#354 Beyond BI: Decision Intelligence with Graphs with Jamie Hutton, CTO at Quantexa

25 snips

Apr 6, 2026

Jamie Hutton, Co-founder and CTO at Quantexa, pioneers entity resolution and decision intelligence for risk and compliance. He discusses using graphs to link messy, siloed data and enrich records with third-party sources. Short takes cover graph analytics for fraud, grounding LLMs with contextual graphs, and operationalizing investigations and human-in-loop workflows.

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ADVICE

Remediate Data Issues With Central Or Federated Workflows

Build processes for remediation: report data quality issues and either centralize fixes or push them to customer‑facing teams.
Jamie notes some clients use data stewards; others let relationship managers fix records.

INSIGHT

Entity Resolution Can Reveal Massive Customer Overcounts

Entity resolution can reveal huge discrepancies in customer counts and unlock benefits across business lines.
Jamie cites a client who thought they had 120M customers but only had ~70M real entities after resolution.

INSIGHT

Resolve Entities First Then Filter Graph Links

You need entity resolution before building a meaningful graph; otherwise duplicate nodes distort connections.
Jamie explains graphs must filter strong links (not coincidental ones like common vendors) to find meaningful patterns.

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Decision intelligence is showing up across data and AI teams as companies move beyond dashboards to decisions made with context. Graphs, entity resolution, and better data products are becoming core tools as messy, siloed data meets stricter risk and compliance needs. In day-to-day work, this means linking “James,” “Jim,” and “Jamie” across systems, enriching records with third‑party sources, and pushing models where the data already lives in your lakehouse. How do you trust your customer counts? Which links in a graph matter, and which are noise? Can graph-based context reduce LLM hallucinations enough for regulated decisions with humans still in-loop.

Jamie Hutton is the Co-founder and Chief Technology Officer of Quantexa, where he leads the company’s global research and development organization in advancing its market-leading Decision Intelligence Platform. With over two decades of experience pioneering data-driven technologies, Jamie has been at the forefront of innovations that connect and unify data at scale to solve complex real-world challenges. He is the creator of dynamic Entity Resolution, a pioneering capability that has redefined how the world’s leading organizations transform raw data into trusted, decision-ready intelligence. This innovation enables enterprises to prepare their data for AI, uncover new revenue streams, and expose hidden connections in even the most sophisticated criminal networks. By providing the foundation for accurate, explainable, and actionable insights, Jamie’s work has empowered governments, financial institutions, and global enterprises to make faster, smarter, and more confident decisions.

Prior to co-founding Quantexa, Jamie held senior technology and analytics leadership roles at SAS and Detica, where he delivered mission-critical solutions for organizations operating in some of the most complex and high-stakes environments in the world. Jamie holds a First-Class master’s degree in computer engineering and is recognized as a leading authority in contextual analytics, data integration, and applied AI for mission-critical decision-making.

In the episode, Richie and Jamie explore decision intelligence beyond BI, entity resolution across siloed data, building context graphs for fraud, AML, credit risk, and growth, how graph analytics separates meaningful links from noise, graph-RAG for LLMs to cut hallucinations, human-in-the-loop workflows, and ways to start today, and much more.

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