underwriters; sales personnel
This is not the only data- and content-centric approach. Here are some others:
•Start with a body of content and ask people to come up with labels.
•Come up with labels and ask people to group them within existing categories.
•Ask people to group labels into new categories.
•Sample all of the existing terminology lists and have people reconcile them.
•Hand out sample content and have people classify it with existing labels.
Each of these approaches informs the mental model of the user and helps create language and terminology that can label and tag products, services, and reference content for easier access.
The challenge with data- and content-centric approaches is that they lose the understanding of the user’s goals. The challenge with a user- and problem-centric approach is to prevent it from becoming a laundry list of user challenges. A combination of approaches is often the best way to crack the problem.
Checking the Box versus Validating the Work
How do you know when an ontology is sufficient to be useful? Here’s a story that may illustrate that question.
The head of knowledge and search at a large global services firm was having challenges with information management across the enterprise. The company’s program for searching for answers to employees’ questions was held up as a model of exemplary practices at conferences; numerous attendees would crowd around the presenter after her talk and ask questions about how her team did it. Each week, the group reported positive results and metrics showed steady improvement in measures such as improved search accuracy and precision of results. But as a head of one of the business units confided to us, “People still can’t find what they need.” The company wanted to understand why, so they hired my consulting firm.
At first, it appeared that the company had already achieved a high level of success. My consultants and I listened to their approach, and our first thought was, “They really know best practices and understand how content, knowledge, taxonomies, and ontologies work. They are applying them and following all the steps.”
But our main contact at the company suggested that we dig deeper. “Go to the end users and look at what they are trying to do,” they advised. “Evaluate their taxonomies and ontology and how they got there.”
The rest of the week was quite revealing indeed. Even though the steps that the global services firm followed were valid, problems emerged. Use cases were vague: “Users must be able to access the information they need when in the field.” That type of use case is not testable. What information? For what purpose? From where? None of the details were specified.
When it came time to build the taxonomies and ontology, a manager sent out a spreadsheet that people added terms to, and then the head of the group added his own terms and deemed the taxonomy complete. There was no validation or testing with actual users or measures of usability. The firm had not followed best practices and heuristics for ontology development. There were too many overly broad terms (such as “documents” and “content”— what is the difference between a document and content?) and too many detailed terms that had insignificant differences (“exemplars” and “examples”). Hierarchies were six or seven levels deep in some parts and one level deep in other areas, making it nearly impossible to establish a mental model of how the information was organized. And there were many other violations of accepted practices (such as large “general,” “miscellaneous,” and “other” categories—which are useless individually and nonsensical when combined).
Ontologies should not be a matter of individual opinion. They should not be deemed complete by business leaders, based only on their judgment or developed in a vacuum. Everything should be testable and measurable. No one at the global services firm tried ingesting information using the system and then measuring how people located the information on an end-to-end, holistic basis.
An even larger mistake in many organizations is a lack of a clear understanding of the customer at a level of detail that truly informs decisions and provides enough of the features that both humans and machine learning algorithms can interpret and act upon. Achieving this level of understanding and insight begins with humans applying consistent, repeatable, testable methodologies, because machines cannot understand human needs without a reference point. What is it about your services, products, and offerings that appeals to your customers? When do customers seek them out and how do they make decisions? What is most important to them and why? What exactly do they need to do from moment to moment when they are interacting with different parts of the organization? That understanding is critical and all too often insufficient, lacking in important detail, or just plain wrong.
AI can be a powerful way to contextualize the customer experience by seamlessly serving up the information, products, services, and solutions they seek, but it cannot make sense of bad data and it cannot substitute for an understanding of the customer. AI cannot make up for your sins of poor information practices and bad customer experiences. Some of the tools can help, but they need a structure, a scaffolding in which to operate and in which to contextualize information. Successful AI programs require that the organization has its data house in order and that it understands what customers and end users need.
The Promised Land: Applying the Ontology
There are thousands of ways to apply an ontology.
There is a sculpture on my desk (the inspiration for the cover image on this book) that represents the infinite ways of applying an ontology while also representing the structures within it. The piece is made of glass of varying types with different refractive characteristics. There is an easily seen cubical structure within it; it is complex but not mysterious. But when light goes through the sculpture, it creates infinite representations of that light. The ontology is the cube. The ways of using the ontology are the light that goes through it. It changes depending on perspective but in predictable ways. It is infinite in its output but made of a finite number of elements.
The ontology is the foundation of language and business terminology and concepts that are important to the organization. It becomes the knowledge scaffolding and reference point for building various applications
