When an engineering leader considers implementing a new initiative, they often run through a checklist of questions: Will it achieve our goals? Is it cost-effective? When considering introducing engineering metrics, leaders might also wonder, how will this make my team feel?
Engineering data can be a powerful way to foster a culture of continuous improvement when introduced and utilised thoughtfully and responsibly. Not only do effective engineering metrics show how an organisation is performing, but they can also provide critical insights into the organisation’s future success.
This blog discusses how engineering leaders can introduce metrics with success.
Best Practices for Introducing Engineering Metrics
The potential benefits of engineering metrics are clear, but leaders are hesitant to introduce them for fear their teams will feel micromanaged. In addition, developers may be concerned that metrics will be used against them — that the data will be used to penalise them for apparently not working hard enough, fast enough, or for making a mistake.
Many of these concerns can be alleviated by helping developers understand exactly how the data will be used and how they can use it themselves, thus contributing to a positive work culture of trust, safety, and transparency.
1 Get Goal-Aligned Before You Begin
Focus on the goals first and the data second. It’s important to assess your goals before you dig into the data. There are many metric options available, but you need to select the ones most suited to your organisational goals, as each company will likely differ in their needs.
Numbers can be a powerful tool, but metrics not matched to goals will offer little insight. So instead, consider your team’s specific priorities to determine what needs to be measured, and ensure that the metrics you’re focusing on are capable of giving you visibility into the thing you most want to monitor and/or change.
It is common for leaders and their teams to expend energy measuring the wrong things or too many things with little gain. Not all metrics are useful for understanding progress toward every goal, and misalignment on which metrics match which goals can hinder a team’s progress.
Engineering leaders and their teams should both have a clear understanding of which metrics are being collected. Will it be team data or individual metrics? Will it be big-picture or granular? It is crucial to ensure your team knows what data you plan to analyse and which goals the aggregated data is intended to support.
2 Embrace Communication
Metrics are there to enhance conversations, not to replace them. Multiple factors can impact data, and in a highly collaborative industry like software development, no specific individual should be made to feel personally responsible for a given metric.
However, data can help leaders to identify opportunities for individuals to improve so they can grow; it can also help keep the team focused on team performance and project milestones.
As part of any data-driven conversation, metrics should always be applied constructively, not punitively. Missed targets are an opportunity to coach, while metrics help keep the conversation grounded in facts. By focusing on the work, not the person, conversations will remain productive and free of blame.
As an engineering leader, you are responsible for ensuring data is being used as intended. Therefore, in both processes and communications, it is crucial to convey the value and the purpose of engineering metrics. Opening the lines of communication regarding data supports the team in two ways: it ensures that every team member is comfortable and establishes the basic principles of responsible data use.
3 Consider Data Access
Leaders must also be transparent about who will have access to which data. Every organisation will be different, and executives will typically look at a different data set than what a manager or contributor would need. Different roles may also have access to varying levels of data.
For example, in many organisations, individual contributors don’t have access to engineering data or can only look at team-level data. Transparent, accurate reporting improves alignment for everyone.
4 Quantitative and Qualitative Data In Action
Quantitative data isn’t a diagnosis; it’s a set of symptoms. It can alert you to a possible problem and help you figure out what questions to ask next as you work with your team to solve it. For the data to help you understand how your team is working, it must be paired with qualitative information gathered from your team.
This context is essential. If you notice in the data that something is off from your expectations or trending in a concerning direction, that information can be brought to a team meeting to dig deeper.
To make the data work for your company, it has to work for everyone. By defining how data will be used and illustrating the value of engineering metrics, teams will be more willing to participate. When used effectively and appropriately, objective data can improve visibility and boost alignment. Data can empower developers to solve problems that matter to them. As an engineering leader, data can support you in increasing your