Delivery Performance
8 minutes reading time

Is Bad Allocation of Time Investment Slowing Your Software Delivery?

Is Bad Allocation of Time Investment Slowing Your Software Delivery?

A few years ago, growth was about hiring more people. Today, growth is about efficiency—doing more with less. As an Engineering Leader, you must align your teams to achieve business goals while improving efficiency. Not every organization has the luxury of hiring more people, so what do we do? We capture each source of inefficiency and thrive on removing them.

After working with several organizations over the years, we have noticed common pitfalls that slow teams down. More importantly, we prepared a list of recommendations to avoid them.

5 Common Pitfalls That Drive Your Software Engineering Efficiency Down

Bad Allocation of Time Investment

Aligning efforts to achieve business goals while operating efficiently is a challenging mission for an engineering leader. You must prioritize creating new value, keeping the lights on, and improving things. Do you spend too much time on bugs or infrastructure work? Is there shadow work that increases time spent on keeping the lights on and thus not spent on priorities?

How to Spot The Trend?

You can inspect how your team occupies its time. Significant variations in time spent on bugs or new value can be a symptom of a big batch of changes or a “bug-only sprint.” Focusing too much on new value can also indicate that the team accumulates technical debt and never addresses it, resulting in a big-bang refactor later.

Issue type time investment graph in Axify for software engineering teams

Solution

  • Create visibility on where the work is going. Inspect how much time you invested in new value versus keeping the lights on or improving things, for example.
  • Set a benchmark. Great teams spend less than 10% of their time keeping the lights on. Spending more than 50% of your time keeping the lights on is a symptom of prioritization or quality.
  • Avoid shadow work by building good process hygiene, i.e., pull requests assigned to issues and issues assigned to epics when possible. It will create a better reflection of reality.

How to Get Started

The first step is to start measuring yourself. The first set of metrics I suggest gathering is the DORA metrics. This will give you a first picture of the team’s delivery performance and let you know which teams need more attention from the pack. 

Setting up Your DORA Metrics Dashboard

Tools such as Axify integrate seamlessly with your tech stack to collect accurate data at all phases of development. Our DORA metrics dashboard tracks Deployment Frequency, Lead Time for Changes, Change Failure Rate and Failed Deployment Recovery Time. It allows teams to compare their performance with industry benchmarks, past performance, and other teams in the same organization to identify areas for improvement and celebrate successes.

DORA metrics dashboard in Axify for software engineering teams

Comparing Your Teams’ Delivery Performance

Our teams’ insights allow you to visualize DORA metrics for each team. They offer the advantage of comparing apples to apples on two important engineering efficiency factors: speed and stability. You can quickly see which team could benefit from more attention and which could share their best practices for better performance.

Teams’ insights view in Axify for software engineering teams

Working Toward Continuous Improvement

Transform how your team sets and achieves goals with our objective and key results tracking tool. See immediately the evolution of your performance indicators and implement initiatives that support the continuous improvement of your development team.

objectives tracking for DORA metrics in Axify for software engineering teams

Contact us for more information on how Axify helps development teams measure DORA KPIs and improve engineering efficiency, or request a demo.

More Pitfalls to Avoid for Maximum Software Engineering Efficiency

  1. Pull requests are stuck or taking too long to review
  2. Working items are too big
  3. Quality control is long due to a lack of automation
  4. Working on too many items at once

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