AI-Ready Data in Financial Services : The Delivery Playbook

In Part 1, we introduced why“AI-ready data” is a different bar in financial services: it must be fit for a specific use case and defensible under audit, model-risk, privacy, and regulatory scrutiny.

If you haven’t read Part 1 yet, start here: Optimizing Data Readiness for AI Modeling in FinancialServices

This Part 2 blog is the implementation guide. It translates the concept into an end-to-end delivery blueprint: what to build, what controls to put in place, what documentation to produce, and how to operationalize AI-ready data so it works reliably in production.

What makes part 2 different?

• Less theory, more execution: architecture, gates, and artifacts you can deliver.

• A practical approach for all data types: system data, external/third-party data, and unstructured data for RAG.

• A readiness scorecard and an AI-Ready Pack checklist to make progress measurable and repeatable.

A working definition we use in delivery

AI-ready data is governed, contextualized, and operationalized data that can be consumed repeatedly (by models and humans), at scale, with predictable quality, cost, and risk.

In financial services, that definition implies:

• Traceability: lineage and reproducibility down to what drove a decision.

• Controls: access, retention, masking, and purpose limitation enforced (not just documented).

• Operational reliability: SLAs, tests, monitors, and run books.

• Explainability readiness: features and inputs that can be reviewed and defended.

The scope: three data categories you must make AI-ready

Most AI programs stumble because AI-ready data gets reduced to a single pipeline. In practice, it is a portfolio of governed pipelines and assets spanning:

• System data: core operational platforms (lending, payments, trading, servicing, claims, CRM).

• External data: bureaus, KYC/AML utilities, market data, open banking feeds, alternative data.

• Unstructured data: policies, SOPs, underwriting notes, call transcripts, emails, documents, PDFs and scans.

How we organize it: medallion architecture plus AI layers

Medallion architecture makes readiness tangible: move from raw to standardized to certified assets, with explicit quality and governance gates at each stage.

Bronze: land data safely (raw but controlled)

• Immutable landing with source metadata (system of record, extract time, license constraints).

• Security from day 1: encryption, RBAC/ABAC, masking rules, retention tags.

• Schema versioning and ingestion logs (critical for third-party feeds).

Silver: standardize and validate (trusted operational data)

• Canonical schemas and identifiers (customer, account, entity, instrument).

• Quality checks and reconciliation against business rules.

• Reusable transformation patterns (not one-off scripts).

• For unstructured: normalize text, de-duplicate, redact, and enrich with metadata.

Gold: package for consumption (data products and features)

• Business-aligned data products with contracts, definitions, and SLAs.

• Curated aggregates and certified metrics.

• Reusable feature views / feature store for model consistency.

• For GenAI: retrieval-ready document collections and governed vector indexes tied to provenance.

AI layers that sit alongside Gold

• Feature layer: versioned, governed features with online/offline parity.

• Vector layer: embeddings index with metadata filters andchunk-to-document lineage.

• Semantic layer: glossary and ontology that standardize meaning.

• Observability layer: monitoring for pipeline health, drift, data breaks and cost.

Unstructured data delivery: make documents query-able with accountability

GenAI outcomes depend on unstructured data, but most teams jump to embeddings too early. We start by making documents query-able with accountability: every answer must be traceable back to authoritative sources under the right access controls.

The document readiness flow

1. Inventory and ownership: what exists, where it lives, who owns it, and what is authoritative.

2. Classification: sensitivity tags (PII/PCI/confidential), jurisdiction,and retention rules.

3. Canonical versioning: reduce duplicates, track effective dates and superseded content.

4. Parsing and redaction: OCR/extraction with traceable transforms;remove sensitive content where required.

5. Chunking by structure: chunk by headings/sections (not arbitrary tokens) and preserve citations to page/section.

6. Metadata-first indexing: business taxonomy, regulatory taxonomy, product/process context, access tags.

7. Hybrid retrieval: vector + keyword + structured filters (role, jurisdiction, effective date).

8. Evaluation and monitoring: retrieval quality harness, hallucination controls, drift as documents change.

This is the practical heart of our GenAI Data & Document Readiness Accelerator: it standardizes these steps so teams can move fast without creating a new compliance surface.

The AI-Ready Data Scorecard

To avoid vague conversations, we use a simple scorecard that measures readiness per domain and use case. It forces clarity on what must be true before AI can be trusted.

