Break Up Your Measures

It's important to intentionally track a vast array of measurements and metrics, and most of us don't. It's a hard problem to solve, even conceptually.

What do we do when we have a hard problem? Break it up.

I've had a lot of success in having better conversations about measurement by establishing two primary categories of measurement: Operational and Aspirational

Operational and Aspirational

Aspirational measures

Aspirational are metrics and measures related to current targets, they help us measure progress and maintain direction over time. They're the things we talk about in big annual presentations. Examples are:

- OKRs/KPIs: How you're tracking towards objectives - highly contextual

- North Star Metrics: What's driving your strategy?

- Customer and business performance metrics: Measures like NPS, Revenue, and eNPS

- DORA Metrics: Yes, these are aspirational. Focus on the operational metrics and these will come.

- Value-added time: The ratio of time dedicated towards customer-value attributable activity (can be broken down into subcategories like: Coordinating, Testing)

We want aspirational metrics to reflect changing context, goals, and evolution of the business. In general, they should always head up and to the right, but what we measure depends on what the current target is, what the strategy is, what the environment is like. These should be highly specific and tailored to your business, domain, customers, and landscape.

Operational measures

Operational (what you may call Flow Metrics) are metrics and measures related to operational performance, they help us stay on track and measure continuous improvement of the fundamentals of the organization. Examples are:

- Throughput: Quantity of work completed within a given time period

- Lead time/Cycle time: Time elapsed across a full end-to-end flow of work (Lead time) or segment of workflow (Cycle time)

- Work item age: Time elapsed since a work item was created

- Work in progress: Quantity of distinct items of work active at any given time

- Mean time to recovery/resolution: Time elapsed from discovery of an issue to resolution of the issue

- % Complete and Accurate: How much of our work meets specifications for each activity

- Flow distribution or Work item profile: What types of work is being done (Across Features, Tech Debt, Defects, Risk Mitigation, Experimentation, etc)

- DX Core 4: A big portfolio of metrics related to performance impacts

Operational metrics should aim to fall within a defined threshold but generally trend in a positive direction. There is an exhaustive portfolio of these metrics and generally every organization should be tracking all of them, but what you pay attention to and how you prioritize them will be circumstantial.

How they relate

Operational metrics are always in service of aspirational metrics. Too much focus on operations will drive your perfect business out of business. Aspirations are built on a foundation of solid operations.

How to use them

If you struggle to have better conversations about what to measure and how, maybe take a step back and get on the same page. Is this a situation for aspirational metrics or operational metrics?