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This changed the way people coded, stored data, and thought about data in general. In my mind, the implications, not necessarily the details, were some of the m

This changed the way people coded, stored data, and thought about data in general. In my mind, the implications, not necessarily the details, were some of the most profound in the history of computational papers. For the last few years, I've been working with the premise that there is an analogous information-minimization theory for all project information, not just data storage. So far I'm optimistic that this is true --- it's just not the same minimal format for different teams working on the same problem. It's people-dependent, not data-dependent. (Obligatory link: https://leanpub.com/info-ops https://leanpub.com/info-ops ) If true, and it looks like it is, it means that teams can have "analysis compilers" that work inside the build pipeline alongside all the other tools. This leads to "continuous information deployment"

kthejoker2 8y ago
As someone who serves as a product owner for analytics use cases, I can attest that the hardest problem in Agile is accurate qualitative and quantitative measurement of progress, value, and delivery. Definitely going to check out the book.
- DanielBMarkham 8y ago
  To make it work, I had to propose a new working definition of what analysis is. The rough definition for most people is that analysis is all the "work around the work" If you're not coding you're doing analysis. I needed something more concrete than that, yet flexible enough to allow tractability on all the kinds of things teams do. Once I got that definition in place, the rest of the process of information minimization proceeded quite naturally. At that point you know what it is you're trying to minimize, ie, you have a "key" in Codd's terms. Then the trick is just minimize information and maximizing flow using key-based storage and transmission. (This also lets you look at whether your processes are working or not from a new, rational, and objective standpoint, which is a nice side-effect)

kthejoker2 8y ago

As someone who serves as a product owner for analytics use cases, I can attest that the hardest problem in Agile is accurate qualitative and quantitative measurement of progress, value, and delivery. Definitely going to check out the book.

DanielBMarkham 8y ago
To make it work, I had to propose a new working definition of what analysis is. The rough definition for most people is that analysis is all the "work around the work" If you're not coding you're doing analysis. I needed something more concrete than that, yet flexible enough to allow tractability on all the kinds of things teams do. Once I got that definition in place, the rest of the process of information minimization proceeded quite naturally. At that point you know what it is you're trying to minimize, ie, you have a "key" in Codd's terms. Then the trick is just minimize information and maximizing flow using key-based storage and transmission. (This also lets you look at whether your processes are working or not from a new, rational, and objective standpoint, which is a nice side-effect)