Metadata
- Author: Taylor Brownlow from More Than Numbers
- Full Title:: A Study of Embedded Analysts
- Category:: 🗞️Articles
- Document Tags:: data culture, Data culture,
- Finished date:: 2024-03-13
Highlights
how he runs a central analytics team with embedded analysts across the business. (View Highlight)
Scaling is difficult: without a centralized presence it’s difficult to scale common activities, or even have visibility into those common activities (View Highlight)
No real authority: since a data leader is a dotted line manager at best in this context, it’s hard to have much say in what embedded analysts do (View Highlight)
We have a central data team with 2 analysts and 2 analytics engineers. Then across the business, 21 Data Gurus (embedded analysts) work within different teams on a group of 280 employees (Protolabs Network). (View Highlight)
The analyst is in charge of cascading the input of the data guru to our project board and aligning priorities with the directors. The analysts are responsible to educate and coach the data guru to become self-sufficient where possible. (View Highlight)
Some departments work well with a Data Guru at a level 2 or 3 though - it just depends on what’s needed. (View Highlight)
How do you decide what they work on? If not you, then who does decide what they work on? We meet with each department lead once a month set the expectations and projects for that month, and decide what the Data Gurus will work on. It’s expected that Data Gurus spend 20% of their time on analytics, and the rest is spent on the other parts of their job. So ideally at the start of the month, we have a rough idea of the major projects they work on, but it’s still up to their department leads what exactly they work on. (View Highlight)
Maintain the Looker folder for their department, removing unused dashboards and ensuring the quality of existing dashboards (View Highlight)
We run regular trainings with Data Gurus and host socials with the central analytics team and the Data Gurus. We also work closely with Data Gurus on their work - it’s common for them to ask for help on certain things or ask for advice on a certain approach. (View Highlight)
The second is workload. We get to focus on the foundations - the pipelines, the data quality. We hardly ever get that request ‘I need a report tomorrow’ because that’s never on us (except from top management who don’t have their own data guru). (View Highlight)
What are some challenges you’ve faced with this model? The big challenge at the moment is if the Data Guru doesn’t have time to work on something, that falls back on us, and we rarely have time to do those things either. Another challenge is keeping the data gurus educated on the latest developments in our pipelines. New data is being added every month, definitions are updated and it can be challenging to keep up. (View Highlight)
Do you think this changes how the rest of the organization perceives the value of the central data team? People can take it for granted that the data keeps flowing and is reliable. That’s why we make sure we’re adding value with our advanced analyst capabilities as a data team too. This is a skill our embedded analysts don’t have and can make a big impact on the business. (View Highlight)