Forum Discussion
Doman Driven Design for Business Intelligence
Further thinking on this..I have ended up going instead down a 'Behaviour Driven Development' route using 'behave' behave 1.2.7.dev6 documentation
Scenario: Fit Pareto/NBD model with PyMC
Given I have a dataset of customer transactions
When I fit the Pareto/NBD model using PyMC
Then I should obtain the model parameters
That could be replaced and enhanced with an ever-more nuanced description of self-evident customer value such as presenting churn predictions in the form of number of churning/non-churning customers, their likelihood of churn and their dollar values. This seems to be going down a similar route: Tutorial: create, evaluate, and score a churn prediction model - Microsoft Fabric | Microsoft Learn
once again..I will update when i get further.
Yeah, I am coming from the other end of the stick - our company has been around for a while, and we have easily 500+ data subjects and probably 1000+ different teams that do not talk to each other the way they should.
Reality tunnel refers to a myopic view on the usage of the data you own. For example Order Management owns that data subject "Order" but all they care about is to place and process the order. They are oblivious to the fact that other teams need to know details like who was the customer on the order, what currency was it placed at, which changes were made etc. There is no incentive for OM to care about that, so they don't. This then makes the reporting life of all the other teams/groups so much harder.
You bring up a great point about measuring/quantifying the value add. I'm afraid we're way too busy to even think about that (but we should).
Further thinking on this..I have ended up going instead down a 'Behaviour Driven Development' route using 'behave' behave 1.2.7.dev6 documentation
Scenario: Fit Pareto/NBD model with PyMC
Given I have a dataset of customer transactions
When I fit the Pareto/NBD model using PyMC
Then I should obtain the model parameters
That could be replaced and enhanced with an ever-more nuanced description of self-evident customer value such as presenting churn predictions in the form of number of churning/non-churning customers, their likelihood of churn and their dollar values. This seems to be going down a similar route: Tutorial: create, evaluate, and score a churn prediction model - Microsoft Fabric | Microsoft Learn
once again..I will update when i get further.
As usual, keep Goodhart's Law in mind.