Eight Archetypes of Data Citizens
And what they mean for a data community
This is the final part of a series exploring attitudes towards data. - 1 - 2
Last week, I fleshed out the four poles of data attitudes: optimism, pessimism, skepticism, and nihilism.1 These stretch along two axes:
A person’s drive to understand the world through data
A person’s drive to participate in an existing social system
In the extreme, the pure optimist is naive, trusting numbers to a fault. The hardcore pessimist is — no offense intended — an unintelligible mathematician. The nihilist is a solipsistic psychopath, for good or nefarious reasons. The skeptic is a befuddled mess, for grasping too many reasons.
But pure x-metricists are rare. Most of us have an attitudinal center but travel broadly due to our social milieu and available resources.
The space you occupy lends itself towards certain classes of contribution — certain roles. I’ve segmented and labeled these in the chart below: the innovator, builder, advocate, adherent, investigator, challenger, cynic, and prophet. (See the blog’s appendix for more detailed descriptions.)
At any given moment, you can map any person in the business to this grid, for every data domain. But that position is not static. It can change rapidly. And understanding how and why these attitudinal shifts happen is an interesting way to think about creating a community that uses data in a healthy way.
Let’s map out a few of these common journeys.
The Data “Person”: OP —> PN —> OP —> PN —> …
The first data hire “does data”. No one knows how, and they hardly know why. They just know their data person is a superhero.
By day, she is the quintessential citizen of Company City. “Here’s our source of truth,” “That’s not how we define LTV,” “Did you check the dashboard?” She schmoozes with leadership. She collaborates with managers. She aids individual contributors.
At night, she dons cape and cowl and deals with a different clientele. She routs existential threats: toxins in the replication pipes, dashboards rigged with explosives, penguin floats parading through Salesforce. She puts the villains away, using any means necessary, lest the city she fights for collapses into chaos. If she has to redefine the metrics a bit to make it work, that’s just the price of doing business.
Batman metaphors aside, this dual existence of the first data person is exhausting — it’s a lot of psychological travel to go back and forth between creator and consumer. But the early data system requires advocacy and cheerleading, as well as legislation and protection. It needs a hero.
The Concerned Citizen: SN —> OS
Fred has seen this movie before. New boss, new dashboards, new talking points. Same eventual collapse into confusion and frustration.
So Fred watches for a while. New Manager seems to care about metrics. New Manager sets up THE dashboard, gets it endorsed by the data team, returns to it in meetings again and again and again.
So Fred starts asking questions — “What about this? What about that?” New Manager answers.
So Fred starts bringing more context to dashboard discussions — “I wonder if there’s any way to measure this or that.” New Manager listens.
Six months later, New Manager asks Fred to onboard three new colleagues. Fred wonders, “Where should I start?” And before New Manager answers, Fred realizes, “Oh, of course: THE dashboard.”
The Ex Nihil Creator: NP —> PO
Igor is upset. The engineering team is sitting on a million-dollar idea and no one even sees it. If someone just took this model and hydated it with that data and piped it into that service… it could be a whole new product line.
So he locks himself downstairs for a week, turns off Slack, and hacks out a prototype. Ooh, ahh, interesting, his colleagues say. Could we change it a bit and — yeah, that’s really neat. I didn’t know we could do that. Igore iterates more. People start to connect the dots, and a new view of the world comes into being.
Now, he’s upstairs, with Slack notifications blaring. A product manager has been assigned and needs updates. Colleagues need PRs reviewed. What once was an idea — a challenge to the accepted order of things — is now its own parallel reality.
The Disenchanted Leader: OP —> NS
“I’m, uh, sorry, ma’am, uh, the dashboard has been, err, double-counting users for the past six months. This, uh, includes the numbers we, um, sent our investors during diligence.”
Jesus Christ, she mumbles, and opens LinkedIn.
The question for data teams is: what sort of attitudes do you want to cultivate?
The obvious answer is: optimism! Just take the number of optimists in your organization divided by the number of people… voila, a “data-driven quotient”.
DDQ_domain = N_optimists / N_employees
But the business needs people who operate outside the system. To build it, to secure it, even to topple it occasionally. And even if the data team wanted to make everyone an optimist, they could never sway the nihilists — they trade in a different currency.
No, I think a better metric better for data teams to focus on is to focus exclusively on the individuals who are socially invested, i.e. to maximize the ratio of optimists to skeptics:
DDQ_domain = N_optimists / (N_optimists + N_skeptics)
You can imagine extensions: a domain Data Leverage metric that multiplies the data-driven quotient of a domain by the relative size of the community.
DL_domain = DDQ_domain * (N_optimists + N_skeptics) / N_employees
Why is this framework useful? Because it eliminates the notion of technical requirements and focuses solely on the group of people who seek to operate more efficiently through data.
The battle to be fought is “How do we make good decisions quickly and confidently.” The battle is not about stacks, stats, or spreadsheets. It’s about the efficient coordination of human activity. And data optimism facilitates this — everyone is calibrated to the same number line.
Appendix: Enumeration of Data Attitude Archetypes
P-N: The innovator ignores the existing system and instead seeks to build new ones. Stakeholder wishes are relevant, but the innovator is obsessed with the potential reality to be brought forth.
P-O: The builder brings concrete new metrics into existence, aligned with the goals of the business. A “metrics layer” is an exemplary Builder project.
O-P: The advocate champions a set of metrics as “the way” to describe the system. This is done with the understanding that there are gaps but that it is socially powerful to ignore or triage them.
O-S: The adherent is bought into the metrics system, but mostly as a way to supplement rather than supplant their existing worldview.
S-O: The investigator evaluates whether metrics resonate with their experience and knowledge. They are in a non-committal state of mind.
S-N: The challenger prioritizes her own context about the world to against the metrics. Qualitative user feedback is a way of actively occupying this position, as it is always opposed to a metrics system.
N-S: The cynic casts doubt and aspersion on the existing metrics. This is generally not a helpful attitude, but it can create friction or weakness in a system that could be useful if it is indeed incompetent.
N-P: The prophet speaks truth to power. He actively seeks to disrupt the status quo. They bring forth a potential new way of organizing the world.
I’m going to drop the -metricist suffix for ease of reading, but I am still referring to a very specific kind of semantic optimism, pessimism, etc.