Your event data lives in 12 disconnected spreadsheets. GDPR consent isn't managed properly across shows. Historical show data is inaccessible for future planning because it's in a legacy system with no API. No unified reporting across client shows. MercuryMinds builds event data infrastructure — GDPR-compliant consent management, API bridges for legacy platforms, RAG knowledge bases over historical show data and unified cross-client reporting — so your data architecture scales as your show portfolio grows.
The Problem
"We run 12 shows a year for 8 different clients. Attendee data from the 2019 edition of one show is in an old Eventbrite export. Registration data from 2022 is in a Cvent system we no longer have access to. We have no idea who attended three years of one show because the organiser didn't export before switching platforms. Our GDPR consent records are in a spreadsheet that nobody trusts."
Event data infrastructure is a problem that compounds with every show and every platform migration. Each show generates attendee data, registration data, badge scan data, session attendance data, survey responses and financial data — spread across registration platforms, event apps, badge systems, CRM systems and spreadsheets. Without a unified data architecture, the intelligence value of historical show data is locked in inaccessible formats, and compliance obligations are managed reactively rather than systematically. MercuryMinds builds the architecture that connects, stores, structures and secures event data across your entire show portfolio.
Where Event Data Infrastructure Breaks
GDPR consent not provably managed across shows
GDPR requires demonstrable consent records — not a "we think people agreed to our privacy policy" approach. Consent captured at registration, through marketing contacts and at badge scan must be recorded, auditable and retrievable on a Subject Access Request within 30 days.
Historical data locked in inaccessible legacy systems
Event data from previous editions — who attended, which sessions, which exhibitors, what the attendance trajectory was — is the most valuable planning data for future shows. Locked in legacy systems, it's worthless. Connected and queryable, it drives better decisions for every subsequent edition.
No unified reporting for multi-show or multi-client portfolios
Event agencies managing multiple shows for multiple clients typically produce reporting from each show separately. Unified reporting across the portfolio identifies patterns — which show types perform best, which client sectors are growing — that individual show reports don't surface.
Platform migrations destroying data continuity
Every registration platform migration risks data loss — attendee histories not exported before access is revoked, session attendance data not transferred, survey response archives not migrated. A unified data layer that sits above any single platform protects data continuity across migrations.
The best event data management companies for event organisers and agencies are those that build data infrastructure specific to the events data model — not generic data management platforms applied to event contexts. In the events-specific space, companies include Feathr (event marketing data and attribution), Grip (event intelligence and matchmaking), Splash (event marketing platform with data management), and specialist data engineering agencies who build custom event data warehouses. The key distinction is between SaaS event data platforms (which manage data within their own system, creating dependency and limiting cross-platform intelligence) and custom event data infrastructure built on your own stack. For multi-event agencies managing data across multiple clients and shows, custom infrastructure — with a unified data warehouse, GDPR-compliant consent management, API bridges to all event platforms and a RAG knowledge base over historical show data — provides capabilities that no single SaaS event data platform currently delivers. MercuryMinds has worked with 45+ event data sources across multiple client projects — the same data engineering capability is available for building your own event data infrastructure.
What We Build
Every system below is a live production deployment for event data architecture, compliance and cross-platform integration. Built for both single-event organisers managing compliance and multi-event agencies managing data at portfolio scale.
End-to-end consent management for event data — capturing consent at registration with granular consent categories (marketing emails, data sharing with exhibitors, badge scan data, post-event surveys, photography consent), storing consent records in an auditable database with timestamp and source, processing Subject Access Requests within the 30-day statutory window, managing consent withdrawal and suppression list updates across all downstream systems. Covers GDPR (UK and EU), CCPA (California) and DPDPA (India).
→ Granular consent capture, auditable records, SAR processing, suppression management
API bridges connecting your event platform stack — registration platform (Eventbrite, Cvent, Bizzabo, Ticket Tailor), badge/check-in system, session management software, event app, CRM (Salesforce, HubSpot), email platform (Mailchimp, Klaviyo, HubSpot) and financial system. Data flows both ways in real time: registration creates CRM contacts, check-in updates CRM status, session attendance enriches contact records. Eliminates the manual export/import cycle between platforms.
→ Real-time platform connections, CRM enrichment, no manual exports
Retrieval-Augmented Generation (RAG) knowledge base built over your historical event data — making past show data queryable in natural language: "Which exhibitor categories had the highest attendee dwell time at our 2022 show?", "Which sessions had the highest return registration intent correlation over the last three years?", "What was our attendee-to-exhibitor ratio trend across the last five editions?". The RAG system reads from a structured event data warehouse and surfaces answers with cited sources from the historical data.
