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{{DISPLAYTITLE: Introduction to Business Ontology}}
== An Introduction to the Business Ontology ==
Various Standards bodies, Organizations, Business frameworks, methods, approaches and or concepts have their own vocabulary. Each of these vocabularies has its own definition of terms, like what is strategy or what is a process. For example, OMG, which is the software standard body that created the Business Process Model Notation (BPMN) standard, has various standards that all have a different shape/notation, description as well as semantic relations around a process/activity.

BPMN has a different shape/notation, description as well as semantic relations; than does Case Management Model Notations (CMMN) or even Value Delivery Model Language (VDML). All of these respected standards are from the same software standard body, but lack standardization between them. The same lack of standardization applies to most other frameworks, methods or approaches we studied. For example, The Open Group Architecture Framework (TOGAF) and Archimate are from the same organization, The Open Group. They do not only have multiple different objects, the objects they actually do have in common have different descriptions, rules and even semantic relationships for them, although both address enterprise architecture. Additionally, TOGAF and Archimate have different models i.e. views as well as meta models.

When an organization adapts both the Architecture Framework TOGAF and the architecture software tool ‘Archimate’ from the same organization i.e. The Open Group, the modeling and architecture work would result in a low degree of maturity, which was found to be surprising to many organizations, regardless of how much work or money and organization would invest into such a project, portfolio or program due to the inconsistencies mentioned above. According to existing maturity modeling concepts such as Capability Maturity Model (CMM), the maturity level of organizations combining TOGAF with Archimate would be level 1, which is siloed – the lowest level.

The examples above illustrate the lack of (and need for) standard business terms, definitions, semantic rules and concepts. These represent the starting point of the academic interest of the Global University Alliance (GUA) in this topic. The first GUA research in 2004 identified that the lack of repeatable standards around business concepts within business modeling, engineering and architecture concepts resulted in unnecessary siloes, lack of reusability and many other modeling issues such as low maturity in organizations. The need to identify relevant reusable/replicable patterns and develop concepts that can be used by any organization, both large and small, regardless of its products/services, activities or industry, became apparent in September 2004. At this point the research and analysis around business ontology was formally initiated.

This included:
*Outlining the research questions.
*Analyzing patterns, both in terms of what doesn’t work (anti-patterns) and what works, again and again (best practice), and what are unique practices applied by leading organizations.
(leading practices).
*Identifying commonly used meta-objects and models used within the repeatable patterns.
*Developing artifacts and templates that increase the level of re-usability and replication.

The next section discusses how the context (i.e. collaboration between academia and industry) in which the ontology was developed. Section three, explains the value of ontology in a business context, including the theoretical foundations for the business domain ontology that is than presented. We conclude with a summary.

=== Academia Industry Design: A Collaborative Process Between Research & Industry ===
Arising from 5 years of previous work, the GUA was founded in 2004 as a non-profit organization and today (Nov, 2015) they are an international consortium consisting of over 450 universities, professors, lecturers and researchers. Their aim it is to provide a collaborative platform for academic research, analysis and development. As illustrated in Figure 1, they achieve this through defining clear research themes, with detailed research questions, where they analyze and study patterns, describe concepts with their findings. This again can lead to additional research questions/themes as well as the development of artifacts, which can then be used as reference content by practitioners and industry as a whole.

[[File:An-Introduction-to-the-Business-Ontology-Figure-1.jpg|thumb|800px|left|alt=Figure 1: Overview of the Academia Industry Design (AID) process used in the Global University Alliance and collaborative industry practitioners.|Figure 1: Overview of the Academia Industry Design (AID) process used in the Global University Alliance and collaborative industry practitioners.]]

