Information and Intelligence Model
​Information connects the dots, but intelligence reveals the path. Together, they transform data into clarity, action, and enterprise value.
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In this article:
What Is the Information and Intelligence Model?
​Why Is It Important to Develop the Information and Intelligence Model?
What Is the Information and Intelligence Model?
The Information and Intelligence Model is a combined framework that defines the data foundation and analytical intelligence required to enable business capabilities and drive informed decision-making across the enterprise.
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The Information Model describes the logical data entities, their attributes, and relationships that support the execution of Business Capabilities and Business Processes. It ensures that the right data is available, structured, and aligned with enterprise needs.
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The Intelligence Model builds upon the Information Model by introducing analytical insights, metrics, and decision-support logic. It transforms raw data into actionable intelligence through analytics, AI/ML, and business rules, enabling predictive, prescriptive, and real-time recommendations.
Role in the AlignedX Model
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Enables Capability Execution: Provides the data and insights needed for capabilities to function effectively and adaptively.
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Supports the Value Stream: Ensures that each stage in the Value Stream Model is powered by relevant information and enriched with intelligence to optimize performance.
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Drives Informed Decisions: Empowers stakeholders with the insights needed to take proactive, data-driven actions aligned with strategic goals.
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Establishes Data-Insight Continuity: Bridges the gap between structured enterprise data and dynamic analytical insight, supporting continuous improvement and learning.
Figure 7. Information and Intelligence Model Illustration for AlignAir
Why Is It Important to Develop the Information and Intelligence Model?
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The Information and Intelligence Model is essential for turning enterprise data into a strategic asset. It enables organizations to execute business capabilities, optimize value delivery, and make intelligent decisions by combining structured data models with analytical insight.
Key Reasons to Develop the Information and Intelligence Model
Enable Data-Driven Capability Execution
Business Capabilities rely on accurate, timely, and well-structured information. The Information Model ensures that each capability is supported by a consistent and reliable data foundation, while the Intelligence Model enhances execution with real-time insights and predictive recommendations.
Translate Data into Actionable Intelligence
The Intelligence Model transforms raw data into analytics, performance metrics, and decision-support logic, enabling stakeholders to make informed, proactive decisions across the value stream.
Ensure Alignment Between Data, Process, and Strategy
By integrating data models with the Value Stream and Business Process Models, the Information and Intelligence Model ensures that data flows and insights are fully aligned with strategic goals and operational workflows.
Establish a Common Semantic Framework
The Information Model defines standard data entities, attributes, and relationships, creating a shared vocabulary across business and IT. This consistency supports integration, governance, and cross-functional collaboration.
Accelerate Digital Transformation and Automation
AI, machine learning, and automation depend on structured data and contextual insight. The model provides the blueprint for building intelligent systems that enhance responsiveness, efficiency, and innovation.
Support Governance, Compliance, and Trust
A clearly defined Information Model supports data quality, lineage, security, and compliance, while the Intelligence Model ensures that insights are explainable, auditable, and aligned with ethical and regulatory standards.
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How To Develop the Information and Intelligence Model
The Information and Intelligence Model is developed in two integrated layers:
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The Information Model defines the logical data entities and their relationships that support Business Capabilities and operational workflows.
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The Intelligence Model enriches this data structure with analytical insights, metrics, and decision-support logic to drive intelligent, data-informed actions.
Development Steps
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Anchor to Business Capabilities and Processes
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Start by identifying the Business Capabilities and Business Processes that the model needs to support.
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For each capability and process, determine the key information needs, such as customer profiles, transactions, assets, or events.
Define Logical Data Entities
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Identify and name the core data entities (e.g., Customer, Product, Order, Asset).
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For each entity, define:
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Attributes (e.g., Name, Status, Date)
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Primary keys and unique identifiers
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Relationships (e.g., one-to-many, many-to-many)
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Map Relationships and Hierarchies
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Use entity-relationship diagrams (ERDs) or logical data modeling tools to visualize how data entities relate to one another.
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Include:
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Hierarchies (e.g., Product → Category)
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Reference data (e.g., Region, Currency)
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Linking entities (e.g., Customer Order linking Customer and Order)
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Align with Value Stream and Process Models
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Ensure that each Value Stream Stage and Business Process has access to the data entities it needs to perform and create value.
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Validate that the flow of information aligns with the flow of value across the organization.
Define Analytical Metrics and Insights
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For the Intelligence Model, identify:
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Performance metrics (e.g., average handle time, on-time delivery rate)
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Behavioral patterns (e.g., churn risk, buying trends)
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Decision rules (e.g., if satisfaction < 80%, trigger escalation)
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Predictive or prescriptive models (e.g., fraud detection, recommendation engines)
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Associate Insights with Capabilities and Value Stages
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Link analytical insights to the Value Stream Stages or Business Capabilities where they will be applied.
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Define how insights influence decision-making, automation, or personalization at each point in the value stream.
Establish Data Governance and Quality Rules
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Define data ownership, stewardship, and access controls for key entities.
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Ensure that the model accounts for data quality, integrity, and compliance requirements.
Document and Communicate the Model
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Use models, glossaries, and documentation to create a shared understanding among business and IT stakeholders.
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Integrate the model with enterprise architecture, solution design, and analytics initiatives.
Best Practices
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Keep the Information Model technology-agnostic at the logical level; physical implementation (e.g., database design) can follow.
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Design with reuse in mind: Ensure entities and insights can support multiple capabilities and processes.
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Update iteratively: Evolve the model as business needs, technologies, and analytics evolve.