CUSTODY

$15T Custody Data Strategy

At custody scale, every team keeping its own version of the data is how the picture blurs. This is the governance and reference-data blueprint co-designed for a business spanning trillions in assets under custody.

Problem statement

A large securities and fund services custody division — holding trillions of dollars in assets under custody — had data governance and reference-data practices that were fragmented by business unit. Different teams sourced, validated, and maintained overlapping reference data independently, leading to inconsistent data quality, duplicated vendor spend, and no single accountable owner for data as an enterprise asset.

At that scale, even small inconsistencies compound quickly — a reference-data gap that's a minor annoyance in a small operation becomes a material risk and cost problem across a $15T book.

Approach

Five steps, applied in sequence — each one deliberately checked back against the big picture before moving to the next, so the detail work never drifts from the actual business problem.

Discover & diagnose

Assessed the current-state data landscape across custody operations, asset servicing, and the business units it touched — surfacing where duplication and inconsistency were concentrated.

Model the domain

Co-designed a blueprint spanning data governance, quality management, and reference-data acquisition — treating custody data as a single enterprise asset rather than a set of siloed business-unit datasets.

Validate against the big picture

Stress-tested the blueprint against the day-to-day realities of trade processing, asset servicing, derivatives, and collateral management, so the model held up operationally and not just on paper.

Build

Established the governance operating model along with the reference-data acquisition and management processes it depended on.

Govern & sustain

Embedded ongoing quality metrics and clear accountability so the model held as new products, clients, and business units were onboarded.

What was built

An enterprise-wide custody data strategy and governance blueprint covering a $15T custody business — standardizing how reference data is acquired, validated, and maintained across business units instead of leaving each to solve it independently.

The blueprint was built to be operational from day one: every governance principle was checked against a real workflow in trade processing or asset servicing before being finalized, so it didn't just describe good practice, it fit how the business actually ran.

Business value

What it delivered

  • More consistent, higher-quality reference data across a $15T book of custody assets.
  • Reduced duplicated vendor spend from redundant, business-unit-level data sourcing.
  • A governance model built to scale as new products, clients, and business units were added.
  • Clearer accountability for data quality at the enterprise level rather than fragmented by team.

The custody data governance model, as a graph

How business-unit level data becomes a single, governed enterprise asset — drag any node to explore, hover for detail.

Core model Operational domain Governance / cost

Illustrative model shown for demonstration — the underlying structure is generalized from real work.

Further reading

Data Modeling from the POV of a Data Product Developer

On why a shared domain model, not a per-team dataset, is what actually resolves quality and governance problems at enterprise scale.

Modern Data 101

FIBO — Financial Industry Business Ontology

The EDM Council's standard ontology for financial concepts — the reference point for treating custody data as one enterprise vocabulary.

EDM Council

More case studies

Working on custody data, or see where it blurs differently? I'd enjoy comparing notes.

info@dataoptician.com

Generalized from personal experience and shared in a personal capacity — this reflects my own views, not my employer's, and isn't an offer of paid services.