Most organizations already have the data they need. The challenge is making it usable.
In his recent article for Research Live, John Bird explores a shift many insights teams will recognize. Data exists across the business, often in abundance, but accessing it, connecting it and applying it to new questions remains harder than it should be.
As he writes, “Most companies already have a large share of the data they need to answer important business questions. What they often lack is a practical way to reach it, explore it and apply it when those questions arise.”
Moving beyond the report
A key theme in the article is the limitation of traditional outputs. Reports still play an important role, but they reflect a moment in time and a specific question. As priorities shift, those static outputs often fall short.
The underlying data, however, continues to hold value. When it is organized and accessible, teams can revisit it with fresh questions, compare across studies and build on what already exists rather than starting from scratch.
Creating a more usable foundation
John points to a more practical model: bringing core research assets into a centralized, managed environment. Brand tracking, segmentation, customer experience and other strategic data sources can be structured consistently, making them easier to search, compare and apply across the business.
This also helps address a common operational challenge. Research is often spread across suppliers, formats and systems. Valuable knowledge exists, but using it can require time, specialist support and institutional memory. A connected environment creates order and makes that knowledge easier to use.
Why this matters more with AI
As AI becomes part of day-to-day decision-making, expectations are changing. Business users increasingly expect direct questions to return immediate, relevant answers. That expectation now extends to internal research.
This is where well-managed data becomes critical. AI performs best when it is working with trusted, structured and relevant inputs. When applied to curated internal data, it can help teams move more quickly through exploration and identify patterns across multiple studies.
At the same time, the role of the insights team remains essential. Validating outputs, checking logic and ensuring findings reflect real patterns continues to be a key part of maintaining trust in AI-supported analysis.
From stored knowledge to applied insight
The article ultimately points to a broader shift in how insights teams create value. The focus is not only on delivering interpretation, but on shaping the system behind it. Bringing data together, structuring it consistently and making it usable across the business becomes part of the role.
When that foundation is in place, organizations are better positioned to reuse what they already know, support faster decision-making and connect insight more directly to action.
Read John Bird’s full article on Research Live: https://www.research-live.com/article/opinion/the-future-of-insights-depends-on-data-access/id/5148603