LetsChangeTogether

What distinguishes this model

1. 1

   Inform layer as single source of technical truth

   A persistent, time-consistent technical state layer before any financial interpretation — preventing competing cost truths. The starting point is operational reality, not billing exports.

2. 2

   Separation of observation, interpretation, and decision

   Clear Inform → Optimize → Govern/Execute phases. Observation, valuation, and steering stay apart — audit-friendly, without collapsing into tool-driven direct optimization.

3. 3

   Multi-perspective model without conflict

   Engineering, product, finance, procurement, and leadership read the same technical reality with different aggregation logic — one data foundation, no competing truths.

4. 4

   Remanence costs & hybrid reality

   Explicit modeling of long hybrid phases: cloud migration can raise costs short term; on-prem and cloud logic must remain jointly steerable — not simplified through cloud-only framing.

5. 5

   Ex-post steering, not ex-ante business cases alone

   Economic value of platform decisions materializes in operation. Systematic feedback from actual cost and technical state — not investment approval before go-live alone.

6. 6

   Role clarity including negative scope

   Defines what each perspective owns — and explicitly what it does not. Reduces trench warfare between engineering, product, and finance in practice.

7. 7

   Governance- and regulation-ready

   Built for auditability, traceability, and integration into existing committee and controlling structures — not an operational, tools-heavy playbook for regulated environments.

What distinguishes this model

1. 1

   Phased, risk-minimized rollout

   Crawl – walk – run instead of big-bang: fast visible value, controlled scale, and durable embedding in organization and architecture.

2. 2

   Federated governance (hub & spoke)

   Central policy guardrails and standards with decentralized execution — divisions stay agile, enterprise-wide steering becomes possible.

3. 3

   Data architecture before tools

   Process before tool: consolidated data foundation, showback and chargeback on a sound architecture — not tool-driven silos.

4. 4

   Service-level TCO for decisions

   Pilot-based service-level TCO models for comparable cost truth and sound make-or-buy and architecture choices.

5. 5

   Political feasibility in decentralized units

   Roadmap with clear milestones, unit economics, and commitment strategies — division acceptance is part of the design.

What distinguishes this model

1. 1

   Inform layer, not cost visibility alone

   Reconstruction of a time-stable technical state from source systems — not billing alone, but facts on storage tiers, DICOM lifecycles, and compute.

2. 2

   Clinical data flows end-to-end

   Imaging → PACS → HL7/FHIR → SAP IS-H → storage tiers (hot/cool/archive) and AI analysis — FinOps follows actual usage behavior.

3. 3

   Multi-perspective without competing truths

   Engineering, radiology/clinical, controlling, and procurement read the same technical reality — with fitting aggregation and interpretation.

4. 4

   Regulation and SAP coupling

   Long retention periods, clinical cost justification, and tight SAP process integration are part of the model — not a late special case.

5. 5

   Steering, not a cost-cutting program

   Sound decisions on architecture, storage strategy, and cloud services on a reliable data basis — within clinical and regulatory constraints.

What distinguishes this model

1. 1

   Delivery ≠ operations

   CI/CD delivers reproducibility — application operations owns runtime, observation, and intervention decisions. Clear separation reduces responsibility diffusion.

2. 2

   Observability as control system

   Not passive monitoring: reconstruct system state and make steering decisions from technical, application, and business-specific signals.

3. 3

   Separation of duties at platform level

   Kubernetes interfaces (namespaces, RBAC, policy controllers) enable technical responsibility boundaries — uniform pipeline, different risk profile.

4. 4

   Signal interplay, not single metrics

   In market-critical systems, temporal correlation of multiple signals matters — whether deviations are technical, business, or regulatory in nature.

5. 5

   Risk containment over automation alone

   Practical escalation on config changes: operational judgment for rollback, scale-up, or intervention — not pipeline logs alone.