Insights Interview | Bonnie Bowes

We interviewed Bonnie Bowes, a Taxonomist and Information Architect Specialist with over ten years of experience developing and managing enterprise taxonomies. Bonnie is Product Taxonomist at Checkatrade. Prior to Checkatrade she has worked at Meta (Facebook), Sony PlayStation, Apple, and Chevron. Bonnie holds a MA in Library and Information Science from San Jose State University and a BA in History from George Mason University.

Tell us about you and your experiences?
BB: I’m an American living in London. I moved from California to the U.K. with my cat Juno in August 2022. I grew up in Kansas, in the middle of the US, and moved to Washington, D.C., to attend college, and then to California for graduate school. After graduate school I continued to live and work in the San Francisco Bay Area for many years before relocating to London.

I’m interested in linguistics and etymology, learning about the root meaning of words and how they generate mental images that translate into things and concepts. As a kid I spent a lot of time at the public library. I figured out how to find books on my own. From an early age I was drawn to libraries, collections of things, classification systems. At university I studied Latin and found the history of words and how they are related fascinating. I wanted a career that reflected my interests, and I wanted to work with either books or art. Library and Information Science was a natural fit for me.

As an undergraduate I worked at the National Gallery of Art Library in Washington DC. It was a life-changing experience. Working in a prestigious library offered a peek into a certain type of career I hadn’t known existed or was otherwise out of reach. To me the art librarians were so glamorous and sophisticated. One of my tasks was opening packages in the mail room and placing them on the right shelf by auction house. Let me tell you, it was an incredible mailroom job. I had access to rare books and artifacts. I learned to assist the cataloguers and how to archive exhibitions. I wanted to work at the National Gallery of Art Library forever. But I couldn’t because I was on a student work program. My experience at the National Gallery of Art Library influenced my decision to change from Law to Library and Information Science. I thought the world could use another librarian more than it could use another lawyer.

How did your career as a Taxonomist develop?
BB: Many taxonomists find their calling by accident. For me, becoming a professional taxonomist was intentional. I had specific career goals in mind and devised a plan. After college I moved from Washington DC to California. The 2008 American recession was in full swing. I was stuck at a low paid job with no prospects. Then Google started hiring librarians.

Google was in a partnership with Harvard University at the time, working on this great digitization project. They were digitizing all of the works of literature available in the public domain, and it was huge. It was the first time I saw librarians in the news paired with tech. What was interesting to me was Google didn’t refer to them as librarians, although they were. They were calling the librarians “taxonomists.” I thought to myself that’s what I want to do! I want to be a librarian in tech. In graduate school I focused on the more technical courses such as database systems, digitization, information theory, programming, metadata management.

My first professional taxonomist role was as a digital archivist at Chevron, where I learned cataloging, controlled vocabulary, and taxonomy design under the mentorship of the head digital librarian, Ron. He ran a tight ship and adhered to strict best practices, making it an excellent early professional experience. This strong foundational learning shaped my practices for my future career.

From Chevron I went on to work in digital asset manager and taxonomist roles at other technology companies. I was recruited for and took on more senior roles. I worked on several types of taxonomies for very different products. I was the first taxonomist hired at two of the companies. I pioneered taxonomy teams and advocated for a taxonomy community of practice. Being in the right place at the right time helped, meaning Silicon Valley during a time when companies started to really value the information science skillset. Corporate investment in big data, digitization, asset management E-commerce, and machine learning led to new jobs in the field, including taxonomist roles.

What do you look for as a customer of Taxonomy software? What do you expect from a tool?
BB: At my previous company I made a successful business case for licensing specialized taxonomy software – the first in the company. The project was to design a taxonomy out of 300k random terms, a crazy flat list, so professional software was obviously needed. I made a requirements list and ranked them into ‘must have,’ “nice to have,” “could have” buckets. I used the Synaptica Taxonomy Top 100 Checklist which is a fantastic starting point for anyone evaluating a new taxonomy tool. It was a key reference resource for moving away from spreadsheets into a professional taxonomy software tool. Assuming you already have basic functionality, my taxonomy tool requirements shortlist includes:

