If there’s one thing I’ve learned from working with leading engineering teams, it’s that building a data-driven developer productivity initiative is never as simple as it sounds. You pick the metrics, set up the dashboards, and then?
Confusion.
Suddenly, the team is asking: “Wait, what does ‘in progress’ really mean, anyway? Does it start when we open a Jira ticket or when someone has an idea?”
Even the biggest champions of the initiative find themselves grappling with the same issue: “Cool, data… now what?” The real challenge isn’t the metrics themselves, it’s standardization.
The Battle for Standardization
I’ve seen teams trip up on this at every scale. Obtaining the metrics is usually fine. Tools like LinearB or other platforms make it easy to grab essentials like cycle time or change failure rate (CFR).
To solve this problem, you need a balance two things:
- teams must have the flexibility to define how metrics are measured within their workflows…
- … but those measurements must also be consistent enough to roll up into a unified view for executives
Take the “in progress” example from before. One team might define it as the moment a Jira ticket is assigned, while another might start the clock when the first commit is made. Both approaches are valid, as long as they align with the company-wide definition of what “in progress” means.
This approach creates clarity at the organizational level while allowing teams to work in ways that suit their unique workflows.
A Rock and a Hard Place
This balance, flexibility for teams and consistency for leadership, is where the challenge lies. It sounds simple in theory but is incredibly hard to implement at scale.
For example, metrics like MTTR (mean time to resolution) often need to pull data from multiple sources: bugs in Jira, incidents in PagerDuty, continuous delivery failures, and more. Without a system that can unify these inputs into a consistent metric, you end up with confusion at the reporting level.
This is where configurability becomes critical. A robust platform should allow teams to integrate their preferred tools and processes while adhering to company-wide definitions that leadership can trust.
LinearB excels here, offering enterprise-grade configurability that no one else can match. It empowers teams to adapt metrics to their workflows while delivering consistent, actionable data to executives, something other platforms simply can’t do at scale.
This is why standardization is hard:
- To make metrics effective, you need to respect the nuances in how different teams work, while still aligning on a shared company-wide understanding.
- Without this alignment, you risk disengaging your developers and turning metrics into a source of frustration instead of improvement.
- It’s not about uniformity, it’s about creating a shared language so you can actually use the data.
