Data Carve-Outs — From Narrative to Structure

A field experience at a Utility Firm showing how structure turns one-off data extraction into a repeatable, defensible system.

🧭 Context

Multiple phased carve-outs were delivered across the business. Each phase built on the last:
• Phase 1 → initial carve out business dataset 01
• Phase 2 → second carve out business dataset 02
• Phase 3 → third carve out business dataset 03

⚠️ The Problem

Each phase subtly redefined what “in scope” meant:
• Rules drifted
• Edge cases accumulated
• Reconciliation became harder

We weren’t repeating a process — we were redefining the problem each time.

🔍 Diagnosis

The carve-out existed as a narrative, not a structure.

• No fixed grain (what is a record)
• No stable axes (time, product, ownership)
• No reusable rule set

🛠️ Intervention

To make the concept land with an audience—largely of management accountants—I used a simple library analogy: splitting books by university faculty. It anchored the idea in something familiar, allowing the structure to be understood clearly without relying on technical jargon.

“Splitting books in a library”

• Define shelves → dimensions (time, product, entity)
• Define a book → grain
• Define allocation → rules

Not a platform. Just a consistent way of deciding what belongs.

🧱 Pushback

The steer from solution architecture was that this had been looked at and discounted on cost grounds — “we don’t have the budget to host a star schema, we just need to do the carve-out.” To me, that reflected a conceptual gap. Cost felt more like a gut feel judgement wrapped in technical language, and structure was being treated as something you host rather than something you define once and reuse.

Translation:
• Structure was seen as infrastructure
• Not as a reusable definition

💡 Core Insight

Without structure, a carve-out is a story.
With structure, it becomes a system.

📉 Outcome

• Increasing reconciliation effort
• Ambiguity across phases
• Manual correction and rework

Cost wasn’t infrastructure — it was delivery friction.

✅ Better Way

Define once, reuse many times:

• Grain → what is a record
• Dimensions → axes of selection
• Rules → inclusion boundaries

Implement as:
• views
• mapping tables
• lightweight staging logic

Delivery. Data. Done Properly.

Define the shape once. Execute it many times.