Enterprise ontology was a topic of academic research in the 90s, e.g. in Canada, Holland, and Scotland. This early work was informed by industrial practice in enterprise modeling, and emphasized industrial issues like product design, requirements, organization, manufacturing, transportation, quality, inventory etc. The added value of an ontological approach compared to conventional enterprise modeling was however never clear to industrial practitioners, and ontology remained an academic exercise, which was not picked up by leading tool vendors.

Recently, the fad of the “semantic web” has brought forward an even more theoretical approach to enterprise ontology. Its proponents seem unaware of the earlier work. As before, interoperability is the core concern that ontologies are addressing, e.g. in the IDEAS framework. However, it seems that the focus has moved from interoperability of enterprises to exchange of enterprise models. This post questions if such an approach is viable. In the absence of any evidence that demonstrates that ontologies work in practice, I apologize for the theoretical nature of this post. Read the rest of this entry »

Data modeling and data management were originally IT-driven activities with the prime goals of providing persistent storage to application systems. Data exchange and interoperability has later become key requirements, extending data modeling to domain models, and data management to hubs and data warehouses. Now, there is a growing demand for adaptable data services coming from business intelligence, networked project design, collaborative design, concurrent engineering, integrated operations and predictable remote maintenance and repair. All these domains have different approaches, and methods for data modeling and management, so the fragmentation and replication of data may still prevail. Providing services for data access, viewing, updating and knowledge sharing among customers, contractors, suppliers, clients and life-cycle service providers is therefore more important to profitability, quality and innovation than ever.

From industry and defense we have learned that a holistic design approach should be adopted to integrate data and knowledge management. In all knowledge work, the life-cycle management of data structures, properties and parameters, values and ranges, dependencies, rules, decisions, and experiences, require in-depth understanding of the data. This in-depth understanding is workspace dependent, local and possessed by practitioners only.

A holistic design approach should therefore be applied, where data models are defined, adapted, and managed by practitioners in a role-oriented Federated Knowledge Architecture. A knowledge architecture is capable of providing the work contexts required for self-serve distributed data management.

Read the rest of this entry »

A previous post outlined some reasons why we think data modeling and semantic approaches are poorly suited for developing common data models across applications, disciplines, functions, and organizations. In particular, we argue that formal, precise representations makes it difficult to discuss terms before we have agreed upon a common language. Another problem is class hierarchies, which typically are local to a community. Enforcing a single classification structure in a common model can alienate stakeholders who have a different way of seeing things. Finally, visual models are preferred over textual representations because they more easily work as a neutral common ground, avoiding terminology wars.

This post introduces a modeling methodology that utilizes knowledge architectures to arrive at integrated information and data architectures. By following this approach, you create a conceptual knowledge model, which is suitable for interdisciplinary, cross-functional and cross-organizational communication. The methodology outlines the steps involved in creating common understanding, and some modeling principles that should be followed.

Read the rest of this entry »

An integrated data model can connect the applications, functions, and disciplines of a company. It can be a foundation for service oriented architectures, business process automation, and master data management. The process towards establishing an integrated model can be long and winding. While data modeling methodologies are suitable for documenting the end result of this process, they may hinder more than facilitate its progress. In particular, prematurely introducing a precise common representation, may alientate groups of stakeholders, leading to a “common” model with a bias towards a few perspectives. An approach more adjusted to group dynamics and social learning is needed. Physical data models and logical information models should be complemented by conceptual knowledge models. This post presents some of the challenges involved, while a later post outlines a knowledge architecture approach to integrating data models. Read the rest of this entry »