MetaDynamic / SAP-connected retail / Data platform & governance
SAP-connected data.
Governed for decisions.
A dedicated data workstream to bring ERP, POS and CRM information into a governed operating model, with shared business definitions, controlled access and accountable data-quality decisions.
The business context / 04
Shared reporting needs shared definitions.
ERP, CRM, POS and commerce data are described as disconnected in the programme brief. Without agreed identifiers and ownership, teams can produce different versions of customer, inventory and finance information from the same business activity.
The roadmap also addresses uncontrolled sharing of customer and sales data. A central platform must therefore combine useful access with privacy, least privilege, data-loss controls and a practical governance model.
Name the data owner
Assign responsibility for customer, product, location and other agreed domains. Separate ownership from technical storage administration.
Create trusted records
Define matching, quality, correction and approval rules before exposing a shared dataset as a business truth.
Control use and sharing
Apply role-based access, appropriate masking and approved sharing routes, supported by reviewable policies and audit evidence.
The technical model
From source record to governed use
Recommended logical layers. The physical platform and tools remain subject to scope, security, residency and operating requirements.
SAP ERP records
Finance and inventory context
POS & service records
Store transaction context
CRM & digital records
Customer interaction context
Governed data platform
Ingest · Validate · Curate
Master data domains
Approved identifiers and correction rules
Business-ready datasets
Documented definitions and lineage
Authorized consumption
Reporting, applications and approved AI use
Detailed workstream scope / 04
Build the data layers and the stewardship together.
The plan combines a centralized cloud data platform with master-data management, access policy and data-quality ownership. The logical layers below describe intended responsibilities rather than a selected physical platform.
Raw and validated layers
Preserve controlled source ingestion and distinguish original records from standardized data. The roadmap’s Bronze/Silver stages provide a useful way to organize this separation, with lineage and reconciliation checks.
Curated business data
Progress toward a Gold layer of approved business datasets and shared metric definitions. Master-data management establishes governed customer, product and location identities with accountable correction rules.
Role-based access and DLP
Design identity and access management alongside the platform. Classify sensitive information, approve sharing routes and review data-loss prevention policies against actual business usage.
Customer-data platform decision
Evaluate whether existing CRM plus a governed data platform can meet customer insight and activation needs before adding a separate CDP. Assess identity resolution, integration complexity, adoption and ongoing operating cost.
Proposed delivery and operating responsibilities
| Data owners and stewards | Approve definitions, resolve quality issues and authorize use of each data domain. |
|---|---|
| Data engineering | Own ingestion, transformation, lineage, refresh operations and source reconciliation. |
| Security and business consumers | Validate role access, retention, permitted sharing and interpretation of approved datasets. |
Proposed delivery sequence
Build governance alongside the platform
Discover and classify
Inventory sources, sensitive fields, duplicate records and current sharing routes. Agree priority data domains.
Gate: data inventory and accountable owners.Define the trust rules
Agree identifiers, quality checks, correction responsibilities, access roles and retention requirements.
Gate: business-approved governance standards.Deliver a governed pilot
Ingest selected sources into controlled layers. Reconcile a priority dataset with source owners and test authorized access.
Gate: quality, lineage and access acceptance.Extend with operational discipline
Expand domains and consumers. Monitor quality, review permissions and manage changes to shared definitions.
Gate: recurring stewardship and support ownership.
Scope & success measures
Define success before
the rollout begins.
These measures define what a proposed pilot should demonstrate. They are acceptance criteria, not reported client results.
| Data quality | Track completeness, validity and duplicates for agreed domains against a measured baseline. |
|---|---|
| Traceability | Document where a dataset originates and how it is transformed. |
| Access control | Test authorized and unauthorized use for representative roles. |
| Stewardship | Review unresolved data issues, correction lead times and ownership coverage. |
This workstream covers the data platform and governance roadmap. Platform selection, master-data rollout and control acceptance are separate delivery milestones.
Practical questions
Details that matter.
Does a data lake automatically create a single source of truth?
No. Trusted information also depends on shared definitions, data quality, ownership, lineage and controlled access.
Is a dedicated customer data platform required first?
The programme scenarios evaluate using existing CRM and a governed data platform before adding a dedicated CDP. Selection should follow proven needs for identity resolution, activation and customer journeys.
Can these datasets support AI later?
They can provide a better foundation when quality, permissions and usage boundaries are established. AI access still requires its own security, privacy and validation controls.
Work with MetaDynamic
Define your enterprise data foundation.
Share your operating priorities, application landscape and delivery constraints. Start with a focused scope and a practical validation plan.
