05 · CASE STUDY · DOCAPOSTE

Data Hub & AIServicesIndustrialisation.

Industrialising a B2B2C Data Hub and Data/AI service portfolio through platform strategy, sovereign architecture, data governance, Digital Trust and cross-functional execution.

Effective mandate: Head-of-Product-level Data Product delivery across Data Hub strategy, governance, secure platform execution and AI-services industrialisation.

Digital Trust Services2019–2023

Fractional consulting mandateData Hub · MDM · APIs

AI ServicesData governanceGDPR

HDSISO/IEC 2700160+ contributors

Explore the journeys ↓View the data platform

Data / AI service portfolioOne governed operating system.

60+ contributors coordinated

PlatformData Hub

ProductsData Services

IntelligenceAI Services

TrustDigital Trust

InfrastructureSovereign Cloud

Regulated servicesHealth · Claims

E2E

Shared delivery capabilitiesProduct · Data · Data Governance · AI · MDM · APIs · Cloud · Security · Compliance · PI Planning · Operations

Role / mandateProgramme leadership · Transformation delivery

A consulting assignment with effective Head-of-Product-level scope across Data Hub strategy and delivery.

Effective mandateData Product programme portfolio · E2E delivery · Platform leadership

End-to-end accountability spanning roadmap, governance, architecture, delivery readiness and service adoption.

Execution frameworkSAFe Framework

Portfolio priorities, architectural epics, cross-team dependencies and release execution coordinated at scale.

Leadership scale60+ contributors

Product, Data, Engineering, Architecture, Cloud, Security, Legal, Compliance, Business and partners.

01 · STARTING POINT

From Data/AI services to a governed platform model.

The challenge was not one dataset or one application. B2B2C services, Data Hub capabilities, AI-enabled products and Digital Trust requirements had to operate as one scalable platform system.

Industrialise the service portfolio without weakening trust.

DOCAPOSTE combined Data Hub marketplaces, Data/IoT services, AI-enabled capabilities and customer-facing Digital Trust services. Each product depended on shared data foundations, APIs, cloud infrastructure, security controls and cross-functional delivery capacity.

The assignment therefore extended beyond conventional backlog ownership: connect Product, Data, Technology, Security and Compliance around a durable operating model capable of supporting secure, repeatable B2B2C delivery.

Platform complexity

Data Hub · APIs · cloud · edge

Backend services, field devices, sovereign infrastructure and customer experiences had to work as one platform.

Governance complexity

MDM · quality · security · compliance

Structured foundations required formal ownership, interoperability, traceability and controlled change.

Operating complexity

Business · Product · Data · Technology

More than 60 contributors and external partners had to align around common priorities and delivery decisions.

02 · PORTFOLIO

One Data Hub. Multiple services. One trusted delivery ecosystem.

The portfolio had to preserve specific customer and public-service use cases while governing the shared data, platform, security and delivery capabilities on which they depended.

SERVICE DEMAND

B2B2C use cases

Digital public services

Field services

Certified claims evidence

Health & social alerts

Partners · citizens · clients

SHARED CAPABILITIES

Data Hub & AI services platform

EdgeCollection

IntegrationSecure API Gateway

StreamingKafka · Flink

Data platformSovereign Data Lake

GovernanceMDM · trusted products

ConsumptionOperational APIs

IntelligenceAnalytics · alerts

AISovereign AI services

OPERATING NETWORK

Execution layer

Product & UX

Data Engineering

Architecture & APIs

Cloud & DevOps

Security & Compliance

Business & Partners

03 · MANDATE

Product leadership.Platform-level accountability.

The consulting mandate covered programme leadership and transformation delivery. The effective mandate combined connected Data Product strategy, Data Hub roadmap, governance, architecture, secure delivery and AI-services industrialisation.

Product leadership extended across the platform lifecycle.

I held end-to-end Data Product responsibility across business feature demand, roadmap and PI planning, platform delivery, governance, production readiness, quality, deployment and user journeys.

The scope included Data Hub strategy, MDM and data governance, cloud and API coordination, security and compliance, business value, adoption and cost considerations, while aligning more than 60 contributors across Product, Data, IT, Architecture, Cloud and business teams.

Engagement2019–2023

Approximately 1.0 ETP through 2021, transitioning to a fractional consulting model.

People60+ contributors

Cross-functional coordination rather than hierarchical line management.

Data & AIData Hub · AI services

Data products, MDM, data governance, APIs, secure cloud consumption and AI-enabled capabilities.

Execution frameworkSAFe · PI Planning

Portfolio backlog, architectural epics, dependencies, cadence and production progression.

04 · OPERATING MODEL

Create the delivery system around secure Data/AI services.

The operating model connected portfolio direction, Data Product governance, architectural epics, Agile Release Train execution and production controls into one delivery cadence.

01 · EXECUTIVE & BUSINESS

Vision · value · guardrails

Business teams, partners, Legal and Compliance define outcomes, priorities, constraints, risk and public value.

02 · PRODUCT / DATA LEADERSHIP

Roadmap · backlog · governance

Data Product strategy, portfolio backlog, PI objectives, MDM, adoption, cost and cross-functional decisions.

