Summary
Data quality isn't just a technical concern. It's fundamental to business trust and decision-making in private markets. As firms adopt AI and expand into complex asset classes and vehicles like asset-based financing (ABF), high-integrity data is essential. This post explores what data quality really means, its risks, and how to implement it effectively.
Ankit Jain
Ankit has 14 years of experience building technology-driven products for the investment management industry, focusing on turning complex operational challenges into scalable, user-centric solutions. His work intersects across product management and solutions architecture, where he combines strategic thinking with hands-on execution. Ankit has led the end-to-end development of platforms supporting the full investment lifecycle, from trade processing to reporting and analytics. He partners with stakeholders across business and technology teams to define product vision, prioritize roadmaps, and deliver robust and adaptable solutions.
Bibliography:
[i] George Washington University, https://it.gwu.edu/data-quality
[ii] Precisely, November 5, 2024. https://www.precisely.com/blog/data-integrity/2025-planning-insights-data-quality-remains-the-top-data-integrity-challenges?utm_source=chatgpt.com
[iii] National Association of State Chief Information Officers (NASCIO) and EY US, October 2, 2024. https://www.theconsultingreport.com/state-cios-face-data-quality-challenges-amid-growing-ai-adoption-survey-finds/
[v] InvestmentNews,May 6, 2024. https://www.investmentnews.com/ria-news/asset-managers-are-focusing-on-quality-data-and-a-better-investor-experience/253016
[vi] George Washington University. https://it.gwu.edu/data-quality
[vii] The role of data science in transforming business operations: Case studies from enterprises, Computer Science & IT Research Journal
P-ISSN: 2709-0043, E-ISSN: 2709-0051, Volume 5, Issue 8, P.2026-2039, August 2024, DOI: 10.51594/csitrj.v5i8.1490, Fair East Publishers
