One of the major data projects I worked on was building a "customer journey" asset to stitch together and sequence each interaction a customer had with the company: sales, billing, processing, call center, website.
Each department was huge (there were many millions of customers over decades), and so each was bucketed by function. There was no universal "customer id" and this made it incredibly hard to create that sequence of data. We relied on fuzzy matching and other algorithms to get our best possible results.
Beyond the data, for the employees of those divisions they didn't really have perspective of the entire customer experience. No idea that problems in the billing department transformed into calls to the service center (which transformed to operational expense on the company's bottom line).
At any rate, some mental model of bucketing is useful for all the reasons you mentioned. But it's up to the savvy mind to create buckets well fit for the end-game solution.
Yes great example. I may not like bucketing but I guess I better follow up with a post about connections based on causality to a desired outcome soon, because that's the alternative approach which can address the cognitive overload issue and also usefully identify what needs to be done to realise the change or effect we are looking for.
Thanks for the feedback! Yes, it's a pattern I've observed often, and I try to counter it by breaking things down by their purpose and value. This enables more nuanced prioritisation decisions and avoids the issue of users and customers unnecessarily waiting for something valuable because it was artificially 'bucketed' along with other things that may have been categorically related but addressed different purposes.
I am always glad when a concept or phrase that helps me understand an issue better resonates with others. It motivates me to share my experiences and what I have learned - so others may benefit.
One of the major data projects I worked on was building a "customer journey" asset to stitch together and sequence each interaction a customer had with the company: sales, billing, processing, call center, website.
Each department was huge (there were many millions of customers over decades), and so each was bucketed by function. There was no universal "customer id" and this made it incredibly hard to create that sequence of data. We relied on fuzzy matching and other algorithms to get our best possible results.
Beyond the data, for the employees of those divisions they didn't really have perspective of the entire customer experience. No idea that problems in the billing department transformed into calls to the service center (which transformed to operational expense on the company's bottom line).
At any rate, some mental model of bucketing is useful for all the reasons you mentioned. But it's up to the savvy mind to create buckets well fit for the end-game solution.
Yes great example. I may not like bucketing but I guess I better follow up with a post about connections based on causality to a desired outcome soon, because that's the alternative approach which can address the cognitive overload issue and also usefully identify what needs to be done to realise the change or effect we are looking for.
Love this bucketing metaphore. Thanks Daniel! Found this via the "estimation does not help us..." post.
Thanks for the feedback! Yes, it's a pattern I've observed often, and I try to counter it by breaking things down by their purpose and value. This enables more nuanced prioritisation decisions and avoids the issue of users and customers unnecessarily waiting for something valuable because it was artificially 'bucketed' along with other things that may have been categorically related but addressed different purposes.
I am always glad when a concept or phrase that helps me understand an issue better resonates with others. It motivates me to share my experiences and what I have learned - so others may benefit.