03 · TECHNOLOGY & PLATFORM

Architecture · APIs · cloud

Data Engineering, Architecture, Integration, Cloud, DevOps, Security and platform implementation.

04 · VALIDATION & OPERATIONS

System demos · release · continuity

Production readiness, quality, compliance controls, release on demand, service continuity and feedback.

Operating rule: Data/AI services could not scale as isolated projects — portfolio vision, architectural governance and delivery cadence had to operate as one system.

DOCAPOSTE SAFe execution model — **SAFe execution model**Portfolio strategy translated into architectural epics, PI planning, Agile Release Train delivery and cross-functional cadence across Product, Data, Architecture, Cloud, Security and Business.
Portfolio visionArchitectural epicsAgile Release TrainPI PlanningRelease on demand

05 · TECHNICAL STORY

Two trusted journeys.
One sovereign data platform.

The technical story is demonstrated through two distinct service journeys: certified claims evidence and priority health/social alerts, each combining trusted edge capture, secure processing and controlled outcomes.

DOCAPOSTE certified claims photo journey — **Certified Claims Photo Journey**Trusted field capture, integrity verification, qualified timestamping and probative electronic archiving support a defensible insurance evidence chain.
PhotoSHA-256eIDAS timestampCertinomis sealWORM / SAE

DOCAPOSTE health and social alert journey — **Health & Social Alert Journey**Pseudonymised priority events move from protected field capture through Kafka and Flink into immediate alerts and HDS-controlled traceability.
AES-256Kafka priority topicFlink alert engineHDS case recordImmutable audit

06 · DATA PRODUCTS

A sovereign edge-to-clouddata architecture supportingtrusted services.

The platform perspective connects Edge, secure API ingestion, priority streaming, a governed sovereign Data Lake, reusable Data Products and controlled Data/AI consumption.

DOCAPOSTE recommended sovereign data architecture — **Recommended Sovereign Data Architecture**A governed edge-to-cloud architecture spanning source systems, ingestion, transformation, semantic Data Products, operational APIs, analytics, alerts and sovereign AI services.
EdgeKafka · FlinkSovereign Data LakeGoverned Data ProductsNumSpot / HDS

07 · LEADERSHIP INTERVENTIONS

Structure. Govern.Industrialise.

The contribution combined Product leadership, Data Governance and execution discipline to make a regulated Data/AI service portfolio scalable and repeatable.

01 · ALIGN

Connect product, data & technology.

Translate business and partner needs into a shared roadmap spanning Data Hub capabilities, APIs, cloud services and customer-facing experiences.

Roadmap · PI Planning · Platform strategy

02 · GOVERN

Clarify ownership & decisions.

Formalise governance, MDM, interoperability, data quality and technical
decision-making across Business, IT, Data and Security.

MDM · Data quality · Traceability

03 · INDUSTRIALISE

Scale trusted Data/AI services.

Establish repeatable standards for secure delivery, production readiness, documentation, adoption, cost and operational continuity.

Digital Trust · Secure delivery · Operations

08 · PORTFOLIO GOVERNANCE

Scale value without weakening trust.

Platform growth and governance were treated as the same accountability: services could scale only when architecture, data quality, security, compliance and operational readiness remained controlled.

Scale

Reusable platform capabilities

Data Products, APIs, streaming, sovereign cloud and AI services were structured as shared capabilities supporting multiple B2B2C journeys.

Control

Trust and production guardrails

Privacy, security, traceability, data quality, compliance and continuity shaped prioritisation, architectural decisions and release readiness.

09 · EXECUTION EVIDENCE

Governance translated into visible delivery mechanisms.

The case is grounded in roadmap, PI planning, architecture, governance and production-readiness practices. Public presentation remains abstracted to protect regulated, security-sensitive and commercially confidential information.

Portfolio

Roadmap · backlog · PI Planning

Business demand, platform capacity, architectural epics and delivery objectives coordinated through a shared cadence.

Data

Governance · MDM · quality

Reference data, ownership, interoperability, documentation and reliable downstream consumption.

Architecture

APIs · cloud · security

Secure ingestion, streaming, sovereign storage, controlled access and reusable service exposure.

Operations

Release · continuity · adoption

Production readiness, deployment, service reliability, user journeys and operational feedback.

Evidence basis: programme and platform outcomes are presented as collective delivery results reported by the programme lead. Confidential endpoints, control details and customer data remain excluded.

10 · IMPACT

From Data/AI services to a scalable and governed platform model.

The programme contributed to a reported 30% increase in B2B customer acquisition and delivered a 50% increase in CI/CD delivery velocity through production release. Industrialisation of the B2B2C Data Hub strengthened data quality, risk control and accountability across Business, IT, Data and Security, with better management of cross-team dependencies. ML model evaluation and validation supported the governance of trusted Data/AI services.

B2B customer acquisition+30%

Reported increase in B2B customer acquisition, to which the programme contributed.

CI/CD delivery velocity+50%

Reported increase in delivery velocity through production release.

