Use Case

Software Development Lifecycle (SDLC) Efficiency.

Efficiency is a core goal for software engineering teams— but achieving it is easier said than done due to data silos created by the tools used across the software development lifecycle (SDLC.)

As engineers, we get this. We’ve invested in processes and tools to speed the business of building software across the SDLC: ticket systems, repo systems, quality systems, DevOps, CI/CD, Value Stream Management, etc. etc. All of this aims to reduce friction in the software development lifecycle. Better collaboration, better automation, better product. This is all essential work.

But these things don’t tell us much about the efficiency of our software development lifecycle. They automate lots of manual tasks, allowing for faster delivery with fewer handoffs and bottlenecks, but they don't give us a clear view of what work is getting done across these systems and teams doing work. They are definitely not helping us determine where and how we might get better. It takes a lot of context-switching (and time) between systems to figure all that out.

Pinpoint has reduced siloed teams, siloed tools, and manual handoffs to create a more connected and fluid software development workflow. We are not just providing a unified view into engineering activity, we are using data science to analyze historical data and trends to show you just how well you are building software and highlighting areas for process improvements. Giving engineering teams answers to questions like:

  • Is our way of working good?
  • Where are we inefficient?
  • What’s our case for more investment?

To really understand this, to make the software factory “smart,” we need sensors and machine learning. Enter Pinpoint.

Software Development Lifecycle Efficiency in 3D

All those investments in tooling? They have another benefit. Each holds a wealth of data about the efficiency and performance of our software delivery lifecycle. By absorbing the raw activity data from these systems, then applying machine learning, Pinpoint can visualize engineering performance in ways not previously possible—all without asking busy teams to stop and report on what they’ve done, or to require an army of data crunchers to make sense of the results.

Our platform operates along three axes:

Software life cycle

The X-axis represents the end-to-end process and systems involved in building software. Coding is a key component of course, but software involves more than just code. There’s the work and systems to capture requests and ideas, to prioritize the backlog, and to manage its delivery; there’s the work of vetting everything we’ve built; there’s deployment and optimization—the full software development lifecycle. For Engineering Performance Management to be worth the name, we harness the raw activity data from across all these efforts and systems.

The Y-axis is the depth of performance intelligence derived, using machine learning, from the activity data harnessed. This starts with being able to compare, say, our efficiency across key metrics from this quarter to last. But ML gives the ability to do considerably more advanced performance analysis. For example, to like estimate how long certain projects or issues will take to complete —and then to recommend what work blocking those issues from getting completed.

The Z-axis shows the people Pinpoint helps. We help technology executives better understand things like performance relative to cost; we help managers run teams more effectively; we help individual engineers better see and measure their contributions. Similar to the way that advanced analytics like Sabermetrics have transformed the work of GMs, coaches, and players, we see Engineering Performance Management helping every member of the organization unlock more efficient software lifecycles, and higher overall performance.

Machine Intelligence Matters

For Pinpoint, machine learning is more than just buzzword compliance. It’s key to helping engineering teams answer:

  • How well do we deliver work?
  • What work is at risk, and why?
  • Are we getting better over time?

Machine learning is how we derive the metrics that produce actionable insights into performance. With it, a much deeper intelligence becomes available. This includes automated diagnostics and the ability to see your outliers and blockers are. Improving software lifecycle efficiency is one way of putting what Pinpoint does. We prefer a more plainspoken mission: to help people build software better.