"Discard everything that does not spark joy." --Marie Kondo
As companies like the one I work for prep for the new fiscal year with a new round of planning, initiatives, and outcomes, I find myself in need of a retrospective.
- Our goal morphed a little bit. But we are still focused on enabling a data-driven culture for our product teams.
- I need to focus more on what it means to be truly data-driven. I need to tell better stories to better educate.
- It's time for spring-cleaning. I've accumulated a mess of stories, requirements, research notes, documentation, and more.
It's my favorite thing in the world. When accompanied by Flow.
But for all people, it's hard to find time. We juggle so many priorities, and have more great ideas than we will ever have time to truly accomplish.
So how could we possibly focus?
That's where data comes in.
But for the data to be valuable, it needs to be reliable and clean.
Spring is around the corner.
That means, at a personal-level:
It's time to Marie Kondo my initiatives list
While we've had a lot of successes to boast about, 2020 was also a rough year for many of our teams.
From my perspective, I have a FY20 initiative that is not being met, mostly due to lack of resource organization/allocation.
That's not to say that the team hasn't been able to get anything done. (Check out my co-worker's exciting work around Modern Data Pipelines in the Google Cloud).
But we're not moving at the speed and scale that we need.
This is not an official list. But from my perspective:
1) We need a team of dedicated developers
At least 2 developers to start with. Our data insights team was sized conservatively based on the roadmap we had planned. This is what we hd
2) We need an Enterprise Data Platform
Check out my draft. We need proper resources to complete this work.
- Enterprise-Scale Centralized Data Management for Decentralized Teams
- Enterprise-class security and governance.
- Multi-function data analytics.
- An elastic cloud experience.
- Hardened silos.
3) We need a Data Governance Program
And the proper resources to support one.
- Data Program Manager
- Data Governors
- Data Security Officers
- Data Stewards
- Data Analysts
- Data Catalogs
- Data Engineers
- Data PM's
4) We need AI-powered insights
Any small team of analysts can only get you so far.
An AI powered future is as limited as the number of questions you're able to come up with.
5) We need to foster curiosity
We need product teams to not only be aware of metrics.
But we need them to be asking
My team can't do this on our own
But we're heading in the right direction.
We're working horizontally across silos, even in the larger org.
- What does it mean to be data driven?
- What does a mean to NOT be data-driven? Factless stories that may get a lot of attention, but have little measurable value.
- Data Scientist vs Data Anaylsts
- How to work with a data analyst
- OKR's - Notes on "VENTURE CAPITALIST JOHN DOERR: MEASURE WHAT MATTERS"