Locally Optimistic Events

Fabio Beltramini from Looker leads a panel on how to contribute to open data projects and how to start your own! Panelists include Izzy Miller and Leigha Jarett of Looker, Thomas Vladeck of Gradient Metrics, and Chris Whong of Qri.io.

The magic of a data team is the combination of rigorous proficiency and open ended creativity. But maintaining the right balance requires building a team culture that finds time and enthusiasm for both. Focus too much on proficiency, and you’ll have accurate reports created by people who are probably bored every day and stakeholders who don’t really understand the value that the data team can add. Enable too much creativity, and you’ve got folks making decisions based on questionable data or a team that works on projects that are interesting but not relevant to the business. How do we reliably find that magic balance?

Caitlin Moorman moderates a conversation about proficiency versus creativity in analytics from this recent post: https://www.locallyoptimistic.com/post/proficiency_v_creativity.

In this conversation, Katie Bauer and the LO community discuss how peer review really works on analytics teams, focused on 4 topics:
- What is the goal of peer review?
- What is being reviewed? Code? Analyses?
- Where does review happen - where in the process and what in what tools?
- How do you ensure accountability and that the peer review process has good coverage?

Some resources noted in the talk:
The art of giving and receiving peer reviews gracefully (with matrix of code quality impact / emotional impact of review): https://www.youtube.com/watch?v=XY6eA2_2hOg

Fishtown Analytic's PR template: https://github.com/fishtown-analytics/dbt-init/blob/master/starter-project/.github/pull_request_template.md, and how they use it: https://blog.getdbt.com/how-to-review-an-analytics-pull-request/
Brooklyn Data Co's PR template: https://github.com/brooklyn-data/co/blob/master/pr_templates/dbt_modeling_pr.md

Github Suggested Changes: https://help.github.com/en/github/collaborating-with-issues-and-pull-requests/incorporating-feedback-in-your-pull-request#applying-suggested-changes
Github Draft PRs:
https://github.blog/2019-02-14-introducing-draft-pull-requests/

Slab as a documentation tool: https://slab.com/

Building your data stack requires a lot of decision-making, and a lot of conversations to get buy-in from a variety of stakeholders across the business.

In this conversation, Nelson Auner and the LO community discuss how you decide what tools to use, when to invest and when to make do with the free option, and how to deal with all the messy people issues involved in building and managing a data team.

Read the original blog post: https://www.locallyoptimistic.com/post/0-to-60-analytics-stack/