Product Management Intro

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• Product Management Research

Key Concepts

Product Vision, Strategy and Principles

These three layers help align teams and stakeholders on why the product exists, how it will win, and what behaviour is non-negotiable when making trade-offs.

Layer	Purpose	Horizon	Example
Product Vision	The long-term impact or North Star; why the product exists.	Years	"Enable every small business to compete with big brands through data."
Product Strategy	How the product will win in the market; where to play and how to differentiate.	1–3 years	"Win in SMB by being the easiest to set up and the most transparent on pricing."
Product Principles	Guardrails for decisions; what we optimise for and what we won't do.	Stable	"We prioritise clarity over cleverness." / "We never use dark patterns."

Vision and strategy inform roadmap and ; principles help when prioritising features, design choices, and partner deals, speaking the nature of the products you want to create and the values you want to uphold.

Example: Principles in practice

If a principle is "We never use dark patterns," then a proposal for a pre-checked "opt in to marketing" box is out of scope, regardless of short-term conversion gains. Principles make tough calls consistent.

By company stage

• Startup: Vision might be a single sentence; strategy may live in the founder's head. Writing down 3–5 product principles early still pays off when the team grows.
• Growth / Established: Document vision and strategy in a living doc (e.g. one-pager or wiki). Revisit strategy at least annually; principles rarely change unless the company pivots.

DORA Metrics

stands for DevOps Research and Assessment, a long‑running research initiative originally formed to understand what makes software teams high‑performing. It is now part of Google Cloud, continuing to publish the annual . DORA’s research spans more than a decade and includes data from tens of thousands of engineering professionals worldwide. The program identified four key software delivery performance metrics (the “DORA metrics”):

Metric	What It Measures	Why It Matters	Elite Benchmarks (2026)
Deployment Frequency	How often code is deployed to production	Indicates delivery velocity	On-demand (multiple/day)
Lead Time for Changes	Time from code commit to production	Process efficiency	< 1 hour
Change Failure Rate	% of deployments causing failures	Release quality, stability	0 – 15%
Time to Restore Service	Time to recover from production failure	Incident response effectiveness	< 1 hour

These metrics measure both speed (throughput) and stability, showing that elite teams excel at both. DORA’s findings link strong engineering practices and healthy team culture to better organizational performance, including profitability, productivity, and customer satisfaction. The metrics are widely used across the industry as a standard framework for assessing DevOps maturity and guiding continuous improvement.

Tools to Help with DORA Metrics

You can integrate tools like LinearB, Haystack and Sleuth with your source control (GitHub) and project management (Jira/Azure DevOps). Even a simple dashboard in Jira that tracks "Issue Created" to "Production Release" can show Lead Time.

The Three Ways

For more information on the Three Ways, see The Phoenix Project

1. The First Way: The Principles of Flow (Backlog Hygiene & Small Batches) - This is about making work visible and moving it from left (Dev) to right (Ops) as fast as possible. For a PO, it means prioritizing ruthless backlog grooming, breaking down large features into smaller, manageable chunks, and making work visible. If you've helped reduce "work in progress" or made sure tasks didn't sit waiting for weeks, that's demonstrating flow.
2. The Second Way: The Principles of Feedback (Telemetry & Rapid Loops) - This is about creating, shortening, and amplifying feedback loops from right (Ops/Users) to left (Dev). As a PO, you'd ensure your team is using analytics tools to understand how users are interacting with features. Getting user feedback early and often, and then rapidly incorporating that into the next iteration, is key. Think about how you've gathered data to validate a feature or quickly rolled back a change that wasn't working.
3. The Third Way: The Principles of Continual Learning (Spikes & Retrospectives) - This is about creating a culture that fosters two things: continual experimentation (taking risks and learning from failure) and understanding that repetition and practice are the prerequisites to mastery.

Tools to Help with the Three Ways

For The Second Way, you can use tools like Mixpanel or Amplitude to understand how users are interacting with features. For the Third Way, you can use tools like Spikes to help with continual learning.

Prioritization Frameworks

With limited resources and endless feature requests, structured prioritization is essential. The most effective frameworks include:

Framework	When to Use	Key Components	Pros	Cons
RICE	Feature-level prioritization with quantifiable metrics	Reach, Impact, Confidence, Effort	Data-driven, objective	Can be time-consuming, subjective estimates
MoSCoW	Sprint planning, stakeholder communication	Must-have, Should-have, Could-have, Won't-have	Simple, aligns teams	Can be vague, subjective
Cost-of-Delay	Strategic decisions, opportunity cost analysis	Business value × Urgency	Focuses on value, urgency	Requires accurate value estimation

For outcome-based planning and aligning roadmap to goals, see Outcome-based roadmaps and OKRs in Planning and Design.

Suggested PM Tool Stack

Concept/Area	Essential Tools/Technologies	Why It Matters for PMs
Operating System	Linux (Ubuntu)	Most workloads run on Linux; basic fluency is critical.
Version Control	Git (GitHub)	Foundation for code, infrastructure, and config changes.
Scripting/Automation	Bash, Python	Enables automation, rapid prototyping, and debugging.
Infrastructure as Code (IaC)	Terraform	Standard for provisioning cloud infrastructure.
Configuration Management	Ansible	Ensures consistent, repeatable server configuration.
Containers	Docker	Standard for packaging and deploying applications.
Container Orchestration	Kubernetes	Industry standard for scaling and managing containers.
Package Management	Helm, Kustomize	Simplifies Kubernetes app deployment and customization.
Continuous Integration & Deployment (CI/CD)	GitHub Actions, Azure DevOps, Argo CD	Automates build, test, and deployment pipelines.
Observability & Monitoring	Prometheus, Grafana, ELK/EFK, OpenTelemetry	Enables proactive monitoring, troubleshooting, and improvement.
Cloud Platforms	AWS, Azure, GCP (pick one to master)	Deep expertise in one cloud is more valuable than shallow knowledge of many.
Security / DevSecOps	Snyk, Trivy, Checkov, OPA	Integrates security into the pipeline ("shift left").