder to carry forward into product decisions.
I approached the rollout like a service and systems problem. Before setting up the tool, I needed to understand how the team currently worked, where analysis friction was showing up, and what would make a shared repository genuinely useful.
The Process
The work moved from understanding the current state, to aligning on structure, to building the workflows that would make Dovetail valuable beyond a one-time setup.
The taxonomy was informed by collaborative workshop activities rather than assumptions. These sessions helped translate individual habits into a structure the broader team could align around.
The biggest finding was that the problem was not a lack of research. It was a lack of shared structure. The rollout needed to support how evidence moved through the team, not just where it was stored.
The team was already doing thoughtful work, especially qualitative and evaluative research, but artifacts lived across too many tools and too many personal systems.
With no shared taxonomy or predefined codes, each project risked becoming a fresh synthesis exercise rather than building on prior learning.
Categories like user goals, pain points, feature requests, support experiences, and journey stages made the repository useful because they matched how teams asked for evidence.
A clean tool setup was not enough. The rollout needed workshops, shared expectations, and guidance for how to keep quality high while expanding access.
Business Impact
Because this was a tools and operations initiative, the value was operational as much as methodological. The rollout created a foundation for faster synthesis, stronger collaboration, and more reusable research knowledge across the organization.
Analysis and process time through more structured coding and less reinvention from project to project.
Insight access through a repository teams could search, learn from, and bring into decision-making earlier.
Research advocacy through workshops, clearer best practices, and stronger visibility into how the team worked.
Foundational and evaluative research quality because the team had better structure for organizing and comparing evidence.
This work reinforced that research ops is not really about tools. It is about making insight travel. The hardest part was not configuring Dovetail. It was designing a structure that respected how researchers already worked while still nudging the practice toward more consistency.
It also reminded me that adoption lives or dies on enablement. A repository becomes valuable when people understand how to use it, when categories feel intuitive, and when the system makes their day-to-day work easier instead of more rigid.
If I were extending this case study further, I would add a stronger longitudinal view of adoption over time, especially around what the team continued using, what needed reinforcement, and where repository governance should evolve next.