Unlocking Global Product Teams Success

Product teams are increasingly global. In a fully-remote, digitally interconnected world geographical distance, cultural differences and various time zones make research coordination, communication and data management genuinely complex. ResearchOps solves that complexity by equipping teams with robust, scalable research infrastructure, tools, and processes.

““We needed a unified, governed place for insights so that any team anywhere could build on what we already knew.””

At Dell Technologies, such complexity had a number. The global digital design organization spanned 320 UX professionals. Dedicated research capacity was low compared to its demand, as there was a 21:1 ratio of People Who Do Research (PWDR) to UX Researchers (UXR), measured across 257 product designers. Consequently research was scattered, duplicated and reactive teams had to put out fires and run tactical studies in isolation rather than building on each other's work. I framed the situation as a maturity problem with a clear goal: From Chaos → To Silos → Cross Integration, where chaos was tactical firefighting, silos was the current state of teams working independently, and cross integration was the win-win target where insights flowed among teams and the wider org. Underneath that goal sat eight concrete challenges a repository would have to address: 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 identify and rank those eight challenges with the people who would be responsible for and use the outcome. The strategic decision was to merge two sectors that are usually run  separately: ResearchOps (standardized methods, governance and operations) and Knowledge Management (indexing, findability and reuse). Securing leadership agreement was treated as the real precondition, considered more decisive to success than the sophistication of the eventual platform.

The extensive workshops led to three priorities, which became the selected requirements for the project:

1. Findability → Discover Data — M ake 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 being able to access 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 that a working proof of concept could be up and running quickly:

1. Search — A Google-like search engine with automatic deep-indexing of entire documents, so that the full content of a study (not just its title) could be found 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.

Considering 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 into one searchable location, reducing duplication (Reduce Redundancy), and research was kept on a single platform with security standards in place (Data Governance).

The clear 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 strategy with a large impact was the knowledge-type taxonomy that mapped research artifacts across four lanes — Operational → Data → Tactical → Strategic, allowing the library to 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), which in turn ultimately roll up into the repository itself and organizational impact (org. knowledge).

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