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The hard work of having an opinion
Maybe your lived experience resonates with the saying, “opinions are like assholes, everyone has one.”
That doesn’t really resonate with me. I’ve seen people miss a lot of opportunities either because they don’t do the hard work of having an opinion, or they have a good one and don’t feel confident enough to share it. “Data people” seem especially vulnerable to analysis paralysis.
Any innovation, adoption project, organizational improvement, job title negotiation, etcetera begins with one very important thing:
Whether you call it a “belief”, a “passion”, an “assertion”, a “conviction”, an “inference”, a “theory”, an “educated guess”, a “claim”, or “advice”—these are all objects of the class opinion.
All opinions are not created equal. Here’s how to craft a good one.
Traits of a good opinion
Good opinions are informed, logical, contingent and mutable and they are not preferences in disguise.
If a statement is confirmed by sufficient, objective information (for example, experimentation and physics models show that the acceleration due to gravity on the earth’s surface is ~9.8m/s²) then it’s a fact.
But we often don’t have sufficient, objective information. Good opinions are an effort to extrapolate from incomplete data, and the more data you have, the more accurate your opinion is likely to be.
How much information is enough? Well, that depends on the risks and how willing you are to be wrong (see mutable, below).
If you tend to err on the side of hasty judgment, take a moment to intentionally look for evidence that contradicts your opinion. If you think raisins in cookies are great, take a moment to google “raisins in cookies are awful” and see what comes up.
If you tend to err on the side of endless hours of research before making an opinion—stop! Make an opinion right now. Even if you don’t have any information. Stretch your brain, do your best, try to logic it out. With every new piece of information, pause and reformulate an opinion. This is good for your critical reasoning muscle, and means you’ll never be caught without at least some opinion.
Also think about the kind of information. Sometimes “tech people” or “data people” can forget (or be told to ignore) the human element. Be mindful of which information is actually relevant, and suss out potential constraints.
For example, consider the question, “What day should we host our big customer event?” You could spend days collecting, combing, and preparing information to determine the most optimal time is early September, but upon going to proudly book your chosen date, learn that the venue is only available on December 6th.
Does it matter that the optimal quota improvement is 15.34% if the sales leadership is only comfortable with a 5% increase because reps will quit otherwise? Does it matter that you can improve conversion by 20% if you’d have to use a dark pattern to do it? Do you need a survey showing how many analytics engineers feel underpaid if your salaries are 50% below the market median?
Use data where it’s useful, but look for business, process, human, and ethical constraints as well.
You may be surprised how many decisions you can make from only the process and ethical constraints, plus logic.
So now you have some facts, but you have to do something with them. You have to compile them into an opinion. This is where it gets difficult, this is the hard work.
Logic turns evidence into influence.
Crack out your critical thinking skills. For every opinion (aka “claim”) you’ll need evidence and also to establish that the evidence is relevant to the claim. More on claims, evidence, and warrants here.
Here’s an example:
Claim: If companies want to retain their analytics engineers, they need to get serious about defining job roles and compensation bands.
Evidence: The analytics engineering job market is very competitive, with jobs filling quickly.
Warrant: The more quickly and easily an employee can move to a different company, the more likely they are to leave their current employer for better pay and recognition.
I’ve observed that a lot of data professionals are very excited to put forward a lot of evidence, but are reluctant or unsure of how to turn that evidence into a claim. This is a crucial syntax error, because the language of strategic leaders is claims. The opinion is the “so what”?
Consider the difference between these two approaches:
Our current workflow is so frustrating. dbt would make it way better. Also, analytics engineers are underpaid and dissatisfied. People are planning to leave soon.
We should make adopting dbt a priority, be transparent about our upcoming plans to do so, and get serious about giving folks appropriate titles and compensation. If we don’t, our core data team is likely to jump ship soon for a company that will give them recognition, appropriate pay, and a pleasant workflow.
Taking that next logical step to reach a well-supported opinion is much more likely to result in influence and change.
Apply logic, but don’t extrapolate too far. What works in one context might not work in another.
If one survival guide tells you that in winter, the sun dips towards the south, whereas another tells you the sun dips towards the north, then what gives? Well, we can conclude that the first guide is intended for the northern hemisphere, the second is for the southern hemisphere, and neither is in an equatorial area.
Just as the survival guides would benefit from making their contingencies clear, so do opinions.
Contingencies take many forms, and here are some great go-to qualifies for good opinions:
Assuming that [some uncertain thing] is true, then…
If [this thing] is our most important priority, then…
When in the context of [a certain thing], we can…
Based on my past experience, I’ve found that…
When everyone on the team believes in the mission, your next biggest problem is…
It’s okay to be wrong.
It’s not okay to remain wrong in the face of sufficient, objective information.
Every opinion you have should always have the implicit contingency: based on what I know now. If what you know changes, it can and should change your opinions.
