Meta analyzing across existing research to inform strategic product uncertainties — and get more done with insights

Integrating research > Expanding reach and connectivity > C. Normalizing research reuse to lay the groundwork for new operations > Article C4

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Reducing risk toward big bets with ‘old’ insights. Strategic unknowns where leaders need a consolidated, evidence-based point of view on important problems to solve. Even after researchers turn their own streams of studies into ongoing meta-analyses, expansive horizontal topics will emerge where no one source has the whole story.

Nothing convinces product leaders of the value of existing research quite like a clearly articulated, summative ‘download’ that demystifies the uncertainties they are losing sleep over. These targeted sets of synthesized insights can also evolve into foundational content for new repository initiatives.

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Great researchers in tech work to identify their organization’s strategic ambiguities as topics and questions for their studies. These ambiguities may be delivered fully formed, as rapid turn around requests from product leaders. Or they may emerge from researchers’ own questioning of product road maps — whether planned features, as envisioned, are targeting real customer problems. In either case, timing is often of the essence.

Tenured researchers often hear big topics and questions as areas where existing insights are already available, if scattered across a range of sources. This scattering can occur even after individual reports have already been transformed into focused meta analyses (C2).

Existing insights for strategic ambiguities may be frequently discussed, well-tread learning that’s ready to be placed in a new frame. Or, they have never truly saw the light of day in leaders’ attentions. Regardless, the fastest way to respond with research point-of-view — before leaders’ early notions crystalize into detailed feature plans — is a meta-analysis of internally available research.

At some point in my career, I essentially stopped collecting new data and became known for conducting huge meta analyses to get more done with existing insights. Leading the process of digesting hundreds of research studies, extracting and collating their underlying insights. It’s satisfying to pick up wasted learning and put it to use in planning work.

As an organization’s research knowledge becomes more mapped, understood, and accessible (C1), delivering a meta analysis on previously-researched topics can become the work of a few hours. But even a rapid meta analysis can feel like valuably changing speeds, downshifting toward a mindset of reflection. Finding new ‘homes’ for timeless insights, crucial problems to solve, and key customer learnings by arranging them in new structures that are sensible in the present tense.

Even as documented learning grows over time, product leaders often lean on recent insights to inform their big bets. Researchers and research ops staff can build new value by shouldering the interpretive labor that’s needed to turn past studies into new outputs. Synthesizing a bigger picture from disparate inputs in order to shape road maps toward what people really need.

Treating meta analyses as distinct projects is not a revolutionary idea, but these activities can evolve product peoples’ attitudes about using ‘old’ research in important ways. Their attitudes can shift from seeing researchers’ role as ‘data providers’ to something more akin to ‘customer needs experts.’ Additionally, seeing an abundance of still-applicable research can drive home the importance of managing existing insights at the portfolio level. And since any research repository plan is nothing without its founding content, a set of leadership-reviewed meta analyses can serve as a strong foundation for research ops experimentation around insight storage, marketing, and re-activation.

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Improving your insight operations

Get more done with your research community’s insights by:

• Identifying and scoping meta analysis topics that could demystify core product uncertainties
  Invest in meta analysis projects that are high risk, important for understanding peoples’ needs, critical for a product’s evolution, and where there is likely to be a sufficient body of existing research in your organization. Bound the project with a clear set of agreed upon research questions. Ensure that there’s room to surface adjacent, unaddressed problems to solve that keep cropping up across studies.
• Building a coalition and collecting research to draw upon
  Staff the meta analysis with a researcher who can own the project and put out the call, building a coalition of insight contributors and stakeholder collaborators. Use a ‘snapshot inventory’ (C1), if available, to narrow in on useful areas of existing research to revisit. Set a project timeline that meets leaders’ decision windows but allows for discussion and iteration of the meta analysis ahead of delivery.
• Theming insights in collected research and building a shared point of view on structure
  Collect research into a common tool that allows for tagging, search, and reorganization. Code insights to find themes, building tags around research questions and emergent threads, to be pruned down later. Tag potential headlines and key observations that jump out in the context of current questions. Collaborate with your coalition to define a structure that meaningfully organizes central insights and articulates specific, critical problems to solve. Ensure that source authors are credited appropriately, and that referenced reports are deep linked. Gather rounds of feedback and iteratively revise.
• Sharing the resulting meta analysis with leaders and through a range of other channels
  Connect with targeted product leaders as directly as possible around the meta analysis, ideally finding in-person time to review and discuss. Market and activate the collective knowledge by finding other avenues to share the revitalized insights (B2). Consider applying the same activation approaches used in any typical research project, such as ‘daily learning’ messages, ideation sessions around walls of insights, etc. Update future research planning to dive further into topics where more evidence would be beneficial, continuing to market the completed meta analysis as part of subsequent studies.
• Proposing meta analyses as founding content for research repository programs
  Use meta analyses as opportunities to build leadership interest toward investing in new forms of ongoing knowledge management. Harvest thematic codes as potential metadata for organizing research over time, with an emphasis on pivots that could route the right insights to the right product people.
• Your idea here…

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On the path from insight to product impact

Meta analyzing across existing research to inform strategic product uncertainties is a part of having sufficient evidence to steer major product decisions with existing research. It’s also part of usefully articulating insights in a way that resonates for big planning uncertainties, as well as building leadership awareness of possible planning targets within customer needs they are pursuing.

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Let’s connect

If you’ve read this far, please don’t be a stranger. I’m curious to hear about your challenges and successes demystifying core product uncertainties with meta analyses in your organization. Thank you!

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Related posts

• C2. Strengthening insights by evolving each research output into a meta analysis of compiled learning — to get more done with insights
• C1. Understanding the product learning landscape with a ‘snapshot inventory’ of all types of available research — to get more done with insights
• B1. Growing research ‘impact radius’ by connecting learning to more internal product audiences — to get more done with insights
• B2. Improving internal marketing about upcoming and new research — to get more done with insights
• View list of all ‘Integrating Research’ posts (and upcoming topics)

Selected references

• “If you’re wondering how to know what to research, there are two easy ways to figure this out. The direct way is to simply ask a lot of people what the biggest concerns/questions are and to pick the one that is the most common or the scariest to you. Go as far up the chain as you can with your asks: if you can get to the CEO, great!” Brad Orego
  https://medium.com/auth0-design/when-to-and-not-to-build-an-ops-first-research-practice-9cfb54ca4d49
• “You may also hear the term thematic analysis… As with grounded theory, you use codes to identify and summarise important concepts within data. Codes are foundational for themes — things that repeat — and themes are what you are going to put in your research report.” JJ Knowles
  https://medium.com/@jjknowles/introduction-to-ux-research-analysis-techniques-32192cfe0139
• “Put those anonymized insights together and bring that to the higher ups to say, ‘Here’s some feedback internally that’s impacting the way our whole organization is operating.’ You’re making it not about you — you’re making it about other people and your coalition” Hana Nagel, Sofia Quintero
  https://medium.com/enjoyhq/researchops-spotlight-hana-nagel-lead-ux-researcher-at-sap-534a8e3d89c1