Dimension	What good looks like	Typical failure mode	Evidence / artifacts
Align to use case	Coverage, correct granularity, stable definitions, right cohorts/time windows	Data exists but does not represent the decision context	Use-case data contract; glossary; source-to-target mapping
Quality & qualification	Automated checks, reconciliation, regression tests, thresholds tied to confidence needs	One-time cleansing; no continuous qualification	DQ rules; test suite; exception workflow; quality dashboards
Governance & control	Access, retention, masking, purpose limitation enforced; approvals captured	Controls documented but not enforced; shadow copies spread	Classification policy; RBAC/ABAC; audit logs; stewardship sign-offs
Lineage & reproducibility	You can trace back and reproduce features/inputs for a decision months later	No lineage; transformations live in ad-hoc scripts	End-to-end lineage; versioning; reproducibility runbook
Unstructured readiness (GenAI/RAG)	Canonical documents, chunking strategy, metadata filters, evaluation harness	PDF dump + embeddings with no provenance or access control	Content inventory; chunk-to-doc lineage; retrieval eval results
Operational readiness	SLAs, monitors, runbooks, dependency visibility, safe backfills	Pipelines work only when key individuals are available	Orchestration DAG; on-call/runbooks; incident process

The AI-Ready Pack: what we deliver besides the data

Stakeholders usually ask for AI-ready data, but what they really need is the full operating system around it. We package that as an AI-Ready Pack so production teams, auditors, and model-risk stakeholders all have what they need.

Category	What’s included (examples)
Data & pipelines	Bronze/Silver/Gold datasets; data contracts; orchestration logic; backfill/replay procedures
Metadata & semantics	Business glossary; metric definitions; ownership; domain taxonomy; catalog entries
Lineage & evidence	Source-to-target mapping; transformation logic; lineage diagrams; approvals and change history
Quality & controls	DQ rules and thresholds; regression tests; exception routing; access controls; masking
Operations	SLAs; dependency graph; schedules; on-call playbook; incident response and postmortems
GenAI / unstructured readiness	Document inventory; classification; redaction policy; chunking strategy; vector index governance; retrieval evaluation

BBI methodology: a repeatable delivery approach

Our approach is consulting-style but engineering-led: deliver one high-value use case quickly while building reusable foundations that scale across domains.

Phase 1 — Frame the outcomes and confidence contract (2–3 weeks)

• Select 1–2 priority use cases (e.g., credit decisioning, fraud detection, claims triage, contact center automation).

• Define the AI input contract: required fields, refresh/latency, and acceptable risk.

• Agree readiness KPIs: time-to-data, quality thresholds, and audit evidence requirements.

Phase 2 — Inventory and assess the data estate (2–4 weeks)

• Map system, external, and unstructured sources; identify authoritative systems and owners.

• Run profiling and readiness assessment: quality gaps, lineage gaps, control gaps, and operational risk.

• Classify sensitive data and map regulatory constraints (retention, residency, purpose limitation).

Phase 3 — Build the medallion pipelines and controls (4–8 weeks)

• Implement medallion patterns and reusable ingestion templates.

• Add reconciliation, quality gates, schema-drift protections, and versioning.

• Establish governance workflows and evidence capture.

Phase 4 — Deliver document readiness for GenAI (parallel track)

• Document inventory, canonicalization, metadata completion, redaction and retention enforcement.

• Chunking and indexing strategy by document type; provenance and access filters.

• Evaluation harness and monitoring for retrieval quality as content changes.

Phase 5 — Package as data products and model-ready assets (4–6 weeks)

• Create governed data products with definitions and SLAs for each use case.

• Provide feature views / feature stores where reuse and consistency matter.

• Deliver the AI-Ready Pack: lineage, controls, run books, and audit evidence.

Phase 6 — Operate and continuously qualify (ongoing)

• Observability: freshness, anomalies, quality regressions, drift and cost controls.

• Incident response and postmortems for data failures (treat data like production software).

• Onboard new sources (including alternative data) using the same governance-first gates.

How this connects to BBI capabilities

Teams typically engage BBI using a combination of consulting, accelerators, and delivery pods depending on urgency and maturity.

Common engagement modules:

• AI-Ready Data Foundation for Financial Services: readiness assessment, roadmap, and build-out of certified data products.

• GenAI Data & Document Readiness Accelerator: make unstructured data safe and usable for RAG with provenance and controls.

• Data Quality & Golden-Record Platform: entity resolution and consistent identifiers across domains.

• Data Migration Factory: modernize legacy estates with repeatable controls and minimal business disruption.

• Operational Assistant for Data & IT Platforms: accelerate triage, dependency visibility, and run book quality.

• Google Cloud Security Guardrails & Compliance Toolkit: secure-by-default foundations for regulated workloads.

• Alternative Data Onboarding: governed onboarding of new external/alternative sources with validation, licensing and purpose limitation enforcement.

• Regulatory Scrutiny Readiness: audit-ready documentation packs (lineage, approvals, evidence) to support MRM and examinations.

Closing thought

AI-ready data is not a one-time cleanup. It is a repeatable way of operating: governed onboarding, medallion delivery patterns, strong metadata and lineage, and production-grade reliability. When you do it right, AI stops being a lab experiment and becomes an operational capability.