Eliminating Data Errors to Protect SLA Commitments at Scale

Client Overview

A leading U.S.-based financial data provider serving institutional clients across North America and the UK needed to safeguard the accuracy and reliability of its daily data operations. The organization distributed critical datasets such as NAV, corporate actions, and fund reference data, where even a single undetected error could lead to SLA breaches, regulatory exposure, and reputational risk. As data volumes and operational complexity increased, leadership needed a more resilient validation framework to preserve quality at scale.

Problem Statement

The client’s operating model exposed the business to quality and control risks across multiple high-impact datasets.

Key issues included:

• Zero, negative, and duplicate NAV values entering downstream fund valuation workflows without timely detection
• No automated spike detection for abnormal day-over-day NAV movement
• Corporate actions data arriving with blank rows, missing symbol suffixes, and unchecked posting flag errors
• Fund reference data lacking structured change tracking, ratio logic checks, and completeness monitoring

For leadership, the challenge was not simply fixing isolated data issues. It was ensuring that operational controls could scale consistently enough to protect client commitments, maintain trust, and reduce dependency on manual review.

Solution Provided

Decimal Point Analytics designed and implemented a structured multi-layer validation framework to strengthen pre-processing controls across the client’s data pipeline.

The solution included:

• Multi-layer validation checks for positive values, duplicate codes, and date-format consistency within the NAV pre-processing workflow
• Amount and symbol-flag validation controls to improve the quality and consistency of corporate actions data
• A Python-based daily comparison tool for fund reference data to track additions, deletions, and field-level changes
• Logic checks for gross versus net expense ratios, along with automated null-count monitoring across critical fields

This approach enabled the client to move from reactive error identification to a more controlled, traceable, and scalable validation environment.

Outcome

The engagement delivered measurable improvements in data quality, visibility, and control discipline.

Results included:

• NAV and corporate actions data accuracy maintained above 99.99%, consistently supporting SLA adherence
• Zero duplicate fund code incidents and zero duplicate corporate action posting incidents after implementation
• Full daily visibility into fund reference data changes, creating a reliable audit trail for every modification
• Analyst effort redirected from manual file scanning to higher-value quality and oversight activities
• More than 8 automated validation controls deployed across the workflow
• 95%+ ratio field reliability and 98%+ expense ratio accuracy achieved

Key Takeaway

For financial data businesses, operational scale without validation discipline creates material risk. This engagement demonstrates that a well-designed control framework can do more than reduce errors. It can protect SLA performance, strengthen auditability, and create the operational confidence required to support growth.

Looking to strengthen data quality controls across critical workflows? Decimal Point Analytics helps financial services organizations build scalable validation frameworks that improve accuracy, reduce operational risk, and protect business-critical commitments. Contact us to explore how this approach can be applied to your data operations.