Hi, thank you for the article, I enjoyed reading it.
I'm glad to share that my data team is on the way to create values in all the 3 types, especially the Tier 3.
However, I still find Tier 2 project sometimes hard to manage both progress and stakeholders expectation. You said that "be careful about shoving this type of work into the agile framework.", so is there any other methods that you recommend for this project type? (I'm working on something similar to Marketing media mix)
Thanks for the article Ergest. It reflects well how I think about value.
To part 2, i.e. R&D, I would like to add the terminology “hypothesis testing” in which you might test (weak) relationships in your KPI tree(s) - which you described in terms of finding causal relationships. Together with the organisation’s strategy it should inform what to work on next.
Finally I would not disregard the value of “answering questions”, in case you find value in them. I like to see my colleagues as subject matter experts with good ideas and questions. Also, you can decide whether you deliver them a workable prototype with reduced effort or a production-ready “data product”.
Hi, enjoyed the classification here. I was wondering what happens when one of the R&D project do yield significant impact? E.g., a multi-touch attribution model that works well enough for the business. Should they remain as R&D, since there might be ongoing research that's still required to improve, while a good enough version "graduates" to Tier 3?
Hi, thank you for the article, I enjoyed reading it.
I'm glad to share that my data team is on the way to create values in all the 3 types, especially the Tier 3.
However, I still find Tier 2 project sometimes hard to manage both progress and stakeholders expectation. You said that "be careful about shoving this type of work into the agile framework.", so is there any other methods that you recommend for this project type? (I'm working on something similar to Marketing media mix)
Thanks for the article Ergest. It reflects well how I think about value.
To part 2, i.e. R&D, I would like to add the terminology “hypothesis testing” in which you might test (weak) relationships in your KPI tree(s) - which you described in terms of finding causal relationships. Together with the organisation’s strategy it should inform what to work on next.
Finally I would not disregard the value of “answering questions”, in case you find value in them. I like to see my colleagues as subject matter experts with good ideas and questions. Also, you can decide whether you deliver them a workable prototype with reduced effort or a production-ready “data product”.
Hi, enjoyed the classification here. I was wondering what happens when one of the R&D project do yield significant impact? E.g., a multi-touch attribution model that works well enough for the business. Should they remain as R&D, since there might be ongoing research that's still required to improve, while a good enough version "graduates" to Tier 3?