Operationalizing Information Architecture: The Power of Ontology-First Design

I was inspired to write this series of articles by Andrea Gioia 's article "Operationalizing the Information Architecture."

As organizations continue to deal with increasingly complex data landscapes, they face a critical challenge: how to operationalize information architecture to guarantee data consistency, semantic clarity, and integration across diverse and distributed systems. While methods like Data-Centric Solutions, Knowledge Warehouses, and Ontology-Based Data Access offer valuable frameworks for structuring and managing data, each has limitations when used in isolation, often falling short of addressing an enterprise's broader, interconnected needs.

The challenge lies in managing the data and establishing a framework that governs how data, knowledge, and workflows interact across multiple systems and departments. A superordinate framework that moves beyond managing data alone and focuses on semantic precision and contextual alignment is required to meet this need. This is where the Ontology-First approach offers a transformative solution. Grounded in ontological frameworks like Basic Formal Ontology (BFO 2020), this approach unifies the data plane and knowledge plane across an enterprise, ensuring a deeper integration of business concepts with operational and analytical data.

The Ontology-First approach treats ontology as the core structure that defines not just how data is organized but also how it is semantically understood across the enterprise. By doing so, it enables organizations to overcome the challenges of data fragmentation and inconsistency, fostering an environment where data and knowledge work in tandem. This unified structure powers next-generation hyper-automation systems, wherein processes are automated and enriched by a shared, semantic understanding of the data involved. This allows enterprises to achieve data consistency and knowledge integration at scale, unlocking the full potential of their information architecture.

This paper will explore how ontology can serve as the guiding framework within the three prominent information architecture models: Data-Centric Solutions, Knowledge Warehouses, and Ontology-Based Data Access. I will demonstrate how an Ontology-First approach offers a more scalable, semantically transparent, and integrated solution that supports advanced automation and knowledge-driven decision-making across complex enterprise systems.

What is Ontology-First?

Ontology-first refers to a development methodology in which formal ontologies serve as the guiding structure for designing, developing, and integrating software systems. Unlike traditional approaches that prioritize data structures or functional requirements, ontology first positions ontologies as the primary organizing principle for all interactions within a system. It draws heavily from Basic Formal Ontology (BFO 2020), a rigorous upper-level ontology that provides a unified, domain-agnostic framework for organizing data.

Our approach builds upon the concepts laid out by Jeff Z. Pan, Steffen Staab, Uwe A?mann, Jürgen Ebert, and Yuting Zhao in their influential book Ontology-Driven Software Development (2013). They explore how ontologies can centralize and formalize system design, providing an organizing structure that enhances software systems' coherence, scalability, and extensibility.

In an Ontology-First approach, the ontology orchestrates all interactions between data, knowledge, and applications from the beginning, ensuring that all system components align with a unified semantic framework. While Andrea Gioia's models offer flexibility by applying ontology in different contexts, Ontology-First emphasizes the consistency and scalability that arise from positioning ontology at the very core of the architecture. This leads to more cohesive, interoperable systems where data is not only well-organized but also richly contextualized.

How Does Ontology-First Differ from Traditional Approaches?

In traditional software development, systems are often built around specific data structures or functional requirements, treating ontology as an optional layer that may be introduced later. In contrast, an Ontology-First approach places the formal ontology—a conceptual model of entities and relationships—at the very foundation of the development process.

By integrating ontology from the outset, an Ontology-First approach ensures that all system components, from data to applications, are aligned with a single conceptual model. This alignment fosters systems that are scalable, interoperable, and semantically precise, avoiding the pitfalls of fragmented or inconsistent data models.

For readers new to these ontological principles, Building Ontologies with Basic Formal For readers new to these ontological principles, Building Ontologies with Basic Formal Ontology by Robert Arp, Barry Smith, and Andrew Spear (2015) offers a comprehensive guide to how BFO structures entities into two main categories:

• Continuants: Entities that persist through time (e.g., objects or organizations).
• Occurrents: Entities that happen or unfold over time (e.g., events or processes).

This structured division is essential for building systems that not only organize data but also impart semantic meaning, enabling automation and decision-making at scale.

