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.
this conversation will be anything but quick. Just for context - how big or small is your company? What's your estimate on the number of data subjects ("objects") in your business ? How many reality tunnels do you think you have?
Good point about the short conversation!
Its a startup SaaS company with the standard set of SaaS metrics you would expect - lets say 10-20 to start with or even just the 5 or 6 mentioned below.
Your use of the term 'reality tunnel' was unfamiliar to me (Robert Wilson???) .
It is certainly an interesting concept when thinking about self-service BI but to keep it simple I would start with simply one perspective...that of the person asking for the analytical product. The acid test is what is the expected and actual incremental business value of that product and how would you measure it.
If I can propose a reference point of my CLV/churn example that assesses the entire CLV by customer at varying risks of churn each month and then measures performance against that then that would seem to be a good stake in the ground to assess a new feature or use case. This is my idea and I intend to start experimenting but I thought I would explore any existing work first.
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.OK. Thanks once again for the very positive response. Let me mock up a few visuals over the weekend and I will share.
As usual, keep Goodhart's Law in mind.