The GUA collaborates with standards bodies such as:
*'''ISO:''' The International Organization for Standardization (French: Organisation Internationale de Standardization).
*'''CEN:''' The European Committee for Standardization (CEN, French: Comité Européen de Normalisation).
*'''IEEE:''' Institute of Electrical and Electronics Engineers is the largest association of technical professionals with more than 400,000 members.
*'''OMG:''' Object Management Group: Develops the software standards.
*'''NATO:''' North Atlantic Treaty Organizations (NATO’s) with the 28 member states across North America and Europe and the additional 37 countries participate in NATO’s Partnership for Peace and dialogue programs, NATO represents the biggest non-standard body that standardizes concepts across 65 countries.
*'''ISF:''' The Information Security Forum, Investigates and defined information security standards.
*'''W3C:''' World Wide Web Consortium-The W3C purpose is to lead the World Wide Web to its full potential by developing protocols and guidelines that ensure the long-term growth of the Web/Internet.
*'''LEAD:''' LEADing Practice, the largest enterprise standard body (in member numbers), which actually has been founded by the GUA. The LEADing Practice Enterprise Standards are the result of both the GUA research and years of international industry expert consensus and feedback on the artifacts and the repeatable patterns documenting their application in practice.

When academics, based on their research, build concepts and artefacts for practitioners, these concepts/artefacts need to be constructed rigorously to meet academic standards and be relevant for practitioners. Construction rigor is typically considered to be the domain of academia, although practitioners are also acknowledged to create knowledge and artefacts relevant to themselves and others (Nonaka, 1996). Academic artefact design methodologies have considered academia as a source of rigorously designed knowledge and artefacts, of which the relevance can be tested in practice. However, we observe an ever-growing involvement of practitioners in academic design science (DS). March & Smith (1996) hint towards an evaluation of academic artefacts in a real world setting, as in natural science. Hevner, March, Park and Ram (2004) consider the organisational context in which academic artefacts need to serve as a major influence on relevance.

They explicitly discuss the evaluation of academic artefacts in the real world, through case studies and field studies. Peffers, Tuunanen, Rothenberger and Chatterjee (2008) identify practitioner feedback as an essential aspect of artefact evaluation in a real world setting. Finally, Sein, Henfridsson, Purao, Rossi and Lindgren (2011) model the design of rigorous and relevant artefacts as a collaborative process between academics and practitioners. In a first phase of their action-design-research (ADR) methodology, academics give the initial version of the artefacts to a small group of practitioners (e.g., a community/panel of experts).

These practitioners provide valuable feedback that helps to mature the artefact. In a second phase, this improved artefact is applied by a larger group of practitioners, whose feedback will allow the academic to improve his artefact further. If this feedback requires no further modifications of the artefact, a final version of the artefact is published. Although ADR is a very mature methodology in academia-driven artefact design, it could be made more generic (generally applicable) by alleviating (eliminating) two implicit constraints (biases) present in all DS and ADR publications:

#Academia is the single source of rigorously constructed knowledge.
#User requirements are invariable and provide a continuous improvement feedback loop to academia.

As regards the first point, although practitioners typically create knowledge (artifacts) that is (are) relevant for them in a specific organizational context, this does not necessarily imply that this knowledge cannot be generalized and applied in other organizational contexts. This generalization (and evaluation) would typically be the role of academia in this kind of knowledge creation scenario. There are multiple instances of such artifacts existing today. For example, the Boston Consulting Group (BCG) created a matrix in 1970 to help analyze organizations’ product lines. This has enabled organizations to allocate resources as well as use it as an analytical tool in brand marketing, product management, strategic management, and portfolio analysis. While widely used, several academic evaluations have given feedback on its usage as a growth–share matrix. (Armstrong; Scott; Brodie; Roderick, 1994).

A detailed academic study from Slater and Zwirlein (1992), analyzed 129 organizations. The conclusion of the study was that those who follow the BCG matrix as a portfolio-planning model for growth success had lower shareholder returns. The study concluded that the BCG matrix is a relevant and useful artifact, but it was applied incorrectly and should be applied in other general contexts. Such an evaluation would typically be the contribution of academia in this kind of knowledge creation scenario. As regards the second point, in ADR, an academic artifact is handed over to practitioners as soon as they accept it. This approach does not account for new feedback when user requirements have changed and the artifact is no longer relevant in its current form. From requirement engineering, requirement modeling and requirement architecture it is known that user requirements continuously change. (Gotel and Finkelstein, 1994, Ralph and Wand, 2009). Therefore, what is needed in reality is an approach that allows for continuous artifact improvement/modification through continuous user feedback, and values user knowledge as valid (relevant) input (which could thus be made more rigorous).

