Oscar

We don’t have any specific data scientists, but of course there are analysts who focus on experimentation + basic predictive applications (demand forecasting etc)

Hey @arikf.bsky.social, the org context is a d2c startup. Sub 200 total employees, the data team is 7 people currently which includes a data engineer, 4 analysts and 2 analytics engineers.
We support most of the company from growth to manufacturing.

Any thoughts on using % of organisation benchmarks to judge a data team size?
How useful is that as an exercise? Are there any considerations to take into account?
Any recommended reading?
#databs

Depends on the formats available in the BI tool, it’s a broad term.
Classic dashboards should be more suited to “data products” showing a pulse of the business via tightly controlled metrics.
Notebooks or canvas are more suited to adhoc analysis, building these often rely on knowing sql/python.

I'll also mention, I like how Omni operates as a company, they're very interested and responsive to feedback, transparent weekly demos and shipping, founders are very customer focused (easy to say, hard to do)

+ Omni follows a similar paradigm to Looker, but better. Its modelling layers allow flexibility for folks to analyse while maintaining governance + super easy to create beautiful dashboards and vizs.
- Its early days, v hard to migrate warehouses and simple things are missing like folder structures.

There will never be a consensus, what makes a good BI tool really depends on your organisation IMO. Having said that, I am really loving Omni.
omni.co