Happy New (Data) Year!
"Everything Starts Out Looking Like a Toy" #179
Hi, I’m Greg 👋! I write weekly product essays, including system “handshakes”, the expectations for workflow, and the jobs to be done for data. What is Data Operations? was the first post in the series.
This week’s toy: some thoughts on what makes a really good toy. It starts with design, but the most important element? The ability to be flexible and support many kinds of play. Edition 179 of this newsletter is here - it’s January 2, 2024.
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The Big Idea
A short long-form essay about data things
⚙️ Happy New (Data) Year!
Happy New Year!
Here’s an entirely unscientific list of trends I think will be important in 2024 for data teams.
Data Operations As A Service
If you don’t already have a dedicated Slack channel to ask for help from your #data-ops team, you will have one in 2024. What’s the idea of data operations as a service?
Think about all of the “not quite right” data situations that happen during a typical day or week. Often they are dismissed as a one-off anomaly and not addressed until they happen in the future. By placing these requests in a central comms channel, you see them get raised, discussed and solved transparently.
Data Ops as a service implies this is a shared capability for all the teams in the company and works more like Customer Ops, Sales Ops, or the other ops teams.
Measure by: questions resolved, time to resolution
GTM Team Support
Data teams are most effective when they solve meaningful problems for the business. 2024 is going to have a renewed emphasis on supporting specific issues for GTM teams.
These solutions look like:
Building automation to distribute accounts
Creating pacing graphs to help GTM teams react to pipeline progression
Responding quickly when an error happens in Salesforce
Measure by: time for a new rep to reach effectiveness, distribution funnel among reps
Roadmaps for Data Products
Data Ops teams can step up to building Data Products by building an understandable sequence of features and products. Making this predictable for the business makes it a lot easier to improve how data is used.
What kind of features belong in a data ops road map?
New capabilities such as building models to support analytics or drive decision-making
Remove rough edges by fixing issues that slow down salespeople or marketers
Measure by: value created by new capabilities, reduction in “rough edges” questions and errors
Thinking Ahead for Analytics
The most important output for analytics? Trust.
New reports need to fulfill the same criteria as existing ones, which means they need to be based on models that will produce the same results wherever they are used. These new reports cannot contradict existing reporting, or else they will result in lowered trust.
How do you think ahead? Identify the base units you measure in existing reports and use those to test new reports under development.
Measure by: report views
Data Operations for Product
If you have a roadmap for Data Ops and are building Data Operations as a service, could some of these innovations prototype product features?
These data ops improvements probably won’t be direct feature prototypes, but squint and you can see:
histograms of customer behavior => intent for which features are popular
conversion rate analytics => information to inform adjusting screens or flows
Measure by: feature usage reports, speed to build reports that don’t need to be built by engineering team
What’s the takeaway? Although we don’t have a crystal ball to see what will happen this year, focusing on a repeatable service to remove problems and increase capabilities is a great goal for data operations teams.
Links for Reading and Sharing
These are links that caught my 👀
1/ Writing code === writing prose - A brief essay by Michael Hart on the similarity of writing code and writing prose. The item that caught my eye?
“It’s very easy to write unreadable code, it’s far more difficult to write code that another developer can easily understand.”
Writing is an art. So is writing good code.
2/ Why is Apple consistently good? - A persuasive analysis of Apple by Fernando Villalba and why they continue to excel. Apple carefully picks where to compete, controls every aspect of that product, and then builds other products that interact with that one. (It’s not just “make it simple”, but that helps too.)
3/ Concept car still looks fresh - Take a look at this car design from 1999. It looks pretty contemporary for 2024. With slight changes you can almost imagine it in production. I feel like we see many fewer daring concepts these days.
What to do next
Hit reply if you’ve got links to share, data stories, or want to say hello.
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The next big thing always starts out being dismissed as a “toy.” - Chris Dixon