30-50% improvements in engineering cycle times
10-20% improvements in feature development times.
10-20% more engineering effort devoted to feature and platform work
non-value added defect fixing work.
Real time visibility into slowing and stalled work to improve situational awareness and let teams spot problems and react faster.
In a nutshell, we give you all the data you need to improve the flow of work and the flow of code, throughout your product development process, from ideation through adoption, and show you how to use it to make precise, targeted improvements in your product development process.
Product development work spends 50% or more of the time waiting in queues.
Typical reasons are multi-tasking, handoffs between people and teams, code reviews, unclear requirements, inefficient branching strategies, and manual QA and release processes.
All these manifest as queueing delays.
Work stalls, but it's not always clear why.
If you are not running a shop with modern software engineering practices, almost all of these delays will be due to poor flow of work at the back end: in manual QA, defect flow-backs, and release processes.
But even if you run a modern shop, queueing delays on the front end of the process can account for over 80% of the remaining process time.
Here, poor information flow manifests as queueing delays.
Reducing these delays is a necessary pre-requisite to improving the end-to -end flow of value in product development.
Eliminating them in the right places means more gets done with the same number of people,
and the work gets done faster.
All with little or no additional investment.
We'll show you how to do that, with hard numbers to back it up.
Polaris is a best-in-class SaaS measurement and process analytics platform
that models the flow of work and the flow of code throughout the product development process with real time updates any time someone pushes code or updates a pull request or ticket.
A lot of invisible work in software development happens at the code level, and you need to make this work visible and observable to understand where the non-value-added delays are.
Our techniques are based on mathematically sound, and well-understood principles from queueing theory that have been successfully applied in many fields over the last century.
Polaris takes low-level engineering signals from your delivery toolchain and updates the parameters of a queueing network model in real time.
This lets you visualize the flow of work in granular detail, and helps identify critical bottlenecks and non-value-added delays in every part of your process.
Polaris simplifies the process of gathering and abstracting the low-level data that drives the high-impact improvements you make to your product development process.