Case Study
A global market infrastructure provider struggled with inconsistent data models across 1100+ market data feeds, conflicting security identifiers, and quality issues threatening client trust and regulatory compliance.
DataCatalyst applied their Accelerate methodology with lean expert teams to design a unified multi-domain data model, comprehensive governance frameworks, and automated quality controls in support of a bespoke Enterprise Data Management (EDM) system.
New feeds can be onboarded in weeks versus months, substantial reduction in market data issues, full regulatory compliance, and productivity equivalent to 200 personnel.
A leading global market infrastructure provider faced a critical challenge that threatened its competitive position. Despite processing over 1100 incoming market data feeds from exchanges, data vendors, and proprietary sources worldwide, the organization struggled with fundamental data consistency issues that directly impacted thousands of international clients relying on their feeds for trading and valuation decisions.
The core problems were deeply rooted in their data architecture:
Absence of proper traceability and governance frameworks put the organization at risk of regulatory non-compliance and contractual violations.
The organization recognized that incremental fixes wouldn’t solve these systemic issues. They needed a comprehensive transformation that would create a single source of truth for all market data domains while maintaining the low-latency performance their clients demanded.
The business case was compelling: data quality escalations were consuming significant client service resources, inconsistent feeds were threatening client retention, and the fragmented approach was preventing rapid launch of new market data products. Most critically, regulatory requirements for market transparency were becoming more stringent, demanding full lineage and auditability across all data flows.
DataCatalyst applied our proven Accelerate methodology to tackle this complex, multi-domain challenge. Our lean team of seasoned experts – combining deep market data expertise with enterprise data management experience – worked directly with the client’s business stakeholders to ensure the solution delivered measurable value.
The Accelerate framework enabled us to break down this massive undertaking into manageable components while maintaining focus on business-driven requirements. Rather than getting lost in technical complexity, we prioritized deliverables based on client impact and regulatory urgency, ensuring rapid time-to-value throughout the engagement.
Our agile hybrid approach allowed us to design the foundational data models while simultaneously mapping critical feeds, accelerating delivery without compromising quality. The emphasis on documentation and stakeholder alignment – hallmarks of our methodology – proved essential when coordinating global stakeholders across multiple asset classes to agree on unified standards.
DataCatalyst delivered a comprehensive enterprise data management solution spanning five critical domains:
The technical solution included logical and physical multi-domain data models designed for extensibility, feed mapping specifications for all 1100 inbound sources with transformation rules to normalize diverse formats and identifiers, and a comprehensive data quality rule library covering completeness, timeliness, accuracy, and referential integrity.
We also established a governance framework featuring clearly defined roles, escalation procedures, and workflows supported by KPI and quality monitoring dashboard specifications. This operating model included a global council and regional stewards empowered to maintain data standards and resolve issues quickly, ensuring sustainable long-term success.
The implementation fundamentally transformed how the organization managed and delivered market data. Where previously each business unit operated with its own data definitions and processes, the new enterprise standard model created consistency across all asset classes and geographies.
The feed mapping and normalization processes eliminated the identifier conflicts that had plagued trading operations, while automated data quality controls caught issues before they reached clients. The governance framework established clear accountability and rapid resolution procedures, dramatically reducing the time from problem identification to solution.
Most importantly, the solution was designed to integrate seamlessly with the client’s bespoke EDM platform, with all specifications delivered in formats directly consumable by their in-house development team. This approach accelerated the build phase and ensured the solution met their exact technical and business requirements.
The transformation delivered significant measurable improvements across multiple business metrics:
The business impact extended beyond operational improvements. Clients can now receive harmonized, trusted data feeds regardless of region or asset class, strengthening relationships and supporting retention. The organization gained the foundation needed for a faster launch of new market data products and services, opening new revenue opportunities.
Perhaps most remarkably, our assembly-line data modeling approach – AgileModeling – produced output equivalent to 200 other personnel in the same time period.
The enterprise data management foundation now positions the organization for continued innovation and market leadership. The extensible data models can accommodate new asset classes and market structures as they emerge, while the governance framework ensures consistent quality standards as the business scales.
The standardized approach to data quality and lineage provides the reliability needed to explore advanced analytics and algorithmic trading support services. With consistent, trusted data across all domains, the organization can focus on developing new products and services rather than firefighting data quality issues.