How Ontology-First Aligns with Abby Coverts Insights

The advantages of an Ontology-First approach are further underscored by aligning these principles with Abby Covert 's insights from her book How to Make Sense of Any Mess. Covert emphasizes the critical role of structure and context in transforming raw data into meaningful information—a perspective that aligns closely with BFO 2020’s ability to formalize relationships between data points.

Covert’s focus on clear information architecture, which turns fragmented and often meaningless data into actionable information, mirrors the goals of Ontology-First design. By ensuring that data is contextually grounded within a formal ontology, we unlock semantic clarity, streamline hyper-automation, and create a unified information framework that allows organizations to move beyond fragmented data silos toward integrated, actionable knowledge.

Key Takeaways:

1. Ontology-First design centralizes formal ontologies as the primary structure in system development, ensuring consistent alignment between data, knowledge, and workflows.
2. Unlike traditional approaches, where ontology might be an add-on, Ontology-First embeds ontology from the beginning, providing scalability, interoperability, and semantic richness.
3. Basic Formal Ontology (BFO 2020) is the foundational framework that structures data into continuants and occurrents, essential for maintaining a coherent data architecture.
4. Ontology-First principles align closely with Abby Covert’s focus on information architecture, which transforms raw data into meaningful information through context and structure. This supports hyper-automation and enhances decision-making processes.

Information is Not Data: Abby Covert’s Insight in an Ontological Context

In her work, Abby Covert draws a critical distinction between data and information, emphasizing that data in its raw form—disconnected facts and figures—lacks inherent meaning or value. It is only when data is organized, contextualized, and imbued with meaning that it transforms into information, which becomes actionable and valuable to organizations. This transformation is central to the discipline of information architecture, whose goal is to provide structure and meaning to otherwise fragmented data points.

In the context of an ontological framework, particularly one like Basic Formal Ontology (BFO 2020), this transformation is not only a philosophical or theoretical concept; it becomes a formalized and structured process. BFO 2020 provides the semantic backbone that defines entities, relationships, and processes within a specific domain, organizing raw data into a meaningful structure. This structured approach enables organizations to unlock the true potential of their data, converting it into information that can drive decision-making, automation, and advanced analytics.

BFO 2020’s Role in the Data-to-Information Transformation

Building on Covert’s insights, BFO 2020 is a foundational framework that embeds data into a semantic structure, providing the context and meaning necessary to transform it into information. This structure establishes relationships between previously disconnected data points, enabling organizations to derive actionable insights and make informed decisions.

For example, consider Covert’s description of raw data points—such as property prices, buyer credit scores, or loan interest rates. On their own, these data points lack meaningful value and remain disconnected without a structured framework. They offer little insight into business processes or decision-making. However, when these raw data points are embedded within a BFO 2020-based ontology, they become interconnected within a broader semantic structure, meaningfully defining their relationships and interactions.

BFO 2020 provides the context that makes these relationships explicit and enables systems to automate processes, infer relationships, and drive decisions. By establishing rules and relationships within the ontology, BFO 2020 allows automation systems to infer complex outcomes, such as loan eligibility or property affordability, based on the interaction of multiple data points.

Example: Real Estate Transaction

To illustrate the power of ontology in transforming raw data into actionable information, let’s consider the following real estate transaction:

Raw Data Points:

- Buyer’s credit score: 720

- Property price: $350,000

- Buyer’s annual income: $85,000

- Loan interest rate: 3.5%

With Ontology (BFO 2020):

When these data points are embedded within a BFO 2020-based ontology, their relationships are formalized, enabling a more meaningful analysis:

• The buyer’s credit score (720) is mapped to loan qualification criteria within the ontology, allowing the system to automatically determine whether this score meets the eligibility requirements for a specific loan.
• The property price ($350,000) is linked to market data and contextualized by the buyer’s annual income ($85,000) through a debt-to-income ratio, enabling an affordability assessment.
• The loan interest rate (3.5%) is tied to broader economic trends within the ontology, allowing for predictions of future financial obligations or adjustments based on evolving market conditions.