• Batch editing. Specifically the ability to integrate, merge, deprecate and restore terms in large batches with minimal clicks. The ability to batch edit is critical, because taxonomies have reached such massive sizes any management tool needs to be able to edit at scale. Ideally, you can do all of your editing in-tool and not have to download the taxonomy into another software like Excel, make changes, and then reupload into the tool.
• The ability to tag and also filter on tags is essential for being able to isolate specific areas of the taxonomy.
• The tool must be collaborative to support cross-functional team adoption. If an editor cannot leave comments to another editor, it won’t be viewed as a collaborative environment.
• The tool should offer customizable output file format options that meet the end users’ requirements for transfer and upload. Unless you have a robust API from the get-go, you’re going to have to use the input / output file protocol to deliver the taxonomy to your engineering team or whoever and they will have specific ingest format requirements.
• The ability to link, map and integrate multiple taxonomies is important with the type of work I usually do. Does it offer API support?
• Robust reporting and analytical features are important. Can the tool generate its own reports, or will you have to export it into another tool to run reporting?
• Can the tool support an API? You’ll need an API for the internal servers to make real-time changes and avoid import/export delivery.
• Finally, is there a customer success package or support that’s included in the contract? Unless your internal engineering team is willing to provide tech support every time there is an issue, you’ll need a reliable customer service package you can turn to when things break, or questions arise.

Why did you choose Synaptica?
BB:  I knew from experience that having the right functionality for the company’s unique business needs, extreme reliability, and an extensive customer support package are important. Software is never what you need it to be right out of the box. There has to be a continual relationship between business owner and vendor to ensure that the tool develops, that it continues to do what we need it to do, as project needs change. I wanted a tool with a vendor that was willing to enter a partnership with the taxonomy team for the continued development and adoption of the tool. Synaptica were willing to do that. Put all this together and that’s been our leverage, our great success.

You collaborated with Synaptica to develop custom functionality and an auto mapping tool. Can you tell us about that experience?
BB: The team needed the ability to quickly map and integrate large taxonomies to meet our goals. On the scale we were working with, it wasn’t possible to do manually. We needed an automated mapping tool that could understand the contextual meaning of the terms, not string matching. Take a term like “pluto” which has four meanings. “Pluto” can refer to a Roman god, a short name for plutonium, a Disney dog character, or a planet. Mapping terms by string matching “pluto” doesn’t work.

We developed a method where the taxonomy tool looks at other characteristics of the term and the node path: including the broader terms, the narrower terms, the source URL, the category. It looked at patterns and similarities of the content. Based on the contextual findings, the tool assigned a mapping confidence score. Let’s say for a good confidence score based on similar broader and narrower terms and categories would be 90% and higher suggests an accurate match. This way we could automatically map tens of thousands of nodes in a batch. And rather than spending weeks or months reviewing the mapped nodes manually, we could approve all mappings with a 90% or above confidence score. That way, the team only had to spend time manually reviewing the lowest confidence scored mappings.

The ability to auto-match saved significant time and increased productivity. We could also determine the degree of the term associations, whether the mappings should be fuzzy, exact, or related, depending on the purpose of the project. Being able to map and integrate large taxonomies quickly and accurately made it possible for us to offer taxonomy mapping as a service to other teams.

Without the automated mapping tool, the same mapping projects would have taken months or years to map. With the auto match tool, it took a matter of weeks to undertake accurately.

What is it like to design a taxonomy in spreadsheets versus using specialized software?
BB: For me, designing a large taxonomy using spreadsheets was a nightmare. I once revamped a taxonomy that had over ten thousand nodes in Excel. It took three months of single focused work and resulted in nearly fifty versions before it was ship ready. It was one of the most painstaking, frustrating experiences of my career. You’re trying to add nodes, removing them, merging them – trying to keep all the node ids intact. It’s difficult to keep the IDs associated to the right nodes while cutting and pasting in a spreadsheet. This is not what spreadsheets are meant to do.

When it was time to deliver the taxonomy, the IDs were not correct from all the cut and paste editing. This meant the engineers had to take time to figure out a solution to this problem. When you have five brilliant engineers who have to stop what they’re doing to figure out how to get nodes matched to the right node ID, that’s a huge drain on ENG resources. The project almost didn’t launch. I would never want a taxonomist to have to go through a situation where they didn’t have the right tool to perform the task. I made a vow that it wouldn’t happen again to my team, that we would build taxonomies using proper tools from then on.

How do you design for machines?
BB: You design for machines by employing rules and consistent logic the machine can understand. I begin by defining the domain the taxonomy describes. The rules for inclusion; what it is and is not. Next, I imagine I am designing for an alien whose language is logic, patterns and association. Information architecture structures are concerned with grouping things based on mental models or their relationship to other things. Taxonomies are concerned with precise classification and logical precision. This allows machine learning models to infer content understanding.

