Beware the SLOTH

For all the frustration with HiPPOs, there’s another creature in the corporate menagerie that leads data professionals astray

Few concepts loom larger in the collective imagination of data professionals than that of the HiPPO, which is the observation that decisions are often determined by the Highest Paid Person’s Opinion rather than data, facts or figures. It’s a near universal experience for those of us who earn our living by assembling those data, facts and figures¹, and it’s understandable why it’s so frustrating. At worst, it’s a dismissal of the value of our profession, and at best, it’s simply a waste of our time.

But for all the frustration with HiPPOs, there’s another creature in the corporate menagerie that frequently sends data professionals down long, winding paths that ultimately amount to very little. We’ll call them SLOTHs—Statistical, Logical and Over-Thinking Hesitaters²—because they avoid acting or making decisions through an over-reliance on quantification and data. SLOTHs might at first blush seem like the opposite of a HiPPO, but working with both is similar because they don’t know how to get value from their company’s data, instead distracting data professionals with busy work that likely won’t see the light of day.

The dangerous thing about SLOTHs, however, is that their fundamental behavior is not bad. They want to be objective, to use metrics to calibrate and broaden their understanding, to find opportunities that are not obvious through casual observation or pure instinct. They see data as a useful tool, and they help wielding it to its fullest extent. This is a flattering and refreshing situation to be in as a data professional—it’s always nice to feel wanted—but it can get out of hand quickly.

SLOTHs reveal themselves gradually, starting with reasonable requests that somehow manage to grow in complexity and scope the more you work at them. Realizing you’re working with a SLOTH requires some situational awareness, and a big part of identifying and handling them is recognizing what kind of SLOTH they are. While I won’t pretend I’m never ambushed SLOTHs, I’ve come over time to recognize three major types and figured out some ways to avoid getting in too deep with them.

Uncomfortable SLOTHs

People see and interact with data today more than they have at any other point in human history. We all have phones and fitness trackers that report our biometrics, our banking apps give us insights about our spending trends, SaaS products we use for work provide usage tracking dashboards, and so on and so forth. There are still people in the world who have never been exposed to a bar chart, but as a data professional, the likelihood that they’re one of your coworkers is essentially zero.

The problem is, familiarity does not guarantee competency—beyond a basic intro to statistics course in school that may have been years or even decades ago, almost no one is trained in working with data. People are expected to just know how to come up with metrics, read charts, and operate their company’s BI tools. Given this environment, many people are uncomfortable or unwilling to admit when they feel out of their depth when working with data. Data literacy is a common part of many jobs, but unlike other professional skills that many people need (like recruiting or selling), it is likely to be mistaken for raw intelligence. Admitting to needing help can feel like admitting to being stupid.

This problem is substantially amplified when the person in question is somehow uncomfortable in their role. Maybe they’re switching to a new function, or this position is broadest their scope has ever been, or this is the most senior role they’ve ever occupied. They will already have something to prove, and if they’re someone who does not feel comfortable with public displays of humility, they will definitely not want to look like they can’t even read a dashboard.

Identification

This type of SLOTH is most identifiable by their analysis paralysis. When presented with data, they are likely to be overwhelmed with indecision, want to see more metrics, segmentations and pivots. Often times they are hoping that enough digging will result in a view of data that will make the answer their question obvious, or that eventually someone else will see the answer and say something about it, relieving them of their need to interpret the data. A common refrain of the Uncomfortable SLOTH is that the data is not actionable—they have information but it’s not evident to them what they should do with it.

Another variant of this SLOTH is one who is uncomfortable with the specific data technology at your company³. They might request that the company switch over to tools they’ve used previously (My last company used Tableau, why can’t we do that here? It’s so much more intuitive!) or complain about low quality of data without being able to provide specific examples of how it falls short.

Handling Uncomfortable SLOTH

Don’t call them out. As a data professional, it can be easy to feel contempt or resentment for this type of SLOTH because they are not good at something you find fundamental. While the idea of exposing their incompetence might feel emotionally satisfying, antagonizing your coworkers is generally a bad way of getting them to cooperate with you.