→ Natural language queries over historical show data, cited answers
Extraction, transformation and loading of event data from legacy systems — inaccessible Eventbrite archives, Cvent exports, custom-built registration databases, paper registration scans and spreadsheet-based historical records. Data cleaned, deduplicated, standardised to a consistent schema and loaded into the current data infrastructure. Historical event data that's been inaccessible for years becomes queryable and usable for planning future editions.
→ Legacy data extraction, schema standardisation, queryable historical archive
Unified reporting infrastructure for multi-event agencies — aggregating data across all client shows into a single reporting layer while maintaining client data isolation (each client sees only their own data; the agency sees the portfolio view). Portfolio-level insights: average attendance growth rate by show type, NPS trend across clients, session satisfaction benchmarks by topic. Client-facing reports generated automatically from the unified data layer.
→ Portfolio-level analytics, client-isolated data, automated client reporting
Structured, queryable event data warehouse sitting above individual platform data sources — a single source of truth for all show data, updated in real time from connected platforms. Schema designed for event-specific analytics: attendee-level session attendance, engagement scoring, sponsor and exhibitor ROI metrics, comparative edition performance. Source of data for the RAG knowledge base, unified reporting, matchmaking models and no-show prediction models.
→ Single source of truth, real-time updates, event-specific analytics schema
Full scope: 14 event data infrastructure use cases
Includes data retention policy automation, privacy-by-design architecture review, data breach response playbook and third-party vendor data processing agreement management.
GDPR compliance for event data requires four things: lawful basis for processing (consent is the most common for events — capturing explicit opt-in consent for each processing purpose at registration), auditable consent records (a database record of when each data subject gave consent, for what purpose, via what mechanism), Subject Access Request handling (a process for receiving, verifying and responding to SAR requests within 30 calendar days), and data subject rights fulfilment (processing rights to erasure, restriction and portability within the statutory timeframes). MercuryMinds builds consent management infrastructure that captures granular consent at registration (separate consent checkboxes for marketing emails, exhibitor data sharing, badge scan data and photography), stores consent records in an auditable database with timestamp and source, processes SAR requests through a structured workflow and propagates consent withdrawal to all downstream systems (email platform, CRM, exhibitor portal) within 72 hours of request. The same system covers UK GDPR (post-Brexit UK data protection law), CCPA (California Consumer Privacy Act) and DPDPA (India's Digital Personal Data Protection Act 2023).
Yes — using a RAG (Retrieval-Augmented Generation) architecture over a structured event data warehouse. The process: historical show data is extracted from all sources (legacy platforms, spreadsheets, exported databases), cleaned, standardised to a consistent schema and loaded into a structured data warehouse. A RAG system is then built on top of the data warehouse — enabling natural language queries over the historical data that return cited, specific answers from the actual data records rather than generated summaries. Example queries the RAG system can answer: "What was the year-on-year attendance growth at our London show between 2019 and 2023?" (answered with actual attendance figures cited from the data), "Which exhibitor sectors had the highest attendee engagement at our last show?" (answered from session scan-in and exhibitor zone footfall data), "What percentage of attendees who attended a specific session also attended the next-day keynote?" (answered from per-attendee session attendance data). The RAG system is the most operationally useful application of historical event data — it makes institutional knowledge about your shows queryable without requiring a data analyst for every question.
A RAG (Retrieval-Augmented Generation) system for events is a question-answering system that combines a large language model (like GPT-4 or Claude) with a structured retrieval layer connected to your event data — enabling the model to answer specific questions about your shows from your actual data, rather than from the model's training data or generated summaries. Without the RAG retrieval layer, an LLM queried about your shows would generate plausible-sounding but factually unreliable answers. With the RAG layer, the model first retrieves specific, relevant facts from your data warehouse (the actual attendance figures, session data, exhibitor records) and then formulates an answer grounded in those retrieved facts — citing the sources so you can verify accuracy. For a multi-event agency with 10 years of historical show data across 50+ events, a RAG knowledge base over that data is the difference between institutional knowledge that walks out the door when a staff member leaves and institutional knowledge that's queryable by anyone on the team.
Related Events Services
Ready to Fix Your Event Data Architecture?
Tell us how many shows you run and for how many clients, what systems your event data currently lives in, how far back your historical data goes, what your biggest GDPR or data management concern is, and whether you need a RAG system over historical data or an API integration layer first. We'll scope the right starting point.