Workspace 06

Data and Integration

Workspace objective

Assess data availability, quality, ownership and integration requirements

This workspace identifies the data sources, data quality, ownership, access, lineage, integration capabilities and information-management controls required to support the initiative.

Workspace progress

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Data and Integration

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What data is required to support underwriting, review, call-over and AI use cases?*

Consider customer, transaction, application, repayment, credit, fraud, document, behavioural, product and decision data.

Which systems and sources currently hold the required data?*

Include internal systems, data warehouses, document repositories, spreadsheets, credit bureaus, identity services and third parties.

Who owns and is accountable for each important data domain?*

Identify business data owners, data stewards, system owners and technical custodians.

What data-quality issues currently affect the processes?*

Consider missing values, duplicate records, inconsistent formats, stale data, incorrect values, incomplete documents and conflicting sources.

What controls currently identify and correct data-quality issues?*

Include validation rules, reconciliations, duplicate checks, exception reports, manual review and data-cleansing processes.

Is sufficient historical data available for analysis, testing or model development?*

Consider the number of years available, data completeness, decision outcomes, defaults, overrides, exceptions and archived records.

Are reliable outcome labels available for model training and evaluation?

Examples include approved, declined, repaid, defaulted, fraudulent, exception confirmed, exception cleared and reviewer decision.

How is access to required data currently requested and approved?*

Include access requests, approvals, role-based access, privileged access, vendor access and environment restrictions.

How is relevant data classified and protected?*

Consider confidential, personal, sensitive, financial, regulatory and restricted information.

Can the origin, transformation and use of important data be traced?*

Identify available lineage records, transformation logic, reconciliation controls and source-of-truth definitions.

What master and reference data is required?*

Consider customers, products, branches, risk grades, decision codes, policy rules, limits and organisational structures.

What systems must exchange data with the proposed solution?*

Identify source systems, target systems, real-time interfaces, batch interfaces, document repositories and reporting platforms.

What integration standards and platforms must be followed?*

Include API standards, middleware, event platforms, security protocols, file standards, naming conventions and architecture principles.

Which data exchanges must occur in real time and which can use batch processing?*

Consider decision response time, customer experience, fraud checks, bureau checks, reporting and reconciliation.

What retention, archival and deletion requirements apply?*

Consider legal obligations, regulatory retention, audit needs, data-protection requirements and model-development needs.

Are there restrictions on where data may be stored, processed or transferred?

Consider cloud use, cross-border transfer, third-party processing, test environments and non-production data.

What data or integration dependencies could affect delivery?

Consider unavailable APIs, legacy interfaces, vendor approvals, data remediation, platform upgrades and access delays.

Can the team provide data dictionaries, interface specifications and sample data?

Include data models, field definitions, API specifications, file layouts, mapping documents, data-quality reports and anonymised samples.

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Dashboard

Why this information matters

The success of AI, automation and intelligent decision support depends on trustworthy data and reliable integration. This workspace establishes whether the required information is available, usable, controlled and accessible.

Completion guidance

Identify authoritative sources rather than relying only on downstream copies.
Record known data-quality problems and their operational impact.
Distinguish real-time requirements from batch requirements.
Include internal, external, structured and document-based data.
Document access, classification, retention and transfer restrictions.

Information captured

Required data
Source systems
Data ownership
Data quality and controls
Historical data
Outcome labels
Access and classification
Data lineage
Master and reference data
Integration requirements
Retention and transfer restrictions
Delivery dependencies

Why this information matters

Completion guidance

Identify authoritative sources rather than relying only on downstream copies.
Record known data-quality problems and their operational impact.
Distinguish real-time requirements from batch requirements.
Include internal, external, structured and document-based data.
Document access, classification, retention and transfer restrictions.

Information captured

Required data
Source systems
Data ownership
Data quality and controls
Historical data
Outcome labels
Access and classification
Data lineage
Master and reference data
Integration requirements
Retention and transfer restrictions
Delivery dependencies