Data in asset management
investment and client data: market and portfolio data, performance attribution, client analytics, and the compliance around it.
This block builds data fluency for asset and wealth management, where portfolio decisions, client reporting and regulatory filings all depend on trustworthy data. You will map the sector data landscape spanning security master files, pricing and reference data, benchmark constituents, holdings, transactions and client KYC records. You will learn the quality and governance metrics that matter, from pricing completeness and reconciliation breaks to lineage and vendor coverage. You will also address privacy rules governing client information, data regulation shaping cross-border flows, and the practical audits that keep NAV, performance and risk analytics defensible. The emphasis stays on data itself rather than valuation ratios, giving you sector-specific rigor applicable to front, middle and back office.
What you'll master
- Map the key AWM data sources and datasets across security master, pricing, benchmark, holdings and client records
- Define and apply data-quality and governance metrics such as completeness, timeliness, lineage and reconciliation break rates
- Design privacy and data-regulation controls covering client PII, consent and cross-border transfers
- Run practical data checks and audits validating NAV, holdings and performance data integrity
Key terms
Modules
Core data concepts applied directly to asset management decisions, performance, clients, and regulation.
How to source, reconcile, and measure the quality of the datasets that drive investment decisions.
How to govern, protect, and audit investment and client data under regulatory expectations.
Latest articles
Recent articles from the blog that apply to Asset & Wealth Management.
- How JPMorgan Chase built data contracts across 50+ domainsJPMorgan Chase spent years grappling with fragmented data ownership across hundreds of business lines before systematically formalizing who owns what and on what terms. Their approach to data contracts offers a working model for CDOs who need accountability without organizational paralysis.
- How JPMorgan Chase built data contracts across 50+ domainsJPMorgan Chase's data mesh initiative forced the bank to confront a problem most large organizations prefer to defer: who actually owns a data product, and what obligations come with that ownership? Their approach to data contracts offers a detailed, replicable model for CDOs managing complex, federated data environments.
- How JPMorgan Chase built data contracts across 50+ domainsJPMorgan Chase spent years wrestling with data inconsistencies across hundreds of business lines before committing to a structured data contract framework. The mechanics they chose, and the organizational friction they encountered, offer a practical blueprint for CDOs facing the same ownership vacuum.