Ontologies have a chequered past in the business arena, although many significant benefits have been realised over the past two decades in biological and medical research. Wrapped up in the AI hype and occasionally lumped in with knowledge management projects or taxonomy projects they are often shelved curiosities whose original purpose is lost by their champions. What is an ontology? Generally speaking, it is a set of concepts, their properties and the relationships that link the concepts together.

 

The power of Ontologies lies in the management of complex, and often overlooked, facts about an Enterprise. Which of your brands are specific to a market and a language? What is the trade name of this widget in some foreign language? What is its common name? Are shipping manifests linked to a single invoice or can they be composed from multiple invoices? Is this within a single division or across the entire enterprise? All of this data is somewhere within the company be it in employees’ minds, in software configurations or explicitly listed within a standards document. 

 

The benefit of an ontology is that it becomes a single point of reference for all of these items in a format that is machine readable and writable, that permits multilingual documentation and labelling and that can differentiate between what an object is and what it is called. An important point is that while an ontology has to be consistent, it can represent multiple truths and perspectives at the same time. Not all parts of the organisation work the same way; this is perfectly normal and there is great value in documenting how they actually work.

People have a natural tendency to categorise and organise information. Taxonomies are one way to create simple hierarchical "trees" to organise concepts and values.  One issue with this method is that different departments in the business have different taxonomy layering structures for the same concepts. This results in many taxonomies being used for some concepts (Products being a typical example) and trying to report or find product characteristics against these different taxonomies can become complex.

 

But ontologies go beyond this by adding the capacity to relate the concepts, instantiate the concepts and add labels and descriptions in multiple languages, thus decoupling the identifiers from the representation data. From an enterprise perspective, many applications and systems fail to differentiate between these and the result is that small changes trigger a complete reconstruction of the application’s database. 

 

The separation of the identifier from the content and multilingualism means that data can be updated automatically within downstream software without having to navigate across what the “official translation” for a concept is: Who would you even ask to find out what the localised name of your corporation is in German or whether the description for a particular widget is the same in the German and Austrian markets are the same?

Master data management often promotes the idea of capturing information as a single source of truth. This makes sense at first glance: everyone should be on the same page, using the same processes and nomenclatures across the enterprise. However, most businesses are more nuanced, with information viewed and used in multiple business perspectives.  This becomes even more of a challenge with the common use of 3rd party applications that can embed different nuanced meanings depending on the degree of fit for the purpose of the business. Often this implied context, and how to handle the exception, is undocumented and lives on an ad hoc basis within the minds of company personnel. This creates an additional unreported business continuity risk.

 

A simple example is a list of office locations: some locations are plants with a shipping yard, multiple buildings, a mailing address for the front office and street addresses for all of the above. The fact that a parcel makes it to the right location will often require either local knowledge from the courier company or a few phone calls and false starts as the right destination is found. 

 

The assignment of an identifier to each sub-location helps to differentiate them all from an ambiguous label “The Pittsburgh Plant”, but fails to record the relationship between them. This creates some concerns for downstream users of the data who have to track the identifier within their own system and attempt to identify the “right one” amongst the list while aggravating end users who just want to send something to “Bob at the Pittsburgh Plant”.

 

It is in these cases that ontological solutions shine because of their ability to handle complex data and handle ambiguity. An ontology can easily represent that the office space in building ‘D’ of the Pittsburgh Plant in Westmoreland has its mail routed through the main plant office at 567 Alpha Drive. Finding the “right” entry is left to the system integrators who have full knowledge of their context and who can query the right entry.

Lastly, ontologies can be designed to promote Interoperability. Enterprise information systems are made up of multiple pieces of software that look at data from different perspectives and with a specific focus. This often results in having various pieces of the same thing in different locations while making it prohibitively complex to resolve the meaning of the information. 

 

Ontologies and their associated knowledge graphs allow for information that is redundant across different applications to be reconciled and loaded from a central point that can be used to link the data. While one application may only need to be aware of products name and description, another may need compliance information and a third may only be aware of product weight and dimensions for shipping purposes. Keeping all of this information in sync is difficult since applications have different ideas about object identity and identifiers; something that an ontology can easily keep track of.

Ontologies and their associated knowledge graphs provide a useful mechanism for handling complex information and translating across different viewpoints of the same instances. Beyond their application to master data management, interoperability and harmonising multiple concurrent taxonomies ontologies are an effective tool to record complex enterprise information. They have been used with success by large companies such as the ‘Fargo’ and ‘Erica’ virtual assistants at Wells Fargo and Bank of America and Google Knowledge Graphs to drive effective business tools. In future posts we will be exploring what tooling, management structure and processes are required to ensure effective ontology use within the company.  

 

This article was written as part of the Bizon Business Ontology project.