A major difference between academia and practice is the way knowledge is acquired. Practitioners typically rely on Experience and Induction, while Academia use research, analysis, deduction and the scientific method. From the above discussion points, we could argue that the academia and practitioners are complementary in the following ways:

*'''Rigor vs. Relevance:''' we can determine that Academia does Rigor best, while Practitioners do Relevance best.
*'''Abstraction Level:''' Academia typically designs solutions at the type level (concepts and solution for a type of problem) while Practitioners typically design solutions at instance level (solution for a particular problem).
*'''Knowledge creation processes''' in terms of developing artifacts should interlink between rigor and relevance, of which the rigor aspect can be analyzed in theory best and the relevance can be tested in practice best. Therefore:
**'''Combining explicit knowledge to develop new explicit knowledge.''' Academia typically combines explicit knowledge at type or instance level to create new knowledge concepts at type level. Whereas the practitioners typically combine explicit knowledge at type or instance level to create new knowledge at instance level. The latter being described by Nonaka (1996).
**'''Internalization:''' Converting explicit knowledge (e.g. books, standards) to tacit knowledge (e.g. personal knowledge). Academia typically teaches explicit knowledge to be transformed into tacit knowledge of students (e.g. practitioners). Whereas practitioners typically study academic concepts and non-academic solutions to develop competencies (tacit knowledge), which was also described by Nonaka (1996).
**'''Socialization:''' Sharing tacit knowledge through interaction. Academia research share tacit knowledge in doing research and publications together. Whereas practitioners share tacit knowledge by doing things together (and learning from each other while doing). The knowledge creation mode involving only practitioners was also identified by Nonaka (1996).
**'''Externalization:''' The need to convert tacit knowledge into explicit knowledge. Academia studies in this context, what practitioners do (at instance level) to create new knowledge at type level. Whereas practitioners sometimes document what they do, and sometimes share this content (e.g. industry standards, best practices).
**'''Feedback Loop:''' There should be a loop of feedback and enhancement between academia and practitioners.

Figure 1 visualizes the knowledge creating processes in academia and practice and how they interact. Academics develop research questions, founded on the research themes they identified. They analyze real-world situations to answer their research question through the identification of patterns (e.g. laws of physics). These patterns are documented and combined with other knowledge (patterns and concepts) to build theories that might require additional concepts, which may lead to additional research themes. Industry practitioners will use these concepts and patterns to develop artifacts that will help them structure their knowledge about the business reality they experience. These artifacts will be published to peers (e.g., as standards), used and improved by them. These improvements, which may point towards user requirements that were not identified by academics, should feedback to academia.

Since practitioners will mostly use the concepts embedded in the artifacts to document their knowledge, the expected impact of practitioner feedback on the elementary concepts of business is expected to be relatively low (i.e., new business concepts are not discovered that often). However, it is very likely that academics will observe new innovative ways of working with their artifacts in real-life situations, when observing the practitioners in the industry. Industry practitioners can also develop their own artifacts, which may contribute directly to the academic literature. The likelihood of this scenario is expected to be between that of identifying completely new concepts and discovering new application scenarios. The next section discusses the value of ontology, this provides the theoretical foundations for the benefits of a business domain ontology that is presented afterwards.

'''Document''': [[Media:An-Introduction-to-the-Business-Ontology.pdf|Download the An Introduction to the Business Ontology document]]

[[Category: Business Ontology]]

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	LEADing Practice's Enterprise Standards are developed by a) Researching and analysing industry best practice & leading practices, b) Identifying common and repeatable patterns (the basis of LEAD's standards), c) Developing the Enterprise Standards that increase the level of re-usability and replication, and d) Build industry accelerators within the standards, enabling to adopt and reproduce the best & leading practices. LEAD is therefore practically oriented, but based on a strong theoretical base that it gathers from its research partner, the Global University Alliance.		LEADing Practice's Enterprise Standards are developed by a) Researching and analysing industry best practice & leading practices, b) Identifying common and repeatable patterns (the basis of LEAD's standards), c) Developing the Enterprise Standards that increase the level of re-usability and replication, and d) Build industry accelerators within the standards, enabling to adopt and reproduce the best & leading practices. LEAD is therefore practically oriented, but based on a strong theoretical base that it gathers from its research partner, the Global University Alliance.
		+

	=== Global University Alliance (GUA) ===		=== Global University Alliance (GUA) ===

LEAD Reference Content:Conceptual Structures - Revision history

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