The practice of Business Analysis revolves around the formation, transformation and finalisation of requirements to recommend suitable solutions to support enterprise change programmes. Practitioners working in the field of business analysis apply a wide range of modelling tools to capture the various perspectives of the enterprise, for example, business process perspective, data flow perspective, functional perspective, static structure perspective, and more. These tools aid in decision support and are especially useful in the effort towards the transformation of a business into the “intelligent enterprise”, in other words, one which is to some extent “self-describing” and able to adapt to organisational change.
However, a fundamental piece remains missing from the puzzle. Achieving this capability requires us to think beyond the idea of simply using the current mainstream modelling tools. Instead, we need to take into account methods that provide a basis for sharing meaning at both human and computational level, and that are geared to capturing the semantics (i.e. the meaning) of entities that describe our enterprise perspectives. This is where the concept of ontologies come in. Ontologies are representations that provide a basis for sharing meaning at human and computational level, and are an invaluable addition to any business analyst’s toolkit.
Ontologies help you formally represent domain knowledge that is accurate and reusable, which aligns very well with your reuse strategy for enterprise knowledge. Ontologies are platform-agnostic knowledge models and building them does not require you to have any extensive technical or software engineering skills. This means that as business analyst, you are able to produce the blueprints for any system or software design project, allowing you to more rapidly prototype information structures and test them out before passing your specifications over to software engineers to implement. Consequently, ontology modelling empowers business analysts as information and knowledge architects.
In addition, ontologies are extremely relevant to the area of information systems interoperability, providing you with the mechanisms to drive semantic data exchange and federation across multiple information sources and repositories. They can also be extended into structured knowledge bases for constantly-evolving linked data that have complex relationships and are held in dynamic schemas, thereby responding very well to changes in organisational knowledge. The thing is – that’s just a glimpse of some of the useful benefits of ontologies from a business analysis standpoint – ontologies, in practice, do much more than that!
Unfortunately, ontology modelling is an underused technique chiefly as a result of a lack of awareness in the industry domain, and because tool support has been relatively limited in the past. But this course is intended to be a game changer, focusing on providing a comprehensive introduction to ontologies in the context of business analysis application, in order to encourage the adoption of the approach. The material provided in the course covers relevant background information to get comfortable with the concepts being explained, the justifications for applying ontologies in business analysis practice, walkthrough examples, and other important details that are pertinent for you to be able to hit the ground running with using ontologies within your own business analysis pipeline. Become a pioneer of applied ontology in the field of business analysis and lead the way to telling your success story!
Welcome to the very first lecture in this series! We'll go through introductions and take a look at the high level aims and objectives of the course.
This lecture will give you a feeling of the roadmap for the course. As well as describing the course structure, you'll also gain a pretty good idea of the various learning objectives to be accomplished by the end of the course.
Here, you will find a decision tree diagram that will help you decide whether this course is really what you are after.
This lecture concludes Section 1, summarizing the main points discussed.
In this lecture, we'll get to cover broadly what an ontology is, focusing on its definition and importance as a basis for sharing meaning.
In this lecture, we'll take a look at the most fundamental components of an ontology. We'll introduce the concepts of classes, relationships, individuals and axioms that we can use to describe a particular subject matter.
The representation of ontologies can be tailored for human and machine interpretation. In this lecture, we'll run through the basics of what's needed for being able to represent ontologies.
Ontologies when encoded for machine interpretation serve as logical models. This lecture covers, at a relatively high level, what the logic based perspective of ontologies is about.
This lecture concludes Section 2, summarizing the main points discussed.
In this section of the course we'll get to explore how an ontology expressed in the Web Ontology Language (OWL) is pieced together in a descriptive way. The model we'll get to explore is a formal representation that describes the field of business analysis from a basic standpoint. The ontology tool used is the Protégé ontology editor.
In this lecture, we'll download Protégé ontology editor and also run through the necessary steps to get you started with ontology exploration.
This lecture covers the basics of classes and class hierarchies in OWL using Protégé.
This lecture covers the basics of properties and property characteristics in OWL using Protégé.
This lecture covers the basics of class descriptions in OWL using Protégé.
This lecture covers the basics of populating an ontology with facts and fact statements in OWL using Protégé.
This lecture explores ontology visualization in Protégé, as well as other external tools for visualizing OWL ontologies.
This lecture concludes Section 3, summarizing the main points discussed.
In this lesson we'll get to kick off the more detailed discussions for applying ontologies in the field of business analysis.
This is a continuation of the previous lecture on the case for ontologies in the field of business analysis.
Ontologies address the requirements for achieving information systems interoperability. This lecture presents an introductory discussion of the benefits of ontologies for semantic interoperability.
Ontologies work hand in hand with important IT and information systems methodologies, including the Model Driven Architecture (MDA), Model Driven Interoperability (MDI) and Service Oriented Architecture (SOA). This lecture explores this understanding in more detail.
This audio lesson provides a core discussion of the cost-benefit implications of applying ontologies within the business analysis pipeline.
In this lecture we will see some concrete real-world examples of applied ontology.
This lecture concludes Section 4, summarizing the main points discussed.
This lecture covers the essentials of applying ontologies as basis for the definition of business processes and rules.
This lecture explores, at a conceptual level, what the building blocks of ontology driven systems are. We'll discuss the basic architecture for being able to 'plug' ontology models into actual information systems for people to start using. We'll also get to see an example of SPARQL querying in action.
The MDA methodology provides an approach for translating system requirements into platform-independent and platform-specific models. In this lesson, we'll discuss another application of ontologies in the practice of business analysis, which is to support platform-independent system design and development.
In this lecture we'll get to discuss, at a conceptual level, the essence of ontology mapping techniques to enable the reconciliation of multiple disparate ontologies.
When it comes to developing ontologies, reusing already-existing ontology models is a good idea to cut down on the development lead time. In this lecture, we'll get to take a look at examples of good reusable ontologies in the likes of Friend Of A Friend (FOAF), Dublin Core, The Organization Ontology, DBpedia, and more. NOTE: The Cabinet Office Organogram appears to have been taken offline, although for illustrative purposes, the example has been kept in this lecture in order to show different kinds of ontology reuse and applications.
This lecture concludes Section 5, summarizing the main points discussed.
This lesson provides some further discussions about the topic of ontologies in business analysis. The lecture highlights the key industries in which ontology engineering is currently being applied as well as a preview of some of key skills for excelling as knowledge architect.
This is the last lecture in this series, where we'll wrap up the course.
Attributions and special thanks to friends and family, etc.