“I got to keep movin’, I got to keep movin’/Blues fallin’ down like hail, blues fallin’ down like hail/Hmm-mmm, blues fallin’ down like hail, blues fallin’ down like hail.” – Robert Johnson, Hellhound on My Trail

I was laid off recently from a taxonomy position I very much enjoyed. Rather than wait for the smart to wear off, the day after, I sat with a not insignificant IBU IPA and doubled down on the mixed emotions rattling through me to expound on some common issues I see in the work world of taxonomists. Many of these challenges I’ve dealt with in my blogs in one form or another in the past, but the day after a layoff hits a little differently when there’s a bruised ego and a true feeling of loss at play.

Like emails sent in the heat of the moment, posting a blog to social media hot on the heels of a personally emotional layoff is probably not the best idea. I’ve had nearly some time to let reality set in and revisit this writing. Surprisingly, I didn’t have to alter much of the content.

It’s Just Business

Layoffs happen. If you think your company loves you, you may have never been laid off. If it has never happened to you, then I am genuinely happy that you’ve not gone through the experience. Personally, I think it’s ok to believe in your company, drink the Kool-Aid (actually, it was grape Flavor Aid), and embrace their mission, goals, and strategy. I also think it’s natural and in the interest of self-preservation to recognize that any company will unceremoniously dispose of you when necessary. Their commitment to you will never match your commitment to their goals. Short of creating your own company and working for yourself building something you strongly believe in, this is always going to be the case. I enjoyed my company, believed in their mission, but also wasn’t one bit surprised when I got laid off.

From a detached, objective position, layoffs are as much a part of doing business as hiring when times are good. Layoffs can be triggered by downturns in which an organization’s revenue drops enough to merit reducing headcount. They can be triggered by well-meaning attempts at reducing redundancy and bloat. They can also be triggered by poor strategic decisions. Whatever spurs the layoffs, they are not always conducted in a strategic and thoughtful manner. Or, perhaps, there is a thoughtful strategy, but not one that will clearly bring about success.

No matter the impetus for a layoff, in my experience they disproportionately affect contractors and, because of what I can see immediately around me, taxonomists. There’s often an overlap between the two groups. When staff is augmented with consultant, freelance, or contract taxonomists, expect those people to be higher on the list when it comes to reducing headcount. The business likely doesn’t understand the role a taxonomist plays or minimizes the skill set as something anyone can do. As a seasoned taxonomist with years of consulting engagements behind me, I can tell you not everyone can just be a taxonomist. Like any proficient role, taxonomists bring unique organizational and research skills to bear. Shifting this work to the technology organization or a business domain in the enterprise is a misappropriation of work.

You’re a Taxo-what-now?

From years in the industry and still having not yet quite perfected my elevator pitch explaining my job, I can tell you taxonomy is not well understood. Cue the taxidermy jokes, financial tax questions, and, if you had the “ontologist” title, interrogations about whether you know anything about cancer. Speaking metaphorically, I do, and that is the metastasizing misunderstanding of what taxonomy, ontology, and semantic technologies bring to the table. Hence, taxonomists are not just laid off singly, but en masse, eradicating entire capabilities from organizations which likely had a long and painful path to establishing an enterprise taxonomy capability in the first place. With one swift slice of the oncologist’s scalpel, an entire function is excised with no idea of how to replace the missing connective tissue.

I’m sure many people think their job is extremely important, if for no other reason than it keeps one motivated to show up every morning. As part of justifying your work to yourself, and, more importantly, to the chain of command above you, there needs to be definitive and clear expressions of why the work is too critical to eliminate. Expressing the necessity of taxonomy work is essential precisely because it is misunderstood. Taxonomy work is often seen as simply gathering terms and putting them in lists or hierarchies, but the deep work of information science is frequently unseen. An inexperienced, self-nominated taxonomist is going to be at a loss when confronted by a dedicated, commercial off-the-shelf taxonomy and ontology system backed by a standards-compliant RDF triple store. That leaves the amateur with two options: do “taxonomy” in a simpler, alternative tool or establish a taxonomy capability in the organization starting with hiring a trained taxonomist to build the taxonomies and lead the effort to evaluate and purchase a taxonomy and ontology management system.

Having worked in many organizations which have gone from zero to taxonomy capability, it is no small task and can take anywhere from months to years. Taxonomists can start as contractors building taxonomies in spreadsheets and then either transition to a full-time role him or herself or lead the effort to hire a full-time taxonomist and bring in a tool. Regardless, the effort it takes to convince upper management of the need for a taxonomy program and the long journey to making it an essential part of the business involves a tremendous amount of time and resources. Establishing such a program and cutting it demonstrates a lack of understanding, an irresponsible waste of company resources, and a phenomenal strategic error, especially in the rising tide of machine learning and generative AI.

