Overview
With index businesses growing in scale and complexity, production cycles are tightening and expectations around lineage, auditability, and documentation are increasing. Yet for the teams running index operations, fragmented systems, manual workbook-based calculations, and corporate-action workflows continue to create most of the risk and delay.
To fix this, a major exchange asked Evalueserve to modernize its end-to-end index ecosystem—across data ingestion, calculation, validation, reporting, and governance—and upgrade its index platform for scalable growth. As a result, production timelines for index variants representing nearly 80% of all new launches were cut from up to two days to about one hour, powered by a centralized backtesting and rebalancing framework that lets analysts implement strategies faster and analyze constraints, criteria, and constituent attributions in far more depth.
Challenge
The exchange’s legacy index platform was creating recurring problems for their Director of Index Products and their team. Key issues included:
- Fragmented data landscape: Data ingestion was spread across vendor databases, flat files, CSVs, and Excel spreadsheets, creating lineage gaps, versioning risk, and inconsistent downstream consumption.
- Manual, non-scalable index calculations: Index logic and daily workflows relied heavily on Excel. Operational teams often had to trace issues manually across data sources, calculations, rulebooks, and reports—slowing turnaround time and increasing error rates.
- Siloed teams and disconnected processes: Business, operations, technology, and strategy groups all interacted with the platform, but without a standardized, auditable operating model.
- Inefficient downstream delivery: Internal and external consumers received manually generated Excel/PDF outputs rather than distributed dashboards or automated delivery pipelines.
To address these issues, the exchange required a complete modernization effort—spanning data architecture, governance, calculation engines, workflow automation, and future AI readiness.
Our Approach
To better understand the client’s needs, Evalueserve conducted a mix of onsite and remote workshops, engaging business, operations, technology, and strategy stakeholders. The approach combined top-down strategic perspectives with bottom-up, user-driven insights. A structured project plan, supported by a dedicated project management layer, ensured high-quality delivery and kept the project on track—aligning the final solution with long-term platform objectives while addressing day-to-day operational realities.
Evalueserve then executed a phased transformation across the full index value chain, led by a blended, cross-functional team including:
- Index quants and operations specialists
- Technical business analysts
- Solution architects and full-stack engineers
- Cloud, DevOps, and Python experts
- Project Managers
The work progressed through three major workstreams:
1. Reengineering and Modernizing Core Platform Foundations
The first priority was to strengthen the underlying data and technology foundations so the exchange could grow without adding operational friction. Evalueserve began by mapping the fragmented data landscape across databases, flat files, Excel, and APIs, then designing a unified, cloud-based architecture with clear lineage, versioning, and traceability.
On top of this, the team defined a scalable ingestion–storage–consumption-governance framework that supports multiple asset classes (equities, fixed income, index-of-index, futures, fixed income, and ESG) and incorporates data segregation, labeling, and classification to comply with vendor licensing. As part of this exercise, they also identified opportunities to reduce costs through alternative data providers and built a flexible switch-over mechanism.
In parallel, legacy Excel-based backtesting and calculation engines were reengineered into modular Python components, migrating around 1,000 workbooks into reusable code. This made the rulebook, normalization, rebalance, and calculation logic easier to manage and significantly accelerated scenario testing and index R&D. To ensure the new platform could be evolved safely, modern DevOps practices—including version control, and peer code reviews—were introduced.
2. Streamlining Index Production Through a Centralized Workbench
Once the foundations were in place, the focus shifted to day-to-day production. Core index workflows that previously relied on Excel and manual data tracing were automated, so analysts no longer had to jump between tools and files to resolve issues. Earlier, even basic data, calculation, and code checks depended on quarterly release cycles, with many vulnerabilities needing intervention from platform and release teams, which slowed production deployment. With the new setup, analysts can instead use a centralized workbench to navigate ingested data, rulebooks, index levels, and constituent weights from one place.
This centralization also enabled more consistent and auditable outputs. Calculations were standardized across the board, and report generation – whether in Excel, PDF, or customized templates – was automated. As a result, index variants that once took 1–2 days to design, backtest, and publish can now typically be launched in about an hour. The business can spin up new variants within an existing index family quickly, without compromising methodological consistency.
3. Strengthening Corporate Actions and Rulebook Governance
Corporate action workflows were automated by integrating ingestion and adjustment logic from specialized vendors, and by designing governance paths covering more than 40 approval scenarios, including overrides, corrections, and exception handling.
Evalueserve also embedded multi-vendor validation and compliance into the platform. It became possible to switch between data providers at an asset-class level while maintaining clean lineage through careful classification, labeling, and segregation aligned to licensing terms. Rulebook management was formalized by clarifying roles and steps for creating new rulebooks, establishing periodic reviews against internal and regulatory standards, and validating index constituents against rulebook criteria to avoid methodology drift or limit breaches. In parallel, the program identified high-impact areas for future use of agentic AI, such as corporate action validation, rulebook review, and exception detection.
Business Impact
The exchange significantly compressed production timelines for index variants—covering nearly 80% of all new launches—from up to two days to roughly one hour. In parallel, it realized broader gains in efficiency, control, and scalability by transitioning to a multi-vendor data model without operational disruption, reducing cost concentration, and improving data availability across asset classes.
Operational governance became much stronger as ingestion, calculation, validation, and reporting were unified into a single, traceable workflow. This introduced clearer data lineage and documentation, consistent labeling, and a formal maker–checker approval model that supports audit readiness.
A modular, modernized platform now supports rapid index innovation and easier integration of new datasets. Crucially, the client can expand its index family without scaling operational headcount at the same rate, positioning the business for continued efficient growth.
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Overview & Impact
Evalueserve helped a major global exchange modernize its end-to-end index operations by rebuilding data foundations, automating calculations, and strengthening governance to enable faster, scalable index launches.
