I love this topic, thanks for bringing this up!
A few thoughts of mine:
Delivery Cadence: This measures the rate at which software is released.
I believe this metric can be misleading due to several factors:
Firstly, the complexity and scope of tasks in software development vary greatly. Some tasks might take a week, others a few days, and some even a month. Delivery cadence is focused solely on the frequency of releases, so it fails to account for the depth or difficulty of the tasks being completed. A period during which several small, simple tasks are completed might appear more productive than one where a single, complex task is being worked on, even though the latter might contribute more substantial value to the project.
Furthermore, emphasizing delivery cadence can negatively influence team behavior and decision-making. There’s a risk that it encourages a preference for quantity over quality. Teams will prioritize a higher number of releases over the significance or thoroughness of what is being released, potentially neglecting more complex, high-value work.
Another concern is the potential compromise on quality to maintain a consistent or accelerated delivery pace. Such compromises can lead to increased technical debt, bugs, and unstable releases, which are detrimental in the long term.
Additionally, delivery cadence doesn’t accurately reflect the effort and skill involved in software engineering. Tasks that involve complex problem-solving, research, and implementation of sophisticated features or dealing with technical debt often take more time but are crucial for the project’s long-term health.
Lastly, a focus on rapid delivery can foster a culture of overwork and burnout. It might pressure engineers to work at an unsustainable pace, leading to decreased job satisfaction, reduced quality of life, and, eventually, a higher turnover rate.
Team Morale: A motivated and positive QA team is more likely to be productive and thorough. Regular check-ins and surveys can gauge team sentiment and highlight any areas of concern.
When team morale is measured too frequently through regular check-ins and surveys, there’s a risk that these activities might become more of a ritual rather than a meaningful engagement. Over time, team members might start to view these surveys and check-ins as routine or even bureaucratic exercises, diminishing their original intent and value. This ritualization can lead to less thoughtful responses, as team members may complete them out of obligation rather than genuine reflection on their sentiments and experiences.
Also the actionability of the data collected from such frequent check-ins might be limited. If morale is already high, frequent surveys won’t add much value, and if there are deep-seated issues, simply measuring morale more often won’t necessarily lead to solutions.
Communication Effectiveness: The efficiency of communication between QA and other teams, such as Development or Product Management, can be a significant indicator of potential bottlenecks or misunderstandings that could impact quality."
I believe there are two key components of effective communication: the initiative where team members proactively share information and the goal of minimal information loss. However, both these aspects can’t be properly tracked.
Measuring the level of initiative in communication is a complex task. The willingness of team members to share information is influenced by a variety of factors, including the culture of the team, individual personality traits, and the perceived value of the information. Traditional metrics can’t capture the reasons why some team members communicate more than others or why communication levels may vary over time.
Assessing the extent of information loss during communication is also very challenging. Information can be lost or distorted due to factors such as the complexity of the subject matter, the communication channels used, and the diverse interpretations of recipients. This loss is hard to quantify, as it often requires subjective judgment and a retrospective analysis, which might not always be accurate.
The absence of established models to accurately track these elements further complicates the use of “Communication Effectiveness” as a reliable metric. While tools and methodologies exist to analyze communication patterns, they often focus more on the quantity and structure of interactions rather than on the quality and effectiveness of those interactions.