Instead, your job is to make your process and assumptions explicit. Include definitions for any specialized terminology or acronyms you use, address limitations of the work, explain the correct interpretation of charts you include in plain language, and critically, have clear takeaways and recommended next steps. If the SLOTH you’re working with doesn’t know how to use data, lead by example.

They will ideally learn and build comfort with data over time by working with you, and even if they don’t, giving them something they can easily act on is a perfectly fine way to drive change at your company.

Distrustful SLOTHs

Another common varietal of SLOTH is someone who doesn’t trust someone else for some reason. It is very common for this SLOTH to be a manager doubling down on their attention to a direct report, or one of your stakeholders squinting with suspicion at one of their peers. This SLOTH will seek out your help when they suspect that someone else has failed or else is on the precipice of doing so, and their goal will be to trawl through all data available to them until evidence of this failure is uncovered. Said simply, they want to weaponize data against someone else, and as a data professional, they expect you to do that dirty work for them.

You could play an endless game of armchair psychologist by trying to determine why folks want to use data for this. Perhaps your SLOTH is unwilling to directly confront someone. Maybe they want data so that their point isn’t perceived as politically or personally motivated. Heck, they might even genuinely believe they’re driving the right outcome for the company by holding someone accountable for results. It doesn’t matter where it comes from because each route cause leads to a similar place—data professionals getting sidetracked from their day job for an extended period of time, working on increasingly value-dubious requests instead things that will help their team move forward.

Identification

The tricky thing about identifying a distrustful SLOTH is that the behavior itself is normal and even healthy. It’s reasonable for a department head to ask analysis-generating questions about why an OKR is behind schedule, or for a customer experience lead to ask for help determining whether the engineering team shipped a bug when there’s a sudden influx of calls about the password reset flow being unavailable. Data can be a tremendously useful tool for understanding why our expectations and reality don’t match, and starting from a set of agreed upon facts (as represented by the answers a data professional has provided to those questions) can make for a more productive conversation.

To identify the unhealthy version of this behavior and the distrustful SLOTHs that are responsible for it, you need to look at the circumstances surrounding the questions, rather than the questions themselves. As the SLOTH is framing up a problem to you, how many potential causes do they outline, and how seriously do they consider alternatives that wouldn’t implicate the person or team that they clearly distrust? Is this a repeated pattern, and if so, how often is the person the SLOTH distrusts involved? Is this person even aware that the SLOTH is having someone deep dive on their domain? It may take some time to be sure you’ve got a distrustful SLOTH on your hands, but eventually the pattern will be clear enough.

Handling Distrustful SLOTHs

This type of SLOTH’s fundamental behavior is quite normal in the modern workplace but becomes detrimental because it’s taken too far. This can make it difficult to deal with since you may already be fairly involved by the time things get out of hand. If you sense that someone is drifting from simply doing some quantitative due diligence into being a distrustful SLOTH, one of the best things you can do is try to defuse the situation.

If you start getting asked to help investigate questions that feel increasingly targeted at someone or some team, take a moment to step back. Is there a good reason to be digging in here, whether it’s qualitative or qualitative? What else could be causing what you’re seeing, and what can you do to make sure those lines of investigation are given a fair consideration too? It’s possible that adequately exploring them would take too much time or effort, so do be careful here. Engaging too deeply can easily lead to getting mired in someone else’s conflict, when your main goal is to keep yourself and other folks you work with focused on productive work, rather than chasing the right answer to the wrong question.

This is also a great place to be opinionated if you can. If the SLOTH is insisting that you look into something that seems unlikely to be true or you are hitting diminishing returns by digging into the same thing again and again, it’s very reasonable for you to tell them that you don’t think this is worth investing effort in. Do this with tact, of course, and if necessary get your manager to back you up when you say your time is better spent elsewhere, but don’t get pressured into doing a distrustful SLOTH’s dirty work.