In this scenario, the ontology enables automation systems to assess the interconnectedness of these data points and provide actionable insights. For instance, the system can evaluate whether the buyer qualifies for a loan or whether the property is affordable, transforming the fragmented data into meaningful information that drives decision-making. Turning raw data into actionable insights demonstrates the power of ontological frameworks like BFO 2020 to support automation and informed business processes.

Ontology as a Bridge Between Data and Information

Abby Covert describes the transformation from data to information, which is aligned with the role of ontologies such as BFO 2020 in formalizing relationships between data points. This alignment creates a semantic foundation for hyper-automation, enabling organizations to move from fragmented data systems to cohesive, integrated information frameworks.

Using ontology as the foundation allows organizations to organize data and enrich it with meaning and context, enabling more sophisticated decision-making processes and scalable automation. As we discussed earlier, the Ontology-First approach ensures that all data is situated within a structured context, facilitating its conversion into information. This approach maximizes the value of data across the enterprise by embedding it in a framework that supports continuous and automated decision-making processes.

Key Takeaways:

1. Data without context is not information: Abby Covert’s insights reinforce that raw data, while necessary, lacks inherent meaning and value until it is contextualized and structured into information.
2. BFO 2020 provides the framework: By embedding data into a BFO 2020-based ontology, organizations can create relationships between data points, transforming them into actionable insights that drive business processes.
3. Automation and decision-making: Ontologies enable hyper-automation by structuring data in ways that allow systems to automatically make inferences and decisions, reducing the need for manual intervention.
4. Ontology-First design: This approach ensures that data is always contextualized and organized within a broader framework, enabling scalability and meaningful automation in complex enterprise environments.

Covert’s and BFO 2020’s Convergence on Information Clarity

One of Abby Covert 's core principles is that data becomes valuable when transformed into information by applying clarity and context. In its raw form, data is merely a collection of isolated facts with little inherent value. Information, in contrast, is structured, contextualized, and actionable. This distinction is fundamental to information architecture, a field in which Covert has made significant contributions by stressing the importance of organizing and adding meaning to disconnected data points.

The Basic Formal Ontology (BFO) 2020 framework supports this transformation by adding a semantic layer that defines entities and relationships, making it possible to derive actionable information from raw data. Within an Ontology-First approach, this semantic clarity extends beyond organizing data—it enables seamless hyper-automation. When relationships between data points are clearly defined in an ontology, systems can autonomously make decisions (e.g., loan approvals or transaction processing) based on structured, contextualized information rather than on fragmented raw data.

Integrating Abby Covert’s Insights into Andrea Gioia’s Proposition

Andrea Gioia advocates leveraging ontology to bring semantic clarity and consistency to enterprise systems. While Andrea's models—such as Ontology-Based Data Access and Data-Centric Solutions—allow flexibility in how ontology is applied depending on the operational context, Abby Covert’s distinction between data and information provides valuable depth to this perspective. Covert emphasizes the critical importance of contextualizing data through an ontological framework, aligning with the goals of Gioia's models.

In Andrea's models, ontology plays a role beyond merely storing or processing data; it contextualizes each data point within a coherent informational framework. This ontological framework enables the transformation of raw data into actionable information, powering decision-making and automation across the enterprise. Covert's principles reinforce this view by emphasizing that ontology provides the structure necessary to turn disconnected data into meaningful and usable information.

How BFO 2020 Contextualizes Data into Information

Building on Covert’s insights, BFO 2020 is a critical ontological framework in Andrea Gioia's models. Although Andrea supports a flexible, context-sensitive approach to how ontology and data interact, BFO 2020 ensures that the transformation from data to information is consistent, structured, and actionable. In an Ontology-First system, BFO 2020 plays several crucial roles:

1. Add a Semantic Layer Over Raw Data

Andrea highlights how adding a semantic layer to data-centric and knowledge-based architectures helps define relationships between data points. Although Andrea allows for flexibility in implementing ontology, he acknowledges that ontology is key to preventing data fragmentation. This aligns with Covert’s observation that, without context, data remains isolated and disjointed. BFO 2020 provides the formal structure that connects these data points, ensuring they contribute to a meaningful whole.