Good opinions are mutable—they’re open to change in the face of new, different information. Perhaps it’s a matter of putting another contingency on the opinion. Or perhaps the opinion needs to be dismantled entirely. Strong opinions, weakly held is a great framework for creating assertive, yet mutable opinions.
Note that “we don’t have enough information to take a strong position on this yet” is an opinion! And in some cases, it’s the appropriate strong opinion to have. But it’s also helpful and powerful to follow with, “given what little we know, the best option seems to be…”
Preferences in disguise
This one gets into some semantics, but hear me out.
I like to make a distinction between a preference and an opinion. You won’t find this distinction in the dictionary, but it taps into the connotations of the words, so I think you’ll find it’s effective even in general conversation.
A preference is I like cats. Now, we could dig deep here and get into what happens when you lie about your preferences, but let’s assume everyone’s being honest.
An opinion is cats are better pets than dogs.
There aren’t really good and bad preferences. You like what you like, and you shouldn’t feel like you need to explain that to others! Nor should others ask you to.
Many of us have learned, in one way or another, the lie that our preferences are not inherently valid. We frame our preferences as opinions (cats are great, chocolate is delicious, anchovies are gross) instead of just our preferences (I love cats, chocolate is my favorite food, I hate the taste of anchovies).
If something is truly your preference, try to accept it without qualification, and to speak about it accordingly (in “I like” and “I dislike” type statements).
You may find that the quality of your opinions increases when you no longer feel like you need to justify your personal preferences because it is entirely reasonable to hold an opinion that contradicts your preferences.
For example: “I prefer reading articles, but with the evidence that more people learn better from video presentations, I think we should invest department budget in recording more video training for our content.”
How to share a good opinion
This wouldn’t be a style guide for sensitive data professionals if I didn’t give you some language that I’ve found effective for framing good opinions. Oftentimes, those first few words are the hardest.
Here are some of my favorite go-tos:
“I recommend that we [do some action], because [of this evidence].”
“Based on [this evidence], our best option here seems to be [some action].”
“In order to [do some fundamental goal], I recommend that we [do some thing], because [of this evidence].”
“A good option here is to [do some action], because [of this evidence].”
“[Some consideration or complaint] isn’t relevant to our decision making here, because [of this evidence].”
“I’ve been thinking through [some problem] and the options for how to approach it seem to be…”
Once you get into it, try to avoid over-explaining yourself, but do give a succinct summary of relevant evidence. Pick your one to three most important pieces of evidence, and keep the rest in your pocket in case you get pushback.
Here’s options to respond to said pushback:
“I agree with your concern about [some thing]. I also looked at [this other evidence] which shows that [my proposal addresses your concern].”
“That’s a great point and something I hadn’t considered. I think [this proposal] will still be effective [based on some other evidence / if we change X thing about it]. Does that address your concern?”
“Some other things that I considered with [initial claim] are [summary of further evidence]. Do any of those address your concerns?”
Some good opinions
I would like to share some good opinions that you might not agree with, to illustrate the important point that two contradictory opinions can both be good but that great opinions usually make room for each other.
For example, consider the two conflicting opinions:
All lowercase SQL is better because lowercase is easier to read, and the small efforts of not capitalizing words add up into performance savings over time.
Uppercase keywords in SQL are better because they differentiate keywords and stay consistent with historic convention.
The discrepancy here is that the two opinions give different weights to the same evidence (convention, readability).
We can make these good opinions great by making them contingent:
For those not raised in legacy SQL [contingency 1] and who prefer readability [contingency 2], lowercase SQL keywords are most effective because lowercase words are more readable [evidence], these users aren’t concerned with legacy standards [warrant to exclude other evidence as irrelevant], and people raised in the internet age tend to perceive that all caps are shouting.
For those whose chief concern is consistency with legacy SQL [contingency 1], especially those who were “raised” in it [contingency 2], all-caps keywords are an effective convention and have only a moderate impact on readability for others.
Bonus opinion: If you run a team of SQL writers, it’s best to either (1) completely automate SQL formatting or (2) let each individual user decide when and how to capitalize their SQL as long as it’s readable because analytics engineers are an opinionated bunch and you’ll incur more trouble than it’s worth trying to force everyone onto the same capitalization convention unless you can make it absolutely 0 effort with option (1) above.
Go forth and be opinionated
If you worry about whether or not your opinions are good enough—then know, we need your opinions!
Good opinions require humility.
But they don’t require permission. Don’t wait for anyone to tell you that you’re senior enough, smart enough, old enough, tenured enough, anything enough to have an opinion.
Knowing what makes a good opinion can give you the confidence that you have one, and also help you spot some not-so-good opinions around you.
Have an opinion that’s informed, logical, and contingent? Are you willing to update it when you get new information? Great! Go for it!