Data Optimization: The Overlooked Layer in AI-Enabled Intelligence

AI, APIs and MCPs are changing how R&D and competitor intelligence teams access, connect and interrogate data. Scientific, clinical, commercial and partnership information can now be pulled together faster than ever, creating new opportunities to generate insights at speed.

But easier access does not automatically mean better intelligence. When data sources use different vocabularies, labels, fields, units and hierarchies, AI can amplify inconsistency rather than resolve it. The result may look sophisticated, but the underlying evidence collected by AI can still be incomplete, mismatched or difficult to defend.

That is why MCP integration is a significant first step, but not the final solution. To maximize the value of connected data, organisations need expert support to optimise the data layer before AI-enabled analysis is scaled.

In practice, this means doing the critical work before a prompt is ever written – aligning terminology, structuring evidence for your specific needs and applying expert judgement where there is a risk of ambiguity.

This work typically includes:

• Ontology mapping: aligning taxonomies, data models and coding structures so teams can compare and triangulate evidence across disparate sources, each with their own structure and nomenclature.
• Data restructuring: consolidating and reorganising datasets so they answer the strategic, commercial and R&D questions that are specific to your organizations needs.
• Data harmonisation: standardising formats, definitions and units to enable better like-for-like comparisons.
• Data architecture optimisation: improving how data is structured and connected so AI-generated insights are more relevant, reliable and decision-ready.

Ontology mapping: In one data integration project, Beacon data was incorporated into a client’s internal library alongside public and enterprise sources. The work revealed inconsistencies in ontology structures and naming conventions that would have reduced matching accuracy and made cross-source analysis less reliable. Robust cross-mapping improved compatibility by Beacon Consulting, strengthened matching accuracy and enabled Beacon and third-party data to be integrated more smoothly.

Data restructuring: In an asset benchmarking exercise, clinical trial evidence needed to be comparable across endpoints, patient groups, response categories and reporting formats. By standardising the data, structuring protocol detail and identifying relevant patient subgroups, Beacon Consulting created a cleaner evidence base for like-for-like benchmarking and more dependable AI-enabled analysis.

Data harmonisation: Clinical evidence for a subset of ADCs in development was fragmented across populations, indications, linker-payload combinations and partnership activity. Mapping and harmonising the key data points into a structured dataset by Beacon Consulting allowed AI tools to interrogate the evidence more accurately and benchmark assets of interest with greater precision.

What this means for intelligence leaders

AI is extremely powerful, but it does not remove the need for expert data optimisation. Organisations that invest in better data and better ways to interpret will ultimately be better placed to generate insights that are actionable, defensible and differentiated.

Three priorities for trusted AI-enabled intelligence

• Start with well-validated data. Insights are only as good as the data used to support them, ensure you have access to validated, trustworthy data before analysing
• Define your business question: Be clear on the decision the data needs to support, then structure and optimise the evidence around that question rather than expecting AI to find the answer.
• Build a shared language. Align terminology across internal and external sources so teams can compare assets, studies, populations and signals without manual reconciliation.
• Keep experts close to the data. Subject matter expertise is essential when mapping complex biology, clinical nuance and competitive context into a structure that AI can use with confidence and avoid becoming a ‘black box’ of insight