Unlocking Global Product Teams Success

Product teams are increasingly global. In a full-remote, digitally interconnected world, geographical distance, cultural differences, and overlapping time zones make research coordination, communication, and data management genuinely complex. ResearchOps is the discipline that answers that complexity, equipping teams with robust, scalable research infrastructure, tools, and processes.

At Dell Technologies, that complexity had a number. The global digital design organization spanned 320 UX professionals. Dedicated research capacity was thin against that demand: a 21:1 ratio of People Who Do Research (PWDR) to UX Researchers (UXR), measured across 257 product designers. The result was research that was scattered, duplicated and reactive teams putting out fires and running tactical studies in isolation rather than building on each other's work.

I framed the situation as a maturity problem with a clear destination: From Chaos → To Silos → Cross Integration, where chaos was tactical firefighting, silos was the current state of teams working alone, and cross integration was the win-win target where insight flowed across teams and the wider org.

Underneath that goal sat eight concrete challenges a repository would have to solve: data security and access control (GDPR, LGPD), data quality and standardization, transparency and cost-effectiveness, scalability, findability, a sound recruitment process, training and documentation, and data ownership and governance.

Rather than start by building a complex tool, I ran leadership and research workshops to surface those eight challenges and rank them with the people who would own and use the outcome. The strategic thesis was to merge two disciplines that are usually run apart: ResearchOps (standardized methods, governance, and operations) and Knowledge Management (indexing, findability, and reuse). Securing leadership alignment was treated as the real precondition, more decisive to success than the sophistication of the eventual platform.

The extensive workshops converged on three priorities, which became the selected requirements for the build:

1. Findability → "Discover Data." — Make personas, journey maps, and discovery research locatable across segments instead of buried in team folders.
2. Transparency & cost-effectiveness → "Reduce Redundancy." — Cut duplicated effort on the same objective by surfacing secondary research and prior references before new work starts.
3. Data ownership & governance → "Data Governance." — Establish a transparent system of rules for managing the repository — responsible data handling with security standards in place.

The Research Library used a knowledge-management platform as its asset backbone, chosen so a working proof of concept could be stood up quickly:

1. Search — A Google-like search engine with automatic deep-indexing of entire documents, so the full content of a study — not just its title — surfaced in results.
2. Structure — Filters and sorts across four dimensions: Demographics, Business, Method, and Output.
3. Content — Continuous research on user needs, ResearchOps operational content for product teams, and a growing library of articles, reports, slides, and templates.

A representative quarter of usage showed the repository was being adopted, not just shipped:

1. 368 repository members (+10%) — across Design, Product, Marketing, and Development.
2. 264 research studies viewed per month — reports, articles, slides, and templates.
3. Engagement held up over time — a 49% engaged-user rate, 38% weekly stickiness (WAU/MAU), 25 active days per month, and 83 minutes spent per day.
4. 91pp customer satisfaction — on the internal customer satisfaction measure.

Against the three priorities, the shift from silos to cross-integration was concrete: teams could now discover personas, journey maps, and discovery research across segments (Discover Data); all lines of business were consolidated in one searchable location, reducing duplicate effort (Reduce Redundancy); and research lived in a single platform with security standards in place (Data Governance).

The clearest takeaways were almost obvious in hindsight:

1. Use a knowledge-management platform as an asset layer — its indexing and search make it fast to build a credible proof of concept.
2. Treat the research repository like a product or service — apply discovery and framing up front, then track adoption metrics to understand its value to teams.
3. Building relationships with leadership — matters more than engineering a complex solution.

The less obvious contribution was a knowledge-type taxonomy that mapped research artifacts across four lanes — Operational → Data → Tactical → Strategic,  so the library could be queried in natural language rather than browsed by folder. Goals (contextual knowledge), target audience, and methods (specific knowledge) feed into findings (explicit) and insights (interpretive), which inform taxonomy, recommendations (tacit), and action plans (procedural), and ultimately roll up to the repository itself and organizational impact (org. knowledge).

“Provide me all the research-study recommendations about Sales Tools from the last quarter.”