matt's blog

Shipping fast and having fun

Data is hard

I think one of the most fun parts about working in data is that it's pretty unique— it's vague, ambiguous, sometimes ill-defined. That's changed a bit in recent years, but to me it's still the "wild west" of tech.

Treating data as a product (what some would call Data Mesh), is a useful framework for creating structure, but data will always be the red-headed stepchild— data products aren't software, but solutions should adhere to software engineering best practices.

They're not marketing, but narratives need to be simple and display clear business value. Data teams need people with soft skills , but they need to be technically rigorous, too.

I think a fundamental gap in data is the ability to sell product, which is why I work in data product marketing, technical writing, and content creation, but that's a story for another post— feel free to hmu if you're intrigued.

I've written previously about feedback loops in analytics, which have been historically atrocious (but getting better), however today I'm going to make an argument for individual feedback loops in data through the use of a 30 / 60 / 90 feedback framework.

Learning to Ship Fast

My first role out of college was in financial consulting. I strived for perfection in everything I did. Even a simple email took no less than thirty minutes— I had to be sure there were zero typos and everything was worded perfectly (this predates the rise in AI, mind you).

I had this idea that everything needed to be professional and polished. This approach, while thorough, significantly slowed down my work. I would spend weeks on an analysis, only to have it torn apart on delivery. When I transitioned into my first product analytics role, I carried those habits with me.

I was terrified of making mistakes and was determined to deliver perfect work, even if it meant missing the mark on what people actually wanted. “What if a query is WRONG?!”

I lost sleep over it, worried that I would make a costly mistake. I tried to read between the lines, to figure out what stakeholders want, then craft painstakingly detailed analyses. There are two truths that went right over my head:

  1. Most people don’t know what they want,
  2. Shipping fast gets more stuff done than perfectionism.

I never stopped to consider that maybe, just maybe, the people at the top didn’t know what they wanted or that that no one is perfect and no one expects you to be perfect.

It was only when some incredibly kind and smart mentors pointed it out to me that I realized it was my job _ ****_ to grab the reigns and take charge. If I truly wanted to have an impact, I had to be the one that figured out what was important and craft a compelling analysis around it. More valuable than being “perfect” is creating valuable things and sharing them with others.

I was letting my perfectionism slow down my work and thus limit what I was able to share. How did I change? The 30 / 60 / 90 framework.

The 30 / 60 / 90 Framework

The 30 / 60 / 90 framework suggests getting feedback on a project at 30%, 60%, and 90% completion.

Since stakeholders often don't know what they want, projects are likely to be poorly scoped and vague. By delivering a MVP at 30%, we can course correct without wasting time if it's not spot on. This process often inspires stakeholders to get creative and ask additional questions, providing valuable learning opportunities.

Now, time for some quick maths: if you can achieve 80% of the result with 20% of the work, then 30 / 60 / 90 actually is 6 / 12 / 18. Ok, that might be an exaggeration. 😂

The point is, as data professionals, we should aim to deliver MVPs. Heck, as humans we should deliver MVPs, so long as we incorporate feedback into our follow-up. This approach allows us to fail fast, learn, and grow.

The Importance of Feedback

Collecting feedback regularly is crucial, but it's equally important to be smart about when and what to ask for. You can easily paint yourself into a corner by asking the wrong people or for the wrong things.

Remember, this is your work. If you disagree with feedback, give a bit of pushback with your rationale. This will let the stakeholder know your stance. That being said, repeatedly ignoring feedback is a great way to… you guessed it, never get any feedback.

Hence, you need to seek out people you trust and whose opinions you value and ask targeted questions about your work. You should also stand tall on points you believe in wholeheartedly. It’s totally ok (and often respected) to say “I’m pretty firm on that point, actually.”

Feedback is essential for growth. Work without feedback is work for work's sake. I don't get any better at writing by writing under a rock— I get better by writing, sharing, and listening to other’s opinions.

The key point is that you need to listen, but to listen, you must first be heard. That’s why visibility is so important - it opens up opportunities for feedback.

Visibility Matters

I've never been one to seek out the spotlight. I prefer to be the guy who "keeps his head down and puts in work," but that's not how you make an impact.

You have to create things that make people talk, that make a point, that are catchy. As much as I dislike it, the world isn't just merit-based.

As data professionals, we have to "play the game."

We need to figure out how to get traction behind our ideas, build consensus, and get our teams on board. This is the art of leadership and selling and to sell is human.

Visibility is a balancing act— I like to think of it likeof Office Space. Don't be invisible like Milton, but don't be overly visible like Lumbergh.

I think of optimal visibility as "enlightened" Peter. Have a healthy amount of "I don't care what anyone thinks." That is, be confident in your work. Don’t be afraid of being different than everyone else, of being who you truly are. Never let the fear of being judged stop you from sharing what you’re passionate about.

When I was sending emails with sweaty palms, I should have been getting them to an acceptable point and firing away, because really _who cares. _When I was bending over backwards to get my “perfect” analyses out, I should have been putting emojis in presentations, recording fun demos, and having a good time, because that’s who I am.

Of course, the irony is that we do still need to care, just not about trivial emails. You can't just mail it in like Peter, as tempting as that might be sometimes. The confidence and “carelessness” has to be balanced with humility and attention to detail where it matters, which is entirely dependent on the situation.

What’s next?

Decision making is about dealing with imperfect information and making educated guesses.

Just like in a job search, you'll never be 100% certain what the right company is for you, so you do your best to reach a comfortable level of confidence, say 80%, and make an educated guess.

With the guidance of mentors, my manager, and a sharp product guy, I learned to use the 30 / 60 / 90 framework for shipping deliverables, which has been an invaluable tool ever since.

I hope you can weave some variation of that approach together with confidence and a healthy “I don’t care” attitude into your next project. I’d certainly love to see it, if you’re passionate. 😀🚀