#10 - Building data teams and culture

As a new content player in town, we have many firsts here at Don’t V*LOOKUP, and on today’s episode we have our first guest co-host Sara Livingston, former Head of CX for Rockerbox and all around data guru.

And we have to thank her for bringing on Celina Wong, current CEO of data consultancy Data Culture, and previously the Head of Data for 3 consumer startups, 2 of which were acquired, including TULA Skincare by P&G.

If you’re an aspiring data leader looking for a playbook for building an effective data-driven culture, this ones for you.

🎯 The TL;DR of our conversation 🎯 

1. (3:57) Effective data teams serve as the translators for data jargon across the organization. Functions like finance can be held back by not speaking the same language, yet the fundamentals remain the same. At TULA Skincare, Celina established the data team and set of metrics that brought all the departments together.

2. (6:36) In building a high-performing data culture, Celina preaches prioritization and a grounding in the P&L to “understand what’s driving the business.” After aligning with leadership on what they care about, the next most important step is to educate and empower those who are inputting and living with the data daily.

3. (11:16) Celina discusses the tradeoffs with forecasting Revenue and CAC targets as an example of expectation setting of what is actually possible. What it comes down to is picking the right north star for the business to optimize around.

4. (15:27) For all those measurement aficionados out there, MMM (Media Mix Modeling) is touched on as a timely topic for data leaders. Celina argues that adopting “as soon as possible” can provide a competitive advantage and help to measure omni-channel outcomes.

5. (30:55) Celina discusses why TULA’s adoption of data and tech became a core competitive advantage that helped in its sale to P&G.

⭐️ North Star Metric & Key Data Tools ⭐️ 

North Star Metric:

Revenue

This one surprised me at first, but as Celina and Sara both agree — if you don’t have income, you don’t have a business. There’s no profitability without Revenue. And aligning various functions requires a unified view of the business, which Revenue is a trackable and approachable metric.

Key Data Tools:

As Celina points out there are many new tools out there looking to lower the barriers for operators to leverage data, but in the meantime she’s employed the Modern Data Stack ranging from.

• ETL tools like Fivetran to move siloed data to a centralized data warehouse

• Centralized data warehouses like Snowflake, BigQuery, and AWS Redshift

• A layer that codifies business logic (e.g. version control) like dbt

• A business intelligence tool like Tableau, Looker, Sigma or Omni (this is the main interface for operators)

For more from Celina and other Don’t V*LOOKUP guests, don’t forget to check out our YouTube channel, or listen on Spotify or Apple.