Top 5 Value Stream Metrics for Teams and Managers

Top 5 Agile Metrics

What are the top 5 Value Stream Metrics for teams & managers?

DevOps Value Stream Management is a rapidly growing field in Agile software delivery, as it recognises that the trick to delivering valuable software is to treat value delivery as an end-to-end process that requires mapping and optimising for the best outcomes.

Indeed, Gartner only started covering Value Stream Management as a discipline in September 2020 with the publishing of its first Market Guide in the space.

As the name suggests, effective Value Stream Management requires identifying (mapping) and managing individual software delivery value streams. Metrics are central to effective management, to track and drive the desired value delivery.

But in keeping with Agile principles, the Value Stream Metrics chosen must engage both teams and managers. Measurement in Agile delivery has always been a contentious area and, as such, should not be a top-down exercise (imposed on teams) – rather, it should be a bottom-up exercise driven by the teams themselves.

So, what are the Top 5 Value Stream Metrics that engage both teams and managers alike?


Criteria for choosing our Top 5 Value Stream Metrics

We have chosen the following criteria to select our Top 5 Value Stream Metrics:

  1. The chosen metrics track the core underlying objective of Agile software delivery (and Value Stream Management).
  2. The chosen metrics are valuable to teams and managers alike, meaning Value Stream Management is universally adopted rather than a management concept separated from the day-to-day reality of software delivery within the teams themselves.
  3. The metrics are meaningful when considered at the team level but also when aggregated across teams.
  4. The metrics are simple to understand and can immediately drive significant improvement in Agile software delivery effectiveness.


Our Top 5 Value Stream Metrics

In no particular order, here are our Top 5 Value Stream Metrics that make an immediate impact on your Value Stream Management (i.e. effective Agile delivery of valuable software). As would be expected, they closely match commonly used Agile metrics.


Lead Time

Lead Time measures your velocity, or the time taken to develop an increment of software, and is a core Value Stream Metric as it is a basic measure of your organisation’s Agility and ability to deliver value at pace.

Lead Time refers to the overall time taken to deliver an increment of software from initial idea through to deployment to live – i.e. the complete end-to-end Software Delivery Life Cycle (SDLC). Cycle Time is a subset of the overall delivery time, typically measured as the time from the start of work (development) until deployment to live (traditional Cycle Time) or from code commit to production (sometimes referred to as Code Cycle Time).

A Lead Time can be calculated for any increment of work, such as a story, task, or bug.

The example below shows the Lead Time for Stories and so relates to the time taken to deliver new features. As the chart shows, the total Lead Time is 11 days (which is actually very good) and represents the time taken for a Ticket to leave the backlog until deployment to live.

Lead time for stories metric
Example software delivery Lead Time summary chart, Plandek Dashboard


Lead time for stories metric
Example Lead Time status review chart, Plandek Dashboard


As shown in the expanded view of Lead Time in the Lead Time Status Review chart above, analysis of Lead Time should become an integral part of effective Value Stream Management (VSM).

The chart shows the active and inactive status groups and the time stories spend in each status across the total 11.3-day Lead Time.

In the example above, inactive statuses such as ‘Ready for Development’ account for a relatively large proportion of the overall Lead Time (5.6 days). Hence, there is friction in the process, and there is scope to reduce the overall Lead Time and thereby increase software delivery velocity and the speed of value delivery.


Deployment Frequency

Deployment Frequency is another fundamental measure of an organisation’s value delivery (when viewed alongside the other critical Value Stream Metrics described here).

A core objective of Value Stream Management is the ability to develop and deploy live small software increments rapidly. Deployment Frequency is a Value Stream Metric that tracks that base competence and is a powerful Value Stream Metric around which to focus effort at all levels in the delivery organisation.

Deployment Frequency Metric
Example Deployment Frequency metric view, Plandek Dashboard


Delivered Story Points or Delivered Value Points

Delivered Story Points is often considered a problematic Value Stream Metric due to the potential inconsistencies in the calculation of story points and how much effort they represent. However, as a basic measure of output and how that is changing over time, it is a powerful Value Stream Metric around which to align.

Indeed, some organisations may have gone further and are working on Value Points rather than story points: each Value Point is aligned with the perceived value of the story under development. In this instance, the Value Stream Metric becomes Delivered Value Points.

There may be concerns about teams ‘gaming’ the metric with story point/value point inflation, but as with all Value Stream Metrics, they should be viewed in context by experienced folk who know the teams well. If this is the case, they can still give an excellent view of how the delivery organisation is progressing over time.

Delivered story points
Example Delivered Story Points metric view, Plandek Dashboard


Escaped Defects

Escaped Defects is a simple but effective Value Stream metric of overall software delivery quality (which is inversely correlated with the delivery of value).

It can be tracked in several ways, but most involve tracking defects by criticality/priority, as per the example below.

Escaped defects metric
Example Escaped Defects metric view, Plandek Dashboard


When these four simple Value Stream delivery metrics are viewed together, the Agile DevOps practitioner can get a well-balanced view of how their Value Stream Management is progressing.

Importantly, the Value Stream Metrics can be tracked over time, making sure that an improvement in one metric (e.g. Lead Time) does not lead to a detrimental effect on another metric (e.g. Escaped Defects).

In addition, the relationship between Lead Time and Deployment Frequency can be closely watched. Very often, teams can reduce their Lead Time, but this does not translate into quicker value delivery due to bottlenecks in the integration and deployment process.


Flow Efficiency

Our final Value Stream Metric in our Top 5 is Flow Efficiency. Flow Efficiency looks at the proportion of time tickets spend in an ‘active’ versus ‘inactive’ status and is a great Value Stream Metric for teams.

The Flow Efficiency analysis (see Figure 7 below) enables Team Leads to isolate and analyse each ‘inactive’ status in the workflow and consider if there is scope to reduce or eliminate it.

The analysis shows the relative size of each ‘inactive’ status opportunity in terms of time spent in the inactive state and the volume of tickets affected.

Flow efficiency metric
Example Flow Efficiency metric, Plandek Dashboard


Typical opportunities to remove inactive bottlenecks include time spent with tickets awaiting definition (e.g. Sizing) and tickets awaiting QA. Where waits for QA are considered excessive, Delivery Managers can reconsider QA resource allocation regarding each team.


About Plandek

Plandek is a global leader in software delivery metrics and analytics, recognised by Gartner as a top nine global vendor in their DevOps Value Stream Management Market Guide (published in Sept 2020).

Plandek works by mining data from toolsets used by delivery teams (such as Jira, Git, CI/CD tools and Slack) to provide end-to-end delivery metrics & analytics to optimise software delivery predictability, risk management and process improvement.

Plandek is based in London and works with clients globally to apply predictive data analytics and machine learning to deliver software more effectively.

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