One of the most rewarding (intellectually, in terms of what I learned, impact wise) projects I've worked on in my career was building a data playground for ads data while at Meta.
The goal of the project was to democratize access to data by giving product marketing managers self serve data tools (the data playground) to arbitrarily query ads data on every conceivable dimension, with 24 months of historical data.
It was delivered via Tableau with a drag and drop interface of dimensions and measures (a data analyst I partnered with drove this).
It required building the data piepliens and joining the underlying tables (a data engineer I partnered with drove this).
I did UX research among the target audience (Product Marketing Managers - a team I was part of and had recently led, so I was familiar with the role) and, through that research found testers and evangelists. The research indicated that the playground itself would not be enough, we'd need to deliver pre-built dashboards for the most commonf use cases. The evangelists (the three most data savvy product marketing managers on a team of 50) also became the trainers who helped with delivery and scaled usage.
In terms of (internal) GTM the ultimate recipients (end-users) of the insights from this data would be Sales and Product teams.
For the Sales teams there were both key account teams (working with large advertisers) and scaled teams (working with SMB advertisers). Rather than engage the Sales teams directly, I worked with Sales Operations teams to build distinct programs to deliver these insights and translate them into action.
For Product teams, similarly, instead of working directly with PMs, we scaled the work via the Product Data Science teams who incorporated the playground into their tools.
I'd like to package the experience of leading this project into something that balances the high-level generic approach with the specific details of how to do something similar in any organization, including recommendations of specific tools for each stage of the process eg.
- Snowplow for events tracking
- Airbyte for ETL/ELT
- Snowflake for Data Warehouse
- Metabase for dashboards
- Count for visualization
And also define the design, development, delivery and scaling process as discrete stages.
The audience are CPO/CDO/CIO or VP equivalents who are exploring how to implement this in their organizations.