⚡ Kraken-Style Platform Improvement View
Make data ownership explicit • Treat time properly • Prove every outcome
🏗️ Reference Architecture (Simplified)
Channels
App
Agent
API
App
Agent
API
Platform
Customer
Billing
Tariffs
Meter Data
Customer
Billing
Tariffs
Meter Data
Ownership Layer
Customer
Account
Meter Point
Tariff
Supplier
Time
Customer
Account
Meter Point
Tariff
Supplier
Time
Data Platform
Bronze
Silver
Gold
Star Schemas
Bronze
Silver
Gold
Star Schemas
Control
GDPR
Masking
RBAC
Audit
GDPR
Masking
RBAC
Audit
➡️ Data flows left to right, ownership and control flow across everything
📌 Ownership
Make it explicit, not implied.
Star schemas define:
who owns what, when.
Make it explicit, not implied.
Star schemas define:
who owns what, when.
⏱️ Time
Effective dates everywhere.
Tariffs, usage, billing aligned.
No “current state only”.
Effective dates everywhere.
Tariffs, usage, billing aligned.
No “current state only”.
🔍 Prove It
Run IDs, rule versions,
traceable pipelines.
Rebuild any outcome.
Run IDs, rule versions,
traceable pipelines.
Rebuild any outcome.
⭐ Ownership Data Model (Core Idea)
Fact_Usage
- Meter Point
- Customer
- Tariff
- Supplier
- Time
- Units
Fact_Billing
- Account
- Charge
- Tariff
- Period
- Amount
Dimensions:
Customer | Account | Meter | Tariff | Supplier | Time
👉 Every row answers: who, what, when, under which rules
🚀 Practical Improvements
- Introduce explicit ownership layer (star schema driven)
- Standardise time handling (valid-from / valid-to everywhere)
- Add traceability layer (run ID + rule version)
- Treat pipelines as reusable data products
- Embed GDPR masking into ingestion (not after)
🎯 Bottom Line
Not redesigning the platform — tightening control.
Define it → Run it → Prove it
Not redesigning the platform — tightening control.
Define it → Run it → Prove it
The Practitioner’s View
⚡ How I’d Improve a Kraken-Style Platform
Data ownership made explicit • Time treated properly • Outcomes that can be proved
Not a grand redesign. More a careful tightening of the machine. The aim is to keep the platform quick on its feet,
but clearer about who owns what, what was true when, and how any outcome can be explained under pressure.
🏛️ Platform as a Governed Engine
🚪
Channels
App
Agent
Partner API
Comms
Agent
Partner API
Comms
⚙️
Core Services
Customer
Billing
Tariffs
Meter Data
Billing
Tariffs
Meter Data
⭐
Ownership Layer
Customer
Account
Meter Point
Tariff
Supplier
Time
Account
Meter Point
Tariff
Supplier
Time
🏺
Data Platform
Bronze
Silver
Gold
Star Schemas
Silver
Gold
Star Schemas
🛡️
Control
GDPR
Masking
RBAC
Audit
Masking
RBAC
Audit
⟶ ⟶ ⟶
Data flows across the estate. Ownership and control must flow with it.
📌 Ownership
Make it explicit, not implied in code. A star-schema ownership layer tells you
who owns the customer, meter, tariff, and usage at a point in time.
⏳ Time
Most trouble comes from dates, not drama. Tariffs switch, readings arrive late,
agreements overlap. So time needs proper modelling, not hopeful assumptions.
🔎 Prove It
Give every outcome a trail: source event, rule version, transformation path,
reconciliation marker. In short: don’t just calculate it — be able to defend it.
⭐ Core Ownership Model
Fact Tables
Fact Usage
Meter Point • Customer • Tariff • Supplier • Time • Units
Fact Billing
Account • Charge • Tariff • Period • Amount
Meter Point • Customer • Tariff • Supplier • Time • Units
Fact Billing
Account • Charge • Tariff • Period • Amount
Dimensions
Customer • Account • Meter • Tariff • Supplier • Time
Each row should answer:
Each row should answer:
Who does this belong to, what is it, when was it true, and under which rules?
🛠️ Practical Improvements
• Add an explicit ownership layer
• Standardise valid-from / valid-to handling
• Introduce run IDs and rule-version traceability
• Treat pipelines as reusable data products
• Embed masking into ingestion, not as an afterthought
• Standardise valid-from / valid-to handling
• Introduce run IDs and rule-version traceability
• Treat pipelines as reusable data products
• Embed masking into ingestion, not as an afterthought
🎯 Why It Matters
• Fewer billing disputes
• Cleaner onboarding of new portfolios
• Better regulatory and audit posture
• Faster diagnosis when something goes wrong
• Stronger customer trust because the data behaves
• Cleaner onboarding of new portfolios
• Better regulatory and audit posture
• Faster diagnosis when something goes wrong
• Stronger customer trust because the data behaves
Bottom Line
I would not try to reinvent the platform. I would make the architecture clearer about ownership,
stricter about time, and easier to defend when challenged.
Define it properly → Run it repeatably → Prove the outcome.
Define it properly → Run it repeatably → Prove the outcome.
🧭 ✦ ⚙️
Revenue Lifecycle Framework
From Product Idea → to Cash → to Accountable Truth
DEFINE
Products & Services
Products & Services
➡️
SELL
Contracts & Customers
Contracts & Customers
➡️
PROVISION
Fulfilment & Activation
Fulfilment & Activation
➡️
USE
Events & Consumption
Events & Consumption
➡️
RATE
Charges Calculated
Charges Calculated
➡️
BILL
Invoices Raised
Invoices Raised
➡️
PAY
Payments Made
Payments Made
➡️
COLLECT
Debt & Settlement
Debt & Settlement
➡️
ACCOUNT
Ledger & Truth
Ledger & Truth
📦 Data Stores (State at Each Step)
Products & Services
Customers / Contracts
Fulfilled Contracts
Usage Events
Charges
Invoices
Payments
Debt / Collections
General Ledger
✦ Guiding Principle
Every step represents a controlled state change.
Every state is captured as data.
Every movement can be traced, reconciled, and proven.
Every step represents a controlled state change.
Every state is captured as data.
Every movement can be traced, reconciled, and proven.
“Move Rows, Not Opinions.”