As insights teams grow and mature, optimizing the flow of insights and data between researchers is increasingly essential.
As the volume and pace of research increase, insights “silos” can create enormous headaches for insights teams. There’s nothing worse than discovering that an insights project is redundant with work already done. Or that a researcher could have saved hours on a current project if only they’d known about a study their colleague had done six months prior.
Breaking down research silos and optimizing the flow of insights information between researchers and teams is imperative for high-performing insights teams.
How to break research silos
Optimizing the flow of information between insights teams and researchers requires the optimization of two related dynamics:
- Managerial (to promote and enable a culture of information-sharing)
- Technological (to store, organize, and synthesize research data across teams)
The managerial problem is ever-green and has been studied and addressed by management academics for decades. The bottom line is that it is imperative to build a culture of transparency where researchers have the resources they need to communicate (and interpret) information.
Optimizing the technological dynamic remains a significant challenge for many insights teams. In particular, adoption rates for insights repositories lag behind those for other research-tech (or ResTech) solutions. Additionally, technological innovation in insights-specific repositories has not kept pace with innovation in repository solutions for different use cases (for example, CRMs for sales teams or human capital management platforms for HR departments).
But which of these causes the other? Does insights repository tech lag because adoption rates are low? Or does insights repository adoption lag because insights technology fails to add value?
Anyone working in the insights industry knows: Poor technology is the cause of this problem.
Most attempts to solve the problem of functional, value-adding insights repositories still need to account for the nuances inherent in insights work. Further, the fast pace of innovation in the insights space has made it difficult for insights repository platforms to keep pace – platform rigidity, staticity, and (ironically) poor UI/UX present insights managers with frustrating challenges in persuading their team (especially on-the-ground researchers) of the value of investing their time and energy into updating and maintaining their insights repository.
If an insights repository creates more work for researchers on net, then they undo whatever is gained by adopting it.
The right kind of insights repository tool
Effective insights repositories should anchor around three key elements, as defined by Kristi Zuhlke:
- Insights, themes, and stories are tagged, indexed, and unified across teams and projects. Observations and nuggets of information that put tribal knowledge from siloed studies on display for all to search and see.
- Raw research data & evidence so that teams can review and relayer primary-source data with newer insights.
- Further, insights repositories’ backend technology should automatically graph together findings for insights researchers. They should not be designed to rely on researchers’ manual work to link relevant insights to discover new findings across projects.
An insights repository, in other words, is not just a wiki or “knowledge base.” While all text entries should be searchable, a good insights repository should not require users to post and publish large volumes of narrative findings for the platform to link common themes and ideas across projects, making it a knowledge management system.
This is why linking raw research data is essential to the continued improvement of an insights repository toward enhancing an insights team’s agility. Predictive technologies and artificial intelligence can reference this raw data to nudge researchers toward completely new findings – in addition to (and to enhance) whatever themes researchers are inputting on their own.
An effective insights repository will solve the problems of low repository adoption by insights teams by adding to – not stealing from – researchers’ time and energy. And they will break down insights silos by facilitating quick and automated connections between related data points across projects and programs by drawing upon both researchers’ inputs and raw data files.
The result: Insights teams capable of operating quickly and independently but with a fuller perspective of the kinds of insights work being done across all research initiatives at their organization. This equals higher-quality insights, minimal redundancy, and enhanced research agility.
Increasingly siloed insights teams are an unfortunate side-effect of a growing and maturing insights department that affords its various teams with heightened operational independence. But insights silos are not unavoidable. Organizations can break down barriers and facilitate value-adding communication between these fast-moving and interrelated projects by using an insights repository like InsightsHub built to account for the idiosyncracies inherent in complex insights workflows.