Engagement2019 – 2023

Long-term mandate transitioning to fractional consulting from 2021.

Coordination scale60+ contributors

Product, Data, Technology, Security, Business and partners.

Execution frameworkSAFe · PI Planning

Portfolio vision translated into architectural and delivery execution.

Core platformData Hub

MDM, data governance, APIs, cloud, trusted Data Products and AI services.

Platform

Industrialised Data Hub

A B2B2C Data Hub supporting repeatable Data/AI service delivery and reuse across customer journeys.

Governance

Trusted data foundation

Clearer ownership, MDM, interoperability and data-quality practices.

Services

Validated ML models

ML model evaluation and validation within shared Data Product, analytics and AI service capabilities.

Trust

Controlled risk

Strengthened data-leakage and privacy risk mitigation; supported compliance with GDPR, eIDAS, HDS and ISO/IEC 27001 requirements for applicable services.

Organisation

Clearer accountability

Clarified responsibilities across Business, IT, Data and Security, with stronger alignment on delivery dependencies and validation decisions.

Data Product StrategyAI Services IndustrialisationData Governance & MDMSovereign ArchitectureAPI / Cloud DeliverySAFe · PI PlanningDigital TrustFractional Consulting

11 · OUTCOME & LEADERSHIP VALUE

The business value of governed Data/AI services.

The programme combined the industrialisation of a B2B2C Data Hub with commercial growth, delivery improvements and strengthened governance. The four business priorities below connect the value achieved or enabled with the technology and working practices that supported it.

Business focusStrategic objective	Business outcomesValue delivered or enabled	Technology outcomesCapabilities structured	Behavioural outcomesWorking practices established
Commercial Growth	Contributed to a reported 30% increase in B2B customer acquisition.	Industrialised a B2B2C Data Hub, with shared Data Product and API capabilities supporting the service portfolio.	Aligned Business, Product, Data and Technology around service priorities, partner needs and a shared roadmap.
Operational Efficiency	Increased CI/CD delivery velocity through production release by 50%, with better management of cross-team dependencies.	Structured reusable integration capabilities, data-quality checks and production-readiness controls.	Clarified responsibilities across Business, IT, Data and Security; established shared planning and validation practices through SAFe and PI Planning.
Customer Value	Strengthened data quality and supported trusted customer and public-service journeys.	Structured governed Data Products, secure processing and traceable outputs for claims evidence and health/social alerts.	Connected Business, Product and Operations around service quality, journey validation and continuity requirements.
Risk Management	Strengthened data-leakage and privacy risk mitigation; supported compliance with GDPR, eIDAS, HDS and ISO/IEC 27001 requirements for applicable services.	Evaluated and validated ML models; combined controlled access, traceability, data-quality checks and safeguards against leakage and privacy risks.	Clarified ownership and validation responsibilities across Business, IT, Data and Security, with Legal and Compliance contributing to governance decisions.

Outcome basis: collective programme results reported by the programme lead. The +30% figure refers to B2B customer acquisition; +50% refers to CI/CD delivery velocity through production release.

ROI — Return on Investment: these commercial and operational indicators provide inputs for assessing returns. A financial ROI requires attributable financial benefits, investment and operating costs.

Scope: regulatory and assurance requirements depend on the service. The sovereign data architecture remains a recommendation.

Employee value and future potential

Complementary perspectives on the programme: employee benefits to document and strategic options to validate.

Employee benefits to document

ROE — Return on Employee

Clearer responsibilities, shared priorities and coordinated validation created conditions for more coherent cross-functional working.

Clearer accountabilityDefined responsibilities across Business, IT, Data and Security help teams understand ownership, contributions and validation decisions.
Shared delivery prioritiesA common roadmap, SAFe cadence and PI Planning support coordination around service priorities and cross-team dependencies.
Coordinated validationShared data-quality, model-evaluation and production-readiness practices support collaboration across delivery, Security, Legal and Compliance.

Governance practices were established. Effects on employee autonomy, time spent, workload and experience remain to be documented.

Future options to validate

ROF — Return on the Future

Reusable Data Hub, Data Product and API capabilities create options for further development of the B2B2C service portfolio.

Additional services and use casesShared platform capabilities provide a foundation for new B2B2C offerings and journeys beyond the services already delivered.
Partnerships and capability reuseReusable Data Products and APIs create options for additional partner integrations and service combinations.

The industrialised Data Hub is an achieved platform outcome. Its ROF contribution depends on enabling new offerings; future adoption and economic value require validation.

CASE TAKEAWAY

Data was the foundation. Industrialisation was the mandate.

DOCAPOSTE demonstrates the ability to turn Data/AI services into a governed platform model—aligning Product, Data, Technology, Security and Compliance around reliable B2B2C delivery.

Leadership mandateProgramme leadership · Transformation delivery

Effective scopeHead-of-Product-level Data / IA Product delivery

Engagement modelFractional consulting mandate

Leadership scale60+ contributors coordinated

Core platformData Hub · MDM · APIs · Cloud

Core domainsData · AI · Governance · Digital Trust

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