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.

Across the business, teams hold years of research, yet still commission new analysis or search through systems built for storage rather than exploration.

Businesses want faster answers, broader access and greater flexibility in how information is used. As AI accelerates those expectations, the pressure to improve access to research data will only increase.

A growing number of organisations now have an opportunity to rethink how research assets are managed by creating centralised, secure environments where data from multiple studies and suppliers can be organised, connected and used to address new business questions.

Reports have limits

For a long time, the report has been the main output of market research. Findings are summarised and a narrative is developed for a particular audience. Reports still play an important role, especially when a business needs a clear interpretation of what matters most, but they are only one layer of value.

They reflect the question being asked at the time. As priorities shift, new questions emerge that those reports were never designed to answer.

The data behind the report often remains useful far longer than the presentation itself. When people can access that data in a structured environment, they can apply fresh filters, compare findings across sources and pursue questions beyond the original debrief. Traditional document libraries can store outputs, but they do little to support discovery.

A centralised hub can unlock more value

A more effective model is to create a managed hub where core research assets are brought together in one place. Brand studies, segmentation, attitude and usage work, customer experience programmes and other strategic data sources can be organised consistently so they are easier to search, compare and investigate, creating value across teams.

It also helps solve a persistent operational challenge. In many organisations, research is spread across suppliers with different structures, delivery formats and naming conventions. Valuable information exists, but using it often requires time, specialist support and institutional memory. A centralised system creates order across that complexity and strengthens the foundation for reusing what the business already knows.

Security and governance are essential. Data should be permissioned, managed carefully and made available with clear controls, giving the right people the ability to investigate the right questions in a reliable environment.

AI will make access more important

AI is reshaping how people engage with information, with business users expecting direct questions to yield rapid answers. That expectation is now extending to internal research teams, who are under growing pressure to support self-service access. Waiting to locate and rework past research no longer fits how decisions are made.

A well-managed data hub becomes especially valuable here. AI works best with trusted, relevant and organised inputs. Applied to a curated internal dataset, it can help teams move more quickly through exploration and surface patterns across multiple studies.

For example, a tourism organisation could ask which countries matter most based on visitor volume, spend and length of stay. A technology brand could explore which audience groups show the strongest potential for a new device. A category team could examine a specific subgroup to understand how interest, attitudes and intent vary. These questions are more useful when grounded in validated internal data and explored in a secure environment.

They also reinforce an important role for insights teams. As AI becomes part of everyday decision-making, someone still needs to verify that answers are supported by the data, the logic is sound and the output reflects a real pattern rather than a confident-sounding hallucination. This remains an important part of maintaining trust in AI-assisted analysis.

The role of insights is evolving

This shift has important implications for research and insights teams. Their value increasingly lies in shaping the system behind the answers, bringing data together across suppliers, structuring it consistently and ensuring it is used responsibly.

Interpretation remains critical, but it is no longer the only output. Validated findings can be captured and reused, building a strong knowledge base.

It should also make return on investment easier to track. When findings, decisions and outcomes are captured over time, organisations have a clearer view of what was learnt and where research influenced action. A centralised and connected environment creates the foundation for asking a more practical question: “what stories have we built and what did we learn from them?”

Where to begin

For organisations considering this approach, the first step is to assess the research assets they already have. From there, it makes sense to prioritise a few high-value sources such as brand tracking, segmentation, and attitude and usage studies, then bring them into a common environment with clear metadata, permissions and governance. Once that foundation is in place, the business is in a much stronger position to support ad hoc questions, AI-supported exploration and wider use of trusted research.

Companies have invested heavily in building research archives. The next stage is to make those assets genuinely usable. Better access to data will help organisations get more value from the work they have already done and put insights to work more effectively across the business.

John Bird is executive vice-president at Infotools