The Top 5 Data Operations Posts of 2022
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The Top 5 Data Operations Posts of 2022
The top 5 posts from this year, in case you missed them. This is a special edition of "Everything Starts Out Looking Like a Toy."
Hi, I’m Greg 👋! I write essays on product development. Some key topics for me are system “handshakes”, the expectations for workflow, and the jobs we expect data to do. This all started when I tried to define What is Data Operations?
This is a roundup of the top 5 posts from this year: it’s December 29th, 2022. This year featured 52 essays (there might be one more) ranging from Purchasing Power measured by the price of a Big Mac to Selling through Memes and writing about Product Led Growth interactions.
These five stood out in driving the most unique subscriptions to the blog. They may not have all had the most views, and they drove the most impact.
Thank you for reading this year! Please consider sharing this post with a friend.
The Top 5 Big Ideas of the year
A summary of short long-form essays about data things
1. “No code” does not equal “no logic”
There’s a popular misconception that “no-code” solutions work immediately and require no configuration. In this essay, I wrote about the key components for a successful no-code implementation. No-code requires a shared data model (or a bridge to create one), an agreed-upon way to share information, and a logical graph or flow chart to know what you’re going to do.
If you don’t write down your logic, your no-code project might go nowhere.
2. Salesforce is a lousy data warehouse
RevOps peeps like to put all of their data in Salesforce. This maps well to the adage that if you’ve got a hammer, everything looks like a nail. But Salesforce doesn’t map all that well to the challenges of a data warehouse. It’s a great system of truth for Sales data, and linking that data effectively to the representation of people and companies in other systems requires a bit of work.
If Salesforce is your only data repository, your other applications might miss out.
3. You're going to get used to using AI models like ChatGPT, whether you like it or not
If you haven’t already heard about ChatGPT, it’s a new type of machine-learning model that can generate templated content or ideas based on a given input. Because tools like this save so much time, they will likely be ubiquitous soon. They are not always right and sometimes invent information. However, AI-based tools like this are here to stay.
How can you best AI tools in your work or accelerate time to value?
4. Using data to elevate features in your product backlog
At this time of the year, your product backlog is filled with ideas from the last year. You might not remember the relative (or absolute) importance of each item. This post proposes a prioritization model to rank features based on a use score (will our persona do this?) and a competitive score (do we need to build this to match to the market).
Using data to make this distinction helps you to stack rank the backlog.
5. Documentation is always out of date
Once you deliver a feature, the documentation starts to age. Great documentation mitigates this issue by describing the concepts and terms of a feature in a well-explained process. If a button or a label changes later, this gives the user a shot at knowing what to do even if they don’t know exactly what to do.
If you build a semi-automated process to update documentation, it’s less likely to be stale and misleading.
What’s the takeaway? Thanks for coming along on the ride this year. I’m looking forward to more writing in 2023. Let me know what topics you’d like to see!
What to do next
Hit reply if you’ve got links to share, data stories, or want to say hello.
Want more essays? Read on Data Operations or other writings at gregmeyer.com.
The next big thing always starts out being dismissed as a “toy.” - Chris Dixon