Data and Analytics Business Data Model: AI-Ready Edition

The Data and Analytics Business Data Model: AI-Ready Edition is for architects and transformation leads in financial services firms who need to know what information data and analytics work runs on before an AI agent touches it. It covers 16 subject areas, 115 entities, 743 key attributes and 388 relationships. Every entity and key attribute is classified for sensitivity, with the AI uses and data-quality requirements an agent depends on. It is the data-model part of Business Architecture for Data and Analytics: AI Transformation Blueprint, sold on its own.

What's Inside

The same model comes in three files: the full model in Word, a workbook to filter and extend, and a guide to the scales and the license.

• 16 subject areas and 115 entities, each with a definition, its typical system of record, sensitivity class, AI uses and data-quality requirements
• 743 key attributes with sensitivity and AI relevance, and 388 relationships with cardinality
• Under each entity, the capabilities that need it and the agents that read it, by name and ID
• The Business Data Model (Word): every subject area and entity in full
• The workbook (Excel): subject areas, entities, attributes and relationships in sheets you can filter and extend
• The guide (Word): the scales the model refers to, the data foundation, and the license
• Free corrections, and new versions free for 180 days

Every file names your organization and carries your license ID.

The 16 Subject Areas

The 115 entities are grouped into 16 subject areas. The list gives what each holds and how many entities it has.

• Data and Analytics Strategy, Investment & Demand (6 entities): Where data and analytics is going and what it is asked to do: the strategy, the initiatives that carry it, their investment cases and tracked benefits, the value placed on key data assets, and the requests that arrive from the business.
• Skills & Data Literacy (3 entities): The skills data and analytics work needs and how the organization builds them: skill requirements for each role, gap assessments at team and role level, and data literacy programs.
• Data Governance & Policy (6 entities): How data is governed: the Data Governance Council and its decisions, data policies, data standards, data business rules and the exceptions granted against them.
• Data Domains, Ownership & Classification (6 entities): Who is accountable for which data and how sensitive it is: data domains, Data Owner and Data Steward assignments, the classification scheme, each asset's classification and criticality, and the handling rules for each level.
• Data Access & Use (5 entities): Who may use which data and for what: the data access policy, each domain's access rules, access requests from people and agents, the Data Owner's decision on each, and findings from reviews of use against approved purposes.
• Personal Data & Privacy Risk (7 entities): Where personal data sits and what it may be used for: the inventory of data sets holding personal data, recorded purposes, locations and transfers, personal data request tasks, privacy risk assessments, privacy requirements and breach impact assessments.
• De-identification & Responsible Data Use (7 entities): Using data responsibly: de-identification methods, de-identified and synthetic data sets, re-identification risk tests, data ethics reviews, data bias assessments, reviews of data uses that inform decisions about individuals, and recorded restrictions on use.
• Data Sharing, Retention & Disposal (8 entities): Data leaving its home or its life: sharing proposals and agreements, obligations on recipients, release records, licenses for outside data, the retention schedule, preservation holds and disposal approvals.
• Data Architecture, Models & Migration (9 entities): How data is structured and moved: the target data architecture, conformance reviews, data models and their changes, interface specifications, platform and recovery requirements stated to Technology, and data migrations with their reconciliation results.
• Data Pipelines, Operations & Data Products (10 entities): Data in motion and the reusable data built from it: data sources, data flows, their runs and failures, change releases, event streams, processing cost, and data products with their commitments and consumers.
• Data Quality (9 entities): Whether data is fit for use: quality expectations, critical data elements, quality rules and thresholds, data content assessments, check results, quality issues, their root causes and approved corrections.
• Master & Reference Data (8 entities): One version of shared business entities and codes: mastered entity definitions, master records, match candidates and decisions, hierarchies, master data change requests, reference code sets and mappings between them.
• Metadata, Catalog & Lineage (6 entities): What data exists, what it means and where it came from: data assets in the catalog, business terms, technical metadata, the metadata standard, lineage links and change impact analyses.
• Metrics, Reporting & Analytics Service (10 entities): The analytics service and its outputs: service definitions, questions from users, official metrics and their single calculation, metric changes, figure certifications, reports, distributions, threshold alerts and user-built content.
• Data Science & Training Data (8 entities): Analysis for decisions and data for AI models: analytical questions, analyses, handovers of results, experiments and their results, training data sets, labeling tasks and training data fitness assessments.
• AI & Digital Labor Governance (7 entities): The AI that works inside data and analytics: AI systems and agents, the log of agent actions, AI decision records, validation and human review records, and the authority limits each agent works within.

Who Uses It

Data owners and stewards, data architects, and AI and analytics engineers who need to know what an agent may read, how sensitive it is and how good it must be first; and business architects who link capabilities to data.

How It Fits With the Other Products

The capabilities named under each entity are defined in the Data and Analytics Capability Model: AI-Ready Edition. The agents are specified in Business Architecture for Data and Analytics: AI Transformation Blueprint, which contains this data model and adds the value streams, use cases, controls, an operating model and a roadmap. The Data and Analytics Value Streams are sold on their own too.