2. Transform Data into Information

Andrea and Abby emphasize that raw data—such as buyer profiles, credit scores, or property prices—remains unstructured and of limited utility without contextualization through ontological relationships. BFO 2020 provides this structure by defining formal relationships between data points, transforming them into actionable information supporting automated processes and decision-making.

3. Support Hyper-Automation

Hyper-Automation and Scalability: Gartner defines hyper-automation as the use of advanced technologies—such as artificial intelligence (AI) and machine learning (ML)—to automate processes that previously required human intervention. By embedding ontological principles into systems, organizations can fully support hyper-automation, enabling the end-to-end automation of complex tasks while ensuring scalability and consistency across their data architecture.

Integrating BFO 2020 into Andrea Gioia's models enhances their ability to support hyper-automation by structuring data into meaningful information. This approach allows systems to manage tasks autonomously—such as loan approvals or property transactions—by leveraging structured, contextualized data points formalized within the ontology. The result is that more efficient, consistent, and scalable decision-making processes are critical for advanced automation strategies.

4. Ensure Scalability and Consistency

Andrea and Abby both emphasize the risks of data silos and inconsistencies that emerge without a consistent framework. BFO 2020 addresses these risks by enforcing semantic rules across systems, ensuring that data remains integrated and consistent throughout the enterprise. This consistency is essential for scaling ontology-based systems across business domains and applications.

5. Enhance Enterprise Knowledge Management

Andrea’s models show how BFO 2020 strengthens knowledge management by linking datasets across domains (e.g., finance, real estate). By embedding data within a coherent ontology, BFO 2020 ensures that raw data is transformed into usable information, making it accessible and actionable across various workflows and applications within the enterprise.

Conclusion: Merging Ontological and Information Architecture Perspectives

By integrating Abby Covert’s insights on transforming data into information with Andrea Gioia’s context-sensitive models, it becomes clear that BFO 2020 provides a robust framework for structuring and contextualizing raw data. This ontological structuring transforms data into meaningful, actionable information, especially within an Ontology-First approach, where the ontology serves as the governing structure. This ensures that contextualized, interconnected information drives all decisions, automated processes, and workflows.

At its core, Abby Covert’s principles emphasize the need to structure data into actionable information, which perfectly aligns with the goals of ontology-based systems. These systems are not just repositories of raw data; they are knowledge systems where information architecture and ontology work together to enable hyper-automation and enhance enterprise knowledge management. Data remains isolated and fragmented without a solid ontological foundation like BFO 2020. With it, data becomes part of a structured, coherent informational ecosystem that drives better decision-making and operational efficiency across the enterprise.

Key Takeaways:

1. Data-to-Information Transformation: Abby Covert's focus on transforming raw data into information through structure and context aligns perfectly with the role of ontology in enterprise systems.
2. BFO 2020 as a Semantic Layer: BFO 2020 adds a semantic layer that formalizes relationships between data points, ensuring data becomes actionable information that powers automated processes.
3. Hyper-Automation and Scalability: By integrating ontological principles into systems, organizations can support hyper-automation and ensure scalability and consistency across their data architecture.
4. Enhanced Decision-Making: Systems designed with an Ontology-First approach can make better, more informed decisions by contextualizing data within a structured ontological framework, driving efficiency and innovation in enterprise operations.

What’s Next: Extending Andrea Gioia’s Vision with Top-Level Ontologies in Part 2

In Part 1, we explored how an Ontology-First approach aligns with Andrea Gioia’s models for Operationalizing Information Architecture, offering a scalable, integrated solution to address the complexities of modern data systems.

In Part 2, we’ll build on this foundation by examining the crucial role of Top-Level Ontologies (TLOs), such as Basic Formal Ontology (BFO 2020), in operationalizing these models. We’ll show how TLOs extend the value of Gioia’s approach by providing the semantic precision and formal logical structure necessary to support cross-domain interoperability, advanced automation, and consistent decision-making across enterprises.

Join us as we delve deeper into how formal ontologies unlock the full potential of information architecture and hyper-automation, driving systems to new levels of efficiency and clarity.