Data Quality Management

Faulty data kills profits—accurate, clean data powers everything from risk models to regulatory compliance, ensuring every trade and decision rests on solid ground. (Buzzelli, 2022; Reis & Housley, 2022)

Nippofin: Your data’s best friend.

You know that high-quality data is essential for financial computing. (Borowicz, 2024)

Nippofin’s Data Quality Management (DQM) services ensure your financial computations are accurate, reliable, and compliant.

Our DQM process covers three crucial aspects:

Ingestion & Validation

Collect and validate data from various sources using automated rules.

Transformation & Cleansing

Standardize, transform, and cleanse data to ensure consistency and accuracy.

Monitoring & Reporting

Continuously monitor data quality and generate regular reports to maintain integrity and compliance.

And the benefits to our clients include:

Accurate models, fewer errors, better decisions
Lower latency, fewer errors, better trades
Reliable assessments, robust tests, compliant results

AAA Quality Data

Six steps to accurate, adequate, and actionable insurance data

January 05, 2024

References

2024

OUP

The data quality problem (in the European Financial Data Space)

M Konrad Borowicz

International Journal of Law and Information Technology, 2024

Abs

By creating the European Financial Data Space (EFDS), the European Commission aims to foster the development of data-driven innovation in the financial sector. Artificial intelligence (AI) is the most notable innovation that could benefit from enhanced access to data through the EFDS. However, the current policy framework establishing the EFDS suffers from a major limitation—it neglects the question of data quality or whether the data shared in the EFDS will be fit for the purpose of the development of AI applications. The AI Act and General Data Protection Regulation could play a role in fostering data quality in this area, but the two frameworks are also unlikely to do that to a satisfactory extent. To address the data quality problem, this article proposes policymakers should develop a framework for data quality that encompasses both a conceptual and an organizational dimension aimed at promoting the free flow of high-quality data in the EFDS.

2022

O’Reilly

Data Quality Engineering in Financial Services

Brian Buzzelli

2022
O’Reilly

Fundamentals of data engineering

Joe Reis, and Matt Housley

2022