Table of Contents

  1. Data transformation
  2. Data testing
  3. Implementations + deployment
  4. Documentation + metadata
  5. The modern data stack
  6. Data dream teams

Balancing creativity and proficiency as a data team

Caitlin is the Head of Analytics at Trove Recommerce, where she helps brands and retailers create circular retail models at scale – buying back and reselling their products to extend their useful life. She previously led data teams in crowdfunding and self-publishing. When she’s not optimizing single-item pricing or operations, she’s usually drinking a cup of tea and watching her chickens peck around the back yard.

Originally presented on 2020-12-13

Your data team has to produce solid data. The pipelines have to run, the logic in your transformations has to be sound, and the report has to show accurate revenue. But if that’s all you’re doing, your team is probably bored and your organization definitely isn’t getting as much value as it could out of its data.

Open-ended creative work is a huge part of the appeal of working in this field – identifying opportunities to improve processes, appeal to new customers, or build better products adds value for the organization, but it is also incredibly satisfying. One of the fundamental challenges of managing a data team is balancing the need for rigor and reliability with the team’s desire to spend most of their time creating new knowledge. How do we manage those sometimes conflicting priorities, and create tools and processes that make the balance easier?

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