Enterprise architecture (EA) has been developed by four different disciplines, as shown in the table below. 

Discipline Focus Architecture Frameworks and  Techniques
Management Consulting Money Enterprise Modeling FEAF, BPM
Information Systems (IS)  People Enterprise Architecture Zachman, TOGAF, ARIS
Software Engineering Software Model Driven Architecture UML, MOF, SOAML
Systems Engineering Hardware (System of) Systems Architecture SysML, MODAF, NAF, UPDM

Enterprise modelling was first applied to analyse industrial operations, extending IDEF and other process modeling notations. Later, information systems people applied similar techniques for aligning the IT with the business it supports, and for IT management in general. After software engineering established object oriented modelling of the internals of software systems, systems engineering adapted these techniques to hardware and software co-design. Systems-of-systems thinking led them to extend their reach beyond technology and into the enterprise realm. Read the rest of this entry »

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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.

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Roles is a key concept in social and organizational studies and is a hot topic in these application areas; Enterprise Role Management (ERM), Role Engineering Assessment (REA), Role Life-cycle Management (RLM), Social Networking for Business, Business Process Management (BPM), Enterprise Resource Planning (ERP), and Holistic Design of Active Knowledge Architectures. The definition of identity, authentication, authorization, accessibility, and traceability are security needs driving this growing interest.

In most IT applications authorization is isolated from the tasks, the enabling methods and data. Roles and role-specific workplaces with tasks and views, are not supported. Coherence, coordination and collaboration are poorly supported. Workplaces are programmed and can not be designed in context-rich workspaces. Roles operate in workspaces created by work-centric and situated knowledge.

In this post we therefore look at the benefits of designing roles in context-rich workspaces. How structured roles and work-centric knowledge form the basis for a new form of smart organization. A smart organization of service-teams must be able to design and engineer roles and workspaces as projects evolve to capture practical rules and methods. Agile project teams with clear responsibilities and rules for providing services to each other should be designed as part of project design. Read the rest of this entry »

Knowledge Management (KM) ranked high on corporate manager agendas in the early 1990s, but KM rapidly became a confusing term that managers scorned. KM systems never delivered what IT providers promised. However, some advanced information management methods were invented, and enterprise portals were developed.  

Web 2.0 and Enterprise 2.0 technologies are providing new means for social networking and sharing of personal and public knowledge. However, the core situated and work-centric enterprise knowledge can not yet be expressed, shared and managed by these technologies.

Managing work-centric and situated enterprise knowledge demands fundamental rethinking of not only the nature of enterprise knowledge, but also the practical approach to realizing knowledge management. Knowledge workers must be equipped with services to manage personal as well as role-specific knowledge. Practitioners must be empowered to react to operational events, and become responsible for their own actions, data, workspaces and work plans. Personal competence and skill profiles must match the roles the person is authorized to perform.

This post looks at how knowledge management can be implemented across projects, stages, systems and disciplines, and lifecycles, using active knowledge architectures to update and configure workspaces of work-centric and situated knowledge. Read the rest of this entry »

In the 1990s most leading companies were very much concerned about knowledge management, recognizing that knowledge and competence are the driving forces of business, design and innovation. Industrial managers were also concerned about brain drain, loss of practical competence when skilled workers with multiple job experiences retired or left to join competitors.

This post is an attempt to revitalize industrial interest in KM by introducing new concepts and discoveries, such as knowledge architectures and families, and by giving good answers to industry questions like:

  1. What is enterprise knowledge?
  2. What inherent properties does enterprise knowledge exhibit, and what capabilities does it provide?
  3. How is enterprise knowledge best expressed, shared and managed by industrial users?
  4. How is work-centric knowledge best encoded to meet industrial needs?

The questions are answered based on scientific discoveries and experiences from industrial pilots. Read the rest of this entry »

Large projects are complex, involving multiple products, disciplines, methodologies, techniques, systems and stakeholders. They often fail to meet expectations, schedules and budgets, and results are often poorly validated and managed. Current IT support to project definition, planning, execution and management is fragmented and rigid. Lifecycle data exchange is transformative rather than evolutionary. Innovation is not driven by evolution, embedding experiences and lessons-learned. There are many uncertainties and unknown dependencies in the early phases, many nonproductive meetings, stove-piped and sequential information flows, poor data management, and limited knowledge sharing. Collaborative design, cross-functional team working, and service composition are inhibited. Work environments and user interfaces are rigid and discipline-specific.

This post raises the questions: Are we wrongly trying to generalize and program creative work, collaborative and adaptive environments, and human behavior? Are we doing right to standardize properties, embedding their parameters and values in code? Can this approach serve complex customers with dynamic demands for controlling dependencies, supporting innovation, and automating adaptation? 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 »