‘Value stream management‘ (or ‘DevOps value stream management’ as it is referred to by Gartner) refers to the end-to-end software delivery process and the importance of managing it effectively from end-to-end as an integrated process of delivery of value for the customer – hence value stream management.
Value stream management is, therefore, a key enabler of effective Agile software delivery and is in keeping with the core principle of Agile software delivery – “the early and continuous delivery of valuable software”.
Value stream management involves the orchestration of the various toolsets and processes that underlie the end-to-end software delivery process or software delivery lifecycle (SDLC). There are various value stream management platform vendors that provide this orchestration layer, and a common theme among them all is the recognition that software delivery metrics, agile metrics, and engineering metrics play a key role in effective value stream management.
Plandek Value Stream Management metrics and analytics
As a provider of end-to-end value stream metrics, Plandek’s value stream management dashboards are therefore seen as a key element of effective value stream management, and Plandek is recognised by Gartner in its 2020 Market Guide ‘DevOps Value Stream Management Platforms’ as one of nine key global vendors in the space.
Indeed, Plandek is the only vendor that enables you to install an end-to-end value stream metrics tool without additional (and costly) value stream orchestration software.
Plandek works by mining data from toolsets used by delivery teams across the value stream (such as Jira, Git, CI/CD tools, and Slack) to provide end-to-end delivery metrics and analytics to optimise Agile software delivery dependability, risk management and process improvement.
Mining data from multiple toolsets provides a unique perspective, enabling Plandek to identify bottlenecks and opportunities for improvement throughout the value stream (in design, development, integration, test, and deployment processes).
Effective value stream management: creating a hierarchy of value stream metrics that everyone understands
Plandek can surface many different value stream metrics – the trick is finding a hierarchy of value stream metrics around which technology leadership and the wider delivery organisation can align.
The Plandek Customer Success team works closely with clients to identify a simple set of ‘North Star’ value stream metrics to sit at the top of the metrics hierarchy.
The ‘North Star’ value stream metrics are carefully selected to be meaningful when aggregated and illustrative of effective Agile software delivery across the entire value stream. They will vary according to each client’s priorities and objectives, but typical examples of ‘North Star’ value stream management metrics are shown in the table below.
These North Star value stream metrics are designed to be adopted by the technology leadership team to set the direction for the value stream management effort across all delivery teams.
A typical Plandek value stream management metrics dashboard is shown below. It shows a typical set of ‘North Star’ value stream metrics that technology leadership can sponsor and around which the delivery organisation can align.
Cascading value stream management metrics across the delivery organisation
Value stream management metrics must be also tracked and managed at the delivery team level to drive a culture of continuous improvement across the value stream.
Plandek team-level value stream metrics dashboards are customisable and are used by delivery Team Leads. Typically, team-level value stream metrics dashboards will include a range of delivery metrics, agile metrics, and engineering metrics in key areas such as:
- Value delivered metrics
- Delivery efficiency metrics
- Delivery Dependability metrics
- Backlog health metrics
- Delivery and engineering quality metrics
- DevOps metrics
The example team-level value stream metrics dashboard above shows a range of delivery efficiency metrics, including First Time Pass Rate, Mean Time to Resolve Pull Requests, Speeding Transitions Rate, Product Release Burndown, Cycle Time for Stories and Flow efficiency.
All of these metrics directly impact delivery efficiency and delivery velocity and are, therefore, popular choices in value stream management dashboards at the team level.
Driving continuous improvement using value stream metrics – Lead Time
Effective value stream management involves teams tracking and managing improvement in critical value stream metrics as part of a structured continuous improvement program sponsored by technology leadership across the entire value stream.
Lead Time (also known as Time to Value) is a popular choice as a key Value Stream metric around which to drive continuous improvement, as it underpins the core objectives of effective Value Stream Management.
Plandek allows the detailed analysis of Lead Time to understand where in the delivery process there is an opportunity to reduce time to value. The drill-down Lead Time metric view allows teams to understand the time spent in each status within the end-to-end delivery cycle (i.e. across the DevOps value stream) to understand where time is lost, and velocity may be increased without impacting quality and/or adding resources.
The flexible analytics capability and powerful filtering allow analysis by Status, Issue Type, and Epic (and any other standard or custom ticket field), all plotted over any time range required.
Teams can also adopt additional (related) value stream management metrics, to identify opportunities to further reduce Lead Times. Examples include:
- Flow Efficiency (%) (which looks at the proportion of time tickets spend in an ‘active’ versus ‘inactive’ status)
- Mean Time to Resolve Pull Requests (hrs)
- First-Time Pass Rate (%)
- Build failure rate (%)
- Deployment frequency
Metrics such as these are easily viewed in real-time using customisable Plandek value stream management metrics dashboards – and are often included in broader continuous improvement programs across scaled Agile value streams.
