How quickly can you recover from mistakes?
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Recently I made a mistake in a daily email.
Not a typo. Those happen all the time. But a material mistake that was important. I was announcing the launch of The Technical and Strategic Data Leader course and linking to the registration page. It’s the biggest launch I’ve done with The Data Shop. But I messed up the link to the main landing page. Not once, but twice in the email. Consequentially, the first people who got the email and clicked the link were sent to a “preview” link.
Within 20 minutes someone alerted me to the issue (thanks, Cristobal!). I was confused about why the mistake was there, but then I reviewed my steps in preparing the email announcement and realized my mistake. Thankfully, I was able to make some updates and have the links working correctly after a few more minutes.
Mistakes will happen on your data team. It’s not if, but when.
Part of your work as a data leader is preparing for mistakes. How to diagnose them. How to resolve them. And how to minimize their occurrence in the future.
A dashboard is showing incorrect data.
A pipeline failed due to an incorrect parameter value.
A report fails to update due to a schema change in the source.
Users are unable to access the database because of credential changes
Do you have a history of changes that you can review to see when the error was introduced?
What was the process for reviewing the pull request?
Is there a data quality check in place for that use case?
What were the table counts before and after that pipeline run? Do we have monitoring of those jobs?
And, most importantly, how quickly can you deploy a fix to production?
These are the core questions a great data team can answer.
They have CI/CD, source control, data quality processes, etc.
They have processes to handle mistakes.
Great data teams aren't surprised by mistakes.
They expect mistakes to happen.
Are you ready for the next mistake?
I’m here,
Sawyer
from The Data Shop
