Information and Intelligence Model

Information connects the dots, but intelligence reveals the path. Together, they transform data into clarity, action, and enterprise value.

In this article:

What Is the Information and Intelligence Model?

The Information and Intelligence Model defines how data is structured, governed, and augmented to support value delivery during transformation.

It introduces the following core constructs:

Data Domain
A logical grouping of related data entities that represents a major business information area (e.g., Customer, Product, Finance).
Data Entity
A canonical representation of a business object such as Customer, Order, Invoice, or Account. Data entities represent what information the organization manages, independent of systems.
AI Augmentation
An extension of a data entity that represents how artificial intelligence is used to enrich, interpret, or derive additional value from that data.

Together, these constructs provide a stable information foundation while enabling modern, AI-enabled transformation.

Why Is It Important to Develop the Information and Intelligence Model?

Organizations often struggle with information management during transformation because:

This leads to:

The Information Model addresses these challenges by:

separating business information from application implementation,
establishing a common data language across initiatives,
positioning AI as an augmentation of data, not a replacement for sound data foundations.

This ensures that data and AI investments remain intentional, governed, and value-aligned.

The Information Model is applied by defining stable data foundations and selectively augmenting them to support transformation goals.

1. Define Data Domains

At the enterprise level:

identify Data Domains that represent major information areas,
use domains to group related data entities and clarify ownership and governance boundaries.
Data domains provide structure without introducing unnecessary rigidity.

2. Define Foundational Data Entities

Within each data domain:

These entities serve as canonical references across all transformation initiatives.

3. Augment Data with AI Capabilities

For data entities where additional insight or automation is valuable:

define AI Augmentations as extensions of the data entity,
specify how AI enhances the data (e.g., classification, prediction, enrichment, summarization),
treat AI augmentations as optional and additive, not mandatory.

AI augmentation enables organizations to extract more value from existing data while maintaining transparency and control.

4. Use Information to Enable Transformation

Defined data entities and AI augmentations:

By grounding transformation in shared information constructs, organizations avoid fragmented and redundant data solutions.

Alignment and Relationships

The Information Model integrates with other AlignedX models through explicit relationships:

A Data Domain is composed of one or more Data Entities.
A Data Entity belongs to a single Data Domain.
A Data Entity may have one or more AI Augmentations.
Transformation L2 Business Capabilities use Data Entities to deliver value.
Application Services manage or consume Data Entities.
AI Augmentations enhance data used across experiences, processes, and decision-making.

These relationships ensure that data remains a shared enterprise asset, even as it is leveraged differently across workstreams.

Summary

The Information Model provides a stable, business-centric foundation for data and AI in transformation.

By:

the model ensures that data and AI investments are aligned, reusable, and value-driven, rather than fragmented or opportunistic.