Skip to main content
Within Cotality, enterprise data teams act as the centralized engine that turns raw, disparate data into trusted, secure, and compliant consumption-ready assets. For developers and integration teams, understanding this group explains where the data comes from, how it is governed, and what rules apply when building on top of it.

​
Who enterprise data teams are

Enterprise data teams comprise four functional groups: Enterprise Data Management (EDM), Data Analytics, Data Science, and Data Platform. Together they operate as the “hub” in a hub-and-spoke stewardship model that spans every business domain.

The hub — centralized data teams

Enterprise Data Management (EDM), the Chief Data Office (CDO), and Data Platforms. They provide standardized templates, data-engineering pipelines, central tooling (including the Collibra Data Catalog), and compliance audits.

The spokes — federated domain teams

Domain-embedded data owners, stewards, and subject matter experts (SMEs) across business units — Real Estate, Data Solutions, Mortgage, Insurance — who manage specific functional data assets.

​
What enterprise data teams do

Enterprise data teams manage data across its entire lifecycle — from initial sourcing through productization — organized around four pillars.

​
1. Data stewardship and knowledge engineering

  • Metamodel curation — Defining standardized business vocabularies, enterprise ontologies, and technical schemas
  • Critical data element (CDE) tracking — Identifying and tagging key business elements (KBEs) vital for business operations, regulatory reporting, and downstream product integrations
  • Data lineage mapping — Constructing end-to-end lineage maps that trace data from its point of origination through all transformations to final consumer endpoints

​
2. Total quality management

Continuous profiling and automated validation across four core dimensions: Initial and periodic profiling detects structural anomalies, null sets, and formatting errors. Root-cause analysis and remediation pipelines prevent bad data from propagating downstream.

​
3. Data protection, privacy, and access

  • Data classification — All incoming data assets are categorized into security levels: Public, Confidential, and Non-Public Personal Information (NPI)
  • Privacy-by-design guardrails — Row-level and column-level masking, automated Personal Information (PI) and Sensitive Personal Information (SPI) filtering, and exfiltration defenses embedded into AI/ML data flows
  • RBAC and least privilege — Role-based access controls ensure developers and platforms interact only with the minimal dataset required for their functions

​
4. Integration, processing, and fulfillment

Raw external data is cleansed and standardized, then engineered into three classes of advanced derivative assets:

​
Data domains under governance

Enterprise data teams govern 13 core data domains organized into three logical groups.

​
Master domains

​
Transactional domains

​
Corporate domains

​
Guidelines for developers integrating data

Permissible use certification — No consumption-ready data asset is eligible for API integration or product deployment until the designated data owner formally certifies that it complies with all legal, regulatory, and contractual sharing restrictions.
AI input validation — If you are building AI systems or utilizing MCP servers, all data pipeline training sets and configuration files (e.g., YAML) must be formally logged in the Metadata Catalog.
Derivative data rules — Any predictions, classifications, or synthetic attributes generated by downstream machine learning models are classified as new data assets and must immediately be registered under Cotality’s data governance policy.