Foundations

It is widely understood and communicated that clear, accurate foundational data is essential for a business to create meaningful analytics, support strategic decisions, and train machine learning models properly. Despite the monumental efforts corporations put into building and maintaining clean data, it’s not all that common to see it done well. Typically, the issue is years of legacy data, all created with good intentions but frequently in conflict across disparate systems, inaccurate due to the passage of time, or made obsolete by shifts in strategic direction. To use a worn expression, there is no magic bullet to solve this problem. Migrating all that data to a data lake is time-consuming and still results in redundant, conflicting data. Creating taxonomies, ontologies, and tying data together with semantic layers and knowledge graphs also assists with creating and building foundational data, but these methods too can result in disparities.

There is no simple plug-and-play solution, but pursuing multiple strategies and bringing them together is not impossible. Data lakes serve a purpose, just as taxonomies and ontologies do. They are not either/or solutions, but AND solutions: structured, relational data working with structured semantic data and both describing semi- and unstructured content across the organization.You can have one strategy without the other…but why? There are realistic barriers to pursuing multiple data strategies, including budgets, resources, data ownership, and governance. Barriers do not preclude building strong capabilities to address these different aspects. Completely eliminating one or more of these pillars makes it more difficult for the business to execute a clear and effective data strategy, only to return to rebuild that capability at a later date at a not insignificant effort.

I’ll Be Back

There is a bitter vengeance tale in my head that goes something like this: you laid me off and now I’m back as a consultant making money from you to fix what you broke. Cyborg, guns blazing, blasting all your crappy taxonomies straight to hell. Well, not very likely, but I always win this one in my head (aside: does anyone ever lose the self-righteous conversations in their head?). Yeah, ok, so maybe I’m not back and may never be. Maybe the story runs more like someone either “discovers” that taxonomy is useful or stumbles on the skeletal, ancient remains of a discarded taxonomy management system half-buried in the earth, sorely out of date, and filled with the eggs of xenomorphs. Some face-hugger plants the seed of understanding in the astronaut and they decide the organization needs to do taxonomy stuff. Maybe someone listens and they hire dedicated roles to do taxonomy stuff. Taxonomy stuff takes off, becomes seemingly essential, and then taxonomy stuff gets stuffed in a round of layoffs. Maybe taxonomists are all just Cylons.

Anyway, bitter, wounded emotions aside, eliminating an enterprise taxonomy capability does your organization a disservice. Taxonomy is foundational to well-structured, semantic, governed data. Taxonomy data should be feeding your website navigation and search, applied as metadata to content, data, and digital assets, and providing a semantic layer to your products to power personalized experiences and recommendation engines. I’ll say it bluntly: if you don’t get taxonomy, you’ll never get machine learning. Or, rather, your machine learning models will never be optimized. If you think your generative AI proof of concept will run without taxonomy, it will…at first. Then, when scaling is the next step, expect it to fall on its face. Large language models without the context of your domain, your organization, what makes you you—that is, what is modeled in taxonomies and ontologies—will give you the bland, contextless results that can only be delivered by models that don’t get who you are. Taxonomists get who you are, but, well, they’re gone.

I said bitter, wounded emotions aside. Scrap that. I am very passionate and emotional about quality data. Nerd or no nerd, this is true. And your organizational truth is going to suffer without the expertise a semantic expert can deliver. You might say this is shameless self-promotion in search of the next gig, and you might be right. What is also right is that whether I’m the taxonomist hero who saves your disintegrating semantics or it’s another capable taxonomist, I’ll applaud the result. Because truth in data. Because waste and redundancy. Because efficiency. Because user experience. Because…

I’m not going to skewer the company that laid me off and dissect what I see as their poor strategic decisions. And, honestly, I don’t know what their strategy is or will be going forward. But, I will say this: I think it was a mistake. Not for me, not for the team I really admired and enjoyed working with, but for the greater strategy of the organization. Revenge tales aside, the company is going to feel the lack of governed, semantic data built by seasoned, professional taxonomists. There are people who remain in the organization who will carry the torch, but they’ve been hobbled by indiscriminate layoffs subjected to, unfortunately, a misguided data strategy. Maybe there are other options the company will pursue to fill the gap. Maybe taxonomy will come back someday when the stock prices are more favorable, but, in the meantime, no decent data strategy is complete without semantics.

I’ll close with a call to action to taxonomists. You already know how difficult it can be to build and maintain a taxonomy capability in your organization. Once established, make it essential. Make it foundational to data work wherever it happens. Integrate the taxonomy system into important, enterprise-wide data systems and strategies. No job is impervious to layoffs, but cementing the capability will, hopefully, help you avoid the taxonomy blues.