How much time will you need for this new analytics project? You might want to underpromise and overdeliver.
In this episode I talk with David Jayatillake (Chief Product and Strategy Officer at Avora.com) about scoping analytics work.
Key takeaways:
- Scoping analytics work is hard, because it involves a lot of exploration and back and forth. Questions often evolve with the knowledge we gain about the data. Don't try to estimate effort in days, but simply group tasks by t-shirt size: S (super easy, less than 1 hour), M (we fully understand what we need to do, less than 1 day), L (effort unclear, could be days or weeks).
- To understand the data, we need to understand the metadata. Technical aspects like tracking implementation, lineage, freshness. But also business context such as outages, one-off events, seasonality, usual drivers of change. To make this metadata available together with the "main" data can unlock a lot of value.
- Analyses are never really finished, there's always a follow-up question. Great analysts anticipate this and "over-engineer" their solutions to allow stakeholders to explore more.
- The goal of self service is not to eliminate work for the analysts. The more data you make available, the more questions you will get. And that's a good thing!
Some tools we talked about in this context:
We also shared our favorite sources of inspiration:
Podcasts:
Blogs, newsletters:
Shitposts:
And of course you should subscribe to David's newsletter!
Find David and Valentin on LinkedIn.
