From Data Collector to Data Connector: Embracing Research Democratization

As AI capabilities expand and research teams stay lean, many researchers find themselves supporting hundreds, if not thousands, of colleagues in their organizations. For them, the model of centralized research is creating bottlenecks that slow decision-making and limit the reach of customer insights.

“The fundamental shift that people have to make is that you’re no longer a data collector. You’re a data connector,” says Ari Zelmanov, former police detective and current research leader. In Ari’s view, as teams get leaner and tools get better at executing research tasks, the job of the researcher becomes standing up repositories, socializing learning mechanisms, and creating the systems that empower organizations to act on good information.

We spoke with research leaders who've successfully made this transition, transforming their teams from siloed specialists into customer-centric learning cultures. Their approaches varied, but one theme was clear: when you empower others to answer their own questions, you don't diminish your value, you multiply it.

The d word holding us back

Before diving into solutions, there's an elephant we need to address: Democratization. Many researchers worry that democratizing research will lead to poor methodologies, incorrect conclusions, or devalued expertise. But Ari feels the argument is nye.

"The only people arguing about democratization are researchers," says Ari. "Nobody else is arguing about it. We're infighting about something that we have zero control over. It's happening."

I tend to feel like anyone arguing about democratization is missing one critical point: customer centricity isn't just one person's job.

Anton Krotov, Researcher in an organization of over 10,000 people, was in the fortunate position of being very trusted by his colleagues. So much so that they believed research could answer all of their questions.

“I had already established a reputation. I was fortunate that I didn't need to sell the value of research. Quite the opposite. People came to me with too many requests. They believed research could do everything for them. I needed to set up boundaries.”

Overwhelmed with requests from colleagues, Anton realized that the solution wasn't saying no—it was saying yes in a different way. Rather than becoming a bottleneck, Anton chose to become a bridge.

Connect teams through shared intelligence

Good intelligence is the responsibility of many disciplines, not just research. To get answers quickly, Ari's teams use what he calls the "Moneyball" approach to research, a framework that prioritizes speed and accessibility over methodological purity:

"Product teams are incentivized to move fast. So, how do you make research fit into that in a way that makes sense? We built something called Moneyball Research. It's super simple: start with what you know. It could be in your repository, it could be what you know. Then you go to what data is accessible within 24 to 48 hours. That's usually internal analytics, CSAT tickets, NPS, sales conversations, and tribal knowledge. Then—and only then—do you go to primary research."

This approach shifts conversations away from methods and focuses instead on what teams need to know and how confident they need to be. "Then it's up to the researcher to be the doctor. Diagnose that, determine how they're going to collect that evidence given the time, money, and level of rigor."

René Bastijans, lead researcher at a growth-stage startup, has found creative ways to loop colleagues into data collection. His sales team is trained to lightly survey prospects during sales calls and report back to the wider team.

"We've trained our sales team to ask for specific data and enter it into Salesforce. Researchers and the product team have access to these data, and therefore, sales has allowed us to keep a pretty good pulse on the market."

This creates a healthy feedback loop that keeps everyone abreast of evolving user needs while extending the research team's reach without expanding headcount.

Invite colleagues into the research process

While it might seem counterintuitive to share methodologies and research responsibilities, successful research leaders see democratization as an opportunity rather than a threat.

To remove research bottlenecks, Anton ran internal workshops to upskill his colleagues on doing their own research. This proactive approach to education focused on tailoring training to his colleagues' specific needs: "I try to cover the cases that will be really applicable, so I don't offer any cookie-cutter material and don't go much into theory. It's really tailored to their day-to-day work."

The key is meeting people where they are and giving them tools that fit their contexts. Not everyone needs to become a master researcher, but many can learn to conduct basic customer interviews or query data effectively.

Brittany Lang, UX Research Manager and M.S. in Research, uses project reviews as a time to cultivate a shared point of view and continually refine her thinking.

“Before we socialize research plans, I usually take a look at it, or I have someone else on my team take a look at it. It doesn't have to be your manager that's reviewing something, but can someone give you feedback?

It's nice when coworkers leave comments and I can see what other people on the team have said and we can agree or challenge, and then have a discussion about it. I also learn in those moments too. When I'm looking at how members of my team have reviewed other work, where they're coming from and their perspective, I learn a lot from them in those moments.”

Facilitate low-risk learning

It takes more than a few ambitious researchers to imbue a company’s culture with a learning mindset, which is why rituals and learning programs are so important.

Anton’s employer formalized this approach to building safe learning environments through a program called "Gigs for Growth," a repository of side projects from different departments where employees can apply to work on learning opportunities outside their typical scope.

"It's like a company green light that you can work on learning during your full-time gig and outside of your typical work scope. Something that you would never otherwise be able to touch in the company."

Under this program, researchers can support QA engineers, sales can support marketing, and everyone gets exposure to new perspectives that inform their primary roles. "You get some really new experiences that otherwise you wouldn't be able to."

At The Good, we like to build regular, low-stakes opportunities for knowledge sharing and skill development. One of our approaches at The Good is a ritual called "Random Question of the Week." During another bi-weekly meeting, team members share client questions that stumped them or that they felt they could have answered better.

These conversations help build shared perspectives that then get turned into artifacts:

• FAQ entries for brief, punchy answers
• Articles for long-form perspectives
• Policies or SOPs that outline ways of working

The result is that teams become more aligned, can answer tough questions on the spot, and save time by referring to their collective knowledge instead of rehashing the same discussions.

Another effective ritual is "Critique & Share" sessions, where team members bring questions, websites they admire, or work they're developing to get fresh perspectives from colleagues who haven't been deep in the weeds of a particular project.

Maggie Paveza, Senior Strategist at The Good, shares that it has helped her break the ice when building a shared P.O.V.

"It's pretty informal and often we're not showing our own work, so it feels less intimidating to ask your team members, 'why do you think this competitor is using this strategy,' than if it were your own work," explains Maggie.

The power of being a data connector

"The fundamental problem that research as an industry has is we've been myopically focused on the front end of the equation," says Ari. "Data collection, statistical significance, theoretical saturation—insert whatever fancy academic word you want in here. But the real power comes on the back end of the equation."

That back end is about connection, synthesis, and empowerment. When researchers shift from being data collectors to data connectors, they don't lose their expertise; they amplify it.

As Anton puts it, "Where soil is right, then you can do things. Praise people for when they do things great. You can learn from mistakes, you can learn from success."

The goal isn't to turn everyone into a researcher. It's to create an environment where customer insights flow freely, where good questions get asked by many disciplines, and where learning happens continuously rather than in bursts.

Making the shift

Building a customer-centric learning culture doesn't happen overnight, but it starts with understanding where your organization is open to change and being constructive about how you facilitate it.

Look for teams and individuals who are already curious about customers. Find the places where people are asking good questions but lack the tools or confidence to find answers. Then meet them there with the right combination of education, tools, and support.

"At the end of the day, it's about empowering decision-making," says Ari. And in a world where customer expectations evolve quickly and research teams are lean, that empowerment might be the most valuable thing researchers can provide.