Slow and steady creates results
"Everything Starts Out Looking Like a Toy" (#74)
Join smart, curious folks to get the Data Ops 📊 newsletter each week (it’s free!)
Hi, I’m Greg 👋! I publish this newsletter on finding data products and interesting data observations with the goal of finding patterns and future product insights. (Also, it’s fun.) If you need a background on how we got here, check out What is Data Operations?
This week’s toy: a tool from Meta to turn your kid drawings (or your own poorly drawn characters) into animated characters. Read the user agreement on this one - you might be unwittingly giving away rights to the next Peppa Pig - or it could just be a fun toy. Edition #74 of this newsletter is here - it’s December 27, 2021.
The Big Idea
A short long-form essay about data things
⚙️ Slow and steady creates results
This is the 48th essay I’ve written this year. How am I doing?
At the beginning of the year my goals were:
write 2x/week (GOAL: missed this slightly ambitious goal, which probably needed to be revised. I did write 48 posts, +77% over last year)
exceed 500 free subscribers (GOAL: missed this one as well. Maybe you’ll help me out with that, though subscriber growth has been steady, so … THANKS!)
I am happy about the output. Writing almost once a week and keeping it up for over a year feels like a win. The message is that continuing the habit of writing creates value over time.
Today, I analyzed the past 47 posts to find enduring topics. There were a few buckets that keep showing up across the writing from this year.
Process and Product
Process is the description of how you do things, preferably in a way others easily understand. Repeatable process is one way you get an improvement over time. Repetition in a process is a core building block in creating a better product: you need to be able to take a similar approach across a wide surface area to have a consistent and coherent product. Product and process appeared in almost 30% of my essays.
This essay is on driving agreement between stakeholders using written product definitions.
You can’t have effective process and product without measuring what you’re doing. Building analytics that look backward and forward means that the component pieces need to be aligned to a common denominator. I wrote about analytics about 14% of the time this year - if you include dashboards, it’s close to 20%.
This essay is on making metrics atomic and self-describing.
Determining what to build is a balance between what users say they want, what they actually do, and what they will pay for. Usability is the study of how easy it is for interfaces to be used. Usability appeared in 18% of essays I wrote this year.
This essay covers building features that match or anticipate user behavior.
Doing things automatically is a logical outcome from building a usable, amazing process and measuring whether it gets done. Since machines are good at doing what we tell them to do, why wouldn’t we build ways for them to augment the manual things in our software? Building usable automation to unlock us to do more high value work covered about 8% of the writing I did this year, though a lot more if you count it as generalized process.
This essay is on creating automation from a well-defined process.
What’s the takeaway? Take some time to review the work you did this year. Even if you missed some of the goals that you set, remember that they were based on what you knew at the time.
A Thread from This Week
A Twitter thread to dive into a topic
Excel and Google Sheets are used by hundreds of millions of people around the world every day. Why has no one built a gaming platform for them yet? Here’s a few ideas:
Links for Reading and Sharing
These are links that caught my 👀
1/ Venn Diagrams, but better … - When you compare intersecting data sets, a typical visualization you might use is a Venn Diagram. This is that overlapping circle shape you’ve seen in lots of memes, because it works better with simple than complex sets. Now, there’s a visualization called Upset that does this more effectively. Putting aside whether using the data style will make you sad, it definitely shows a more effective overlap in multiple dimensions between objects in a set.
2/ What’s in the cart? - What Instacart delivered this year. Enter a zip code around the country, and see a visualization of what was in the Instacart basket. I found the most variation around the Thanksgiving orders, though it was also interesting to see the most often ordered item in the carts by city.
3/ Whither, Stonks? - Ben Thompson explains memes for the rest of us olds. In looking back at 2021, much more happened in instant trading than in other areas of the market. The “meme stock” phenomenon made new hot stocks out of GameStop, AMC, Radio Shack, and others. But none of this was based on fundamentals. Almost all of the growth looked like crypto holders trying to find similar momentum (and perhaps, market making whales) in retail stocks.
On the Reading/Watching List
Some things to watch or read, in no particular order
Watching: “Paper Prototyping” is a technique to create early user prototypes at low cost and complexity. You can do this with great looking printouts, or post its with simple words on them. The key is to test your logic with yourself or prospective users without spending a lot of time and money developing.
Reading: The Secret History of Home Economics, on the way that women drove the creation of science-based standards in the home that could be applied to more than just home cooking.
What to do next
Hit reply if you’ve got links to share, data stories, or want to say hello.
I’m grateful you read this far. Thank you. If you found this useful, consider sharing with a friend.
The next big thing always starts out being dismissed as a “toy.” - Chris Dixon