Project Management – EduDataSci – Educating the world about data and leadership

Functional vs. Nonfunctional Requirements: Making the Split Work in Agile

If you’ve ever shipped a feature that “works” and still disappointed users, you’ve met the gap between what a system does and how well it does it. That gap is the space nonfunctional requirements occupy—and it’s where agile teams win or lose product trust.

In this continuation of our requirements series, we’ll clarify the difference between functional and nonfunctional requirements, show how to make nonfunctional requirements measurable, and connect both to practical agile habits—user stories, acceptance criteria, Definition of Done, SLOs, and pipeline checks. By the end, you’ll have a lightweight pattern you can apply this sprint. This is where #RequirementsEngineering meets Agile and DevOps.

Agile Needs a Spine: Aligning Dates, Deliverables, Objectives, and KPIs

A recent conversation with a colleague reminded me of how important applying structure to an agile project team is. Most industries can’t take, to quote the old Blizzard line, the option to release it “when it’s ready,” because they’ve made commitments to customers or other parts of the business.

Agile frees teams from big-batch planning, but it doesn’t free them from consequences. When dates, deliverables, objectives, and KPIs drift apart, you get motion without momentum—busy sprints, delayed value, and vague success. In this piece, I’ll show why alignment across these four anchors is the operating system for agility, how to keep it lightweight, and why you can’t—and shouldn’t—abandon traditional project management entirely.

Managing Data Platform Projects the Agile Way—and Hitting Your Milestones

One of the things I’ve been thinking about lately a lot is how you formalize the type of project management that is necessary in data platforms, and what you need to do differently compared to software development projects. I brought in a collaborator, one of the best customer success managers I know, to talk about how to do this correctly.

Agile absolutely works for data platform projects, but you need a lightweight way to lock in critical choices without slowing teams down. Architectural Decision Records (ADRs) provide that spine: they capture why you chose a direction, what you rejected, and the consequences—so you can move fast and keep delivery predictable. Combine ADRs with vertical slices, data contracts, quality gates, and observable pipelines, and you can ship in short cycles while meeting real dates.