Data Manufacturing Mapped to Common Enterprise Operating Models
 

Data Manufacturing is not a department.

 

It is an operating capability that sits between insight and execution—designed to help

 

enterprise data investments consistently produce measurable business outcomes.

 

The Core Insight

 

Data Manufacturing integrates across operating models rather than replacing them. It strengthens handoffs,

 

improves alignment, and closes the execution gap between analysis and production.

 

 

1) Functional Operating Model

 

Traditional enterprise model with siloed functions.

 

Where Data Manufacturing Fits
 

Across functions, anchored to execution leadership.

This prevents insights from stalling in handoffs between teams.

 

Typical ownership: COO or Transformation Office

 

Primary purpose: Converts insight into coordinated execution across functions

 

2) Matrix Operating Model

 

Shared services + business units with distributed P&L ownership.

 

Where Data Manufacturing Fits

 

As a horizontal execution layer across business units. Ensures analytics becomes scalable

 

outcomes rather than isolated wins.

 

Typical ownership: Enterprise Transformation, COO, or joint BU leadership

 

Primary purpose: Scales value realization across business units

 

 

3) Product-Centric Operating Model

 

Digital-first organizations with product squads and agile delivery.

 

Where Data Manufacturing Fits

 

Above product teams, below strategy. Aligns delivery velocity with measurable economic impact.

 

Typical ownership: COO, Chief Product Officer, or Growth Office

 

Primary purpose: Ensures feature delivery results in outcomes (not just velocity)

 

 

4) Platform Operating Model

 

 

Shared data/AI/cloud platforms serving internal business “customers.”

 

Where Data Manufacturing Fits

 

As the demand-to-outcome translator. Converts platform capabilities into realized business value.

 

Typical ownership: Chief Transformation Officer or Enterprise Enablement

 

 

Primary purpose: Turns platform investment into measurable value realization

 

 

5) Transformation / PMO-Led Model

 

Change-heavy enterprises led by strategy offices, PMOs, and partner support.

 

 

Where Data Manufacturing Fits

 

As the execution accountability layer. Shifts emphasis from milestones and deliverables to

 

measurable production and outcomes.

 

 

Typical ownership: Transformation Office or COO

 

Primary purpose: Moves from milestone tracking to output tracking

 

 

 

6) Federated / Hybrid Model

 

Most modern enterprises: multiple models operating simultaneously.

 

 

Where Data Manufacturing Fits

 

As connective tissue. Respects autonomy, reinforces outcome discipline, aligns incentives,

 

and closes execution gaps across teams and business units.

 

Typical ownership: Executive sponsor (COO / CFO / Chief Transformation Officer)

 

Primary purpose: Aligns execution across a mixed operating model environment

 

 

Governance & Accountability (Enterprise-Safe)

 

Data Manufacturing is designed to integrate within enterprise governance. It does not replace existing teams

 

or control data custody. It focuses on outcomes and execution alignment.

 

  • Does not override governance

  • Does not own data custody

  • Does not replace internal teams

  • Does own outcome definition, execution sequencing, and measurement against results

 

 

Executive Summary

 

 

 

Data Manufacturing is the operating capability that ensures enterprise data investments

 

consistently produce measurable business outcomes—regardless of operating model.

 

  • Data Engineering = data readiness

  • Analytics = data understanding

  • Consulting = decision support

  • Data Manufacturing = outcome realization