What level of granularity machine tagging can support in a taxonomy?
BB: Taxonomy can support machine tagging at any level, but inference is more accurate at the higher levels. For example, it’s easier to tag whether something is a plant or an animal than the type of species. For deeper taxonomies that require eight or more levels to describe a domain the sweet spot is usually around level four. Granularity is a trade-off; it allows more precise description but at the cost of less accurate inference. What is confusing for a human is even more confusing for a machine. We understand well what a concept or thing is at a broad level, it becomes more complex the more granular we deconstruct it. When thinking about granularity, keep an even level throughout the taxonomy and be intentional about going too broad or too granular. What level is necessary to describe the domain?

How do you use engineered solutions in partnership with human curation?
BB: Human curation is helpful to engineering solutions in situations where human nuance presents inconsistent or fuzzy logic. Curation informs ranking and relevance logic. For example, algorithms need to learn how to rank what is most important, “of all the data available, this is what we care about most”. An E-Commerce algorithm may determine “color” to be an important product attribute. But for which products? Human curation may inform the algorithm that “color” is an important attribute for curtains but not for books.

To improve search results human curation will determine the rules for how backend tables are indexed, and how keywords are weighted to retrieve and display what is most important at the top.

From the taxonomy side, human curation helps determine the right access points and level of node granularity to display in the UI. When working with clustered terms, human curation is needed to determine the granularity level of the cluster, and what to call it. In short, human curation is required whenever an understanding of human mental models is necessary. Machines cannot rely solely on logic.

Why are taxonomies important to organizations?
BB: Taxonomies are essential for any organization that relies on data and information. Data analysis is becoming the basis of the service offer and business models. Data is king. But ambiguous data is a liability, not an asset.

Taxonomies are uniquely valuable because they are the great disambiguation of the IA world. Taxonomies describe a domain. The domain can be all living things like Linnaeus’s taxonomy of life, or the products sold on an E-commerce platform.

Disambiguation is a specialized craft; it is the first step in transforming data into useful information. First, we need to know what it is and where it came from, and then we can write the rules for inclusion. That’s what taxonomies paired with robust metadata do exceptionally well. Nine million things described by seven levels Kingdom-Phylum-Class-Order-Family-Genus-Species – boom-boom-boom.

How should an organization develop a strategy for taxonomy?
BB: Conducting a taxonomy landscape audit is a good place to start. If your organization employs taxonomy in any form, then you want to know what taxonomies are being used, where and how.

For example, while working with an E-commerce platform, we knew that one taxonomy was used for inference, another taxonomy was visible to the sellers, and possibly a third taxonomy in ads and marketing. The taxonomy landscape audit allowed us to take a holistic view of the platform, to confirm which taxonomies were doing what throughout the funnel and Identify gaps and opportunities.

Once the taxonomy audit is complete, and the goals understood, you can work with a data scientist to develop opportunity sizing metrics related to the goals. This is the first phase for developing a business case for a taxonomy strategy.

How can a taxonomy expert help?
BB: Ideally an expert who designed the taxonomy will know it inside and out. They can see its shortcomings. They will be able to understand the opportunities and the limitations of the solution. Taxonomy is not a magic word; it can’t solve everything. Having an expert on hand to scope the taxonomy solution and provide realistic advice about what is and is not possible, and how to best optimize the taxonomy, is valuable for any taxonomy related project.

What advice do you give to others who are developing their taxonomy project?
BB: I always begin with a content audit before designing or building a taxonomy. It’s the second step after defining the domain. Look at the actual content, interact with it and get as many samples as you can. Terms, scheme and structure all develop from the content the taxonomy describes.

What makes a good taxonomist, what skills are needed?
BB: A successful taxonomist would have solid research and strategy skills, as well as an understanding of information science theory. Classification, semantics, linguistics and to be comfortable working with technology. Patience and the ability to do monotonous work also help.

As a taxonomist you should have the ability to work well with other professions. Whoever you’re supporting, whether it be engineers, scientists, doctors, technicians, – we must be able to speak their language. Whichever sector you support, learn corporate vocabulary. Know the basics. Embed yourself into the profession your taxonomy supports.

What do you think are the biggest challenges for taxonomy in the future for the sector
BB: Remaining relevant and useful. There is a danger in becoming too comfortable in a routine way of doing things, of not stretching your skill set or embracing inevitable sector changes. Learning hard skills like data science and analytics to become more self-reliant. Embracing new technology, adjusting with the changes in the market and the world.  We know some of the old ways of doing things no longer require human expertise. That doesn’t mean the writing is on the wall for taxonomy, but we do need to pay attention. Do the university taxonomy courses truly reflect industry demands? Formal training for taxonomy managers would help taxonomy teams to be better functioning. I think of taxonomy as a support science. I have to keep upping my professional learnings to provide solutions to the industry I’m supporting. What I was doing two, three, five or more years ago are not the same things that will work today.

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