Reporting as a Delivery Manager

Reporting as a Delivery Manager

How can you effectively report as a delivery manager?

My career in IT has spanned several roles from software testing to business analysis and managing teams. As a test manager and also a delivery manager, I have experienced so many different variations of ‘reporting’ for senior managers at all levels, from reporting for a large monthly ops pack to checking in at the team level. Whatever the level of reporting, the same main issue will arise: the information requested is not available at the click of a button.


The problems with reporting


The first problem: finding the motivation to report

When delivery managers are faced with a complex situation like this, motivation to get these reports ready quickly falls off a cliff because they might need to take screenshots, export data, and add it to a spreadsheet… the list is endless. A lot of the time the request to report is too generic and doesn’t take into account how a team might work (e.g. a support-based team working on fixing bugs being asked to generate the same report as a product-based feature team working on well-defined requirements with an agreed estimate).


The second problem: reporting commentary and context

Whilst I am a firm believer in providing context to reports, this can also be a laborious task. But, if the data was easy to get hold of, clear and self-explanatory (to some extent), then that small amount of time needed to put some context around the data is immediately easier to find for whoever is creating the report.

We all know that any report or metric is only as good as the information that is put into the tool or application, but that’s the problem delivery managers have always struggled to solve: how can you get access to the actual data rather than trying to recreate it in another way, shape or form?


The solution: Plandek’s clear path to easy reporting

According to new users who sign up for Plandek, the first thing they notice is how useful the integration capability is.

If, for example, you were to report on velocity, burn downs, and Sprint Target Completion every day, you would be able to link the velocity changes to the time it takes my engineers to review a PR. This can then spark up a conversation with an engineering manager to see if there needs to be a review of the PR review process because perhaps velocity is slowly decreasing. Or users can see what the actual lead time of a feature is by incorporating the deployment data with Jira (or Azure) data.

Another huge benefit is being able to tailor the view of data depending on the audience. Rather than exporting data out of a tool into a spreadsheet or a PowerPoint slide, users can create a dedicated view for that stakeholder that means something to them. It’s even possible to give them access to Plandek so they can have a look whenever they want, meaning they don’t have to wait for email responses or meetings with delivery managers.


An example: Ticket Flow

I’m a big fan of Scrum and one of my favourite metrics is Ticket (or Story Point) Flow. I use this for the current sprint but also as a discussion topic for retros by looking at the sprint that has just finished, as I can see if there are bottlenecks in the process thanks to daily breakdowns of the flow.

I can then discuss that with the team and we can create actions to improve Ticket Flow which will then lead to an increase in Cycle Time and ultimately Customer Lead Time to Value.

Ticket Flow Metric
Example Plandek Ticket Flow Metric chart


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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