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Prioritizing Prioritization

Prioritizing Prioritization

The sprint prioritization meeting is integral to the agile process. While many people may be more familiar with meetings such as sprint planning, stand up, back log grooming, and retro, the sprint prioritization meeting often receives less attention. I suspect this is because sprint prioritization is a particularly difficult process to deploy successfully. A good prioritization process requires thoughtful ticket descriptions written in advance, a collaborative review of each ticket in the context of all of the other tickets, and the buy-in and coordination of all of the analytics stakeholders. To top it all off, you have to squeeze this process into the end of each sprint, in advance of sprint planning… There is a reason why scrum masters are typically referred to as cat herders.

Agile Analytics, Part 3: The Adjustments

Agile Analytics, Part 3: The Adjustments

Agile software engineering practices have become the standard work management tool for modern software development teams. Are these techniques applicable to analytics, or is the nature of research prohibitively distinct from the nature of engineering? In this post I discuss some adjustments to the scrum methodology to make the process work better for Analytics and Data Science teams.

Agile Analytics, Part 2: The Bad Stuff

Agile Analytics, Part 2: The Bad Stuff

This is part 2 of my 3 part exploration of the following question: are Agile engineering practices applicable to analytics, or is the nature of research prohibitively distinct from the nature of engineering? For the agile fans, in part 1 I gave an intro to agile and talked through what I like about the scrum development process for analytics. For the agile nay-sayers, in this post I explore the elements of agile that do not work particularly well with Analytics (issues range from annoyance to downright incompatibility).

Agile Analytics, Part 1: The Good Stuff

Agile Analytics, Part 1: The Good Stuff

Agile software engineering practices have become the standard work management tool for modern software development teams. Are these techniques applicable to analytics, or is the nature of research prohibitively distinct from the nature of engineering? In this post I am going to explore some of the pros of using a scrum-like work management process in analytics.