Dreaming SLOTHs

The rarest type of SLOTH is a true believer—they are absolutely certain that there is latent, untapped potential in your data. The problem is, they cannot explore that potential themselves because they do not have the knowledge, the skills, or the time. And so, they turn to you to do it for them.

Of all the SLOTH types, Dreaming SLOTHs are most likely to be your boss or another data professional who wants to live vicariously through you, but they are more commonly someone in a cross-functional role. Your colleague who has bought into the latest data hype cycle is an obvious example, but some of the most insidious Dreaming SLOTHs are folks who have previously worked closely with someone who does the same kind of data work as you. Regardless of whether this past exposure was positive or negative, these SLOTHs will have strong opinions and specific expectations about what should be possible for someone like you, and they will insist that you meet those expectations regardless of their relevance to your current team or company.

Identification

The surest way to identify a Dreaming SLOTH is by their inability to tie data work to business value, particularly value that will manifest along a reasonable time horizon or is achievable without significant investments in your data technology. When Dreaming SLOTHs talk about data, they focus on what “real” companies do, or things that involve sexy technologies or would make them seem insightful and forward-thinking for suggesting. They’re thinking primarily of the narrative—the details are for the data professional to figure out.

Similar to Distrustful SLOTHs, the tough thing about Dreaming SLOTHs is that this is also a fairly normal behavior taken to an unproductive end. While there is a difference between a leader setting a vision that guides execution and a leader expressing a broad aspiration with the expectation of someone else doing all the work, it can be tough to tell which version you’re experiencing without knowing the person or having been in similar situations before.

This is another time where taking a look at the broader context is key. It’s great for folks to dream about the potential of your company’s data, but just because they have ideas doesn’t mean those ideas are fleshed out enough to become the subject of a data professional’s work. What kind of track record does this person have of working with data, at your current company or other previous companies? Do they have real examples of contributing to value creation with data, and if so, how active of a role did they play in that? How well did they understand what their data professional collaborators’ roles? If they can speak to specifics, it gives them more credibility when making claims about how to use data, even if their current idea is expressed vaguely.

Another key thing to think through is whether this person has the proper organizational authority to be making open-ended strategic decisions, much less open-ended strategic decisions about what members of the data team should be doing. Many people are capable of coming up with big ideas for the future, but far fewer have a good understanding of all the tradeoffs a company is making when it chooses which projects and initiatives to focus on in which order. It’s a lot different to have a VP of business development tell you that there’s a line of business to be build around your data than it is to have a product manager that you sometimes help with designing A/B tests to tell you the same thing. The former has context and resourcing that they could potentially put behind you, whereas the latter is far more likely a Dreaming SLOTH who hopes you’ll do all the heavy lifting for them.

Handling Dreaming SLOTHs

If it’s possible, try not to work with Dreaming SLOTHs. They don’t know what data can realistically be used for at your company (and possibly in general as well), so they are going to send you on a lot of wild goose chases that distract you from more valuable work. If you can’t outright refuse them, your best bet is to appeal to OKRs, goals, your backlog, or some other representation of the fact that you don’t have time for what they’re asking. Even if you like this person or think they’re on to something, be cautious about diverting your time to something that feels too much like a dream. Your time as a data professional is valuable, and the best way to convince others is to act like it.

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The danger of SLOTHs is that they don’t seem dangerous at all. They look like eager collaborators, only gradually revealing themselves by delaying action again and again. Don’t go looking for SLOTHs among your colleagues, but stay vigilant and you’ll see them before they can do their worst. The good thing about slow-movers is that they’re easy to outmaneuver.

1. So much so that data technology companies name themselves in reference to it

2. This is admittedly a pretty tortured backronym, but hey, I tried.

3. Note that someone finding a tool doesn’t fit into their workflows is NOT an example of this—execs are notorious for preferring reporting solutions that arrive in their inbox since they spend so much time there anyway, which is part of why so many BI tools have an email delivery feature. Don’t try to solve workflow problems as if it’s just a matter of making people more comfortable with data, solve them by understanding why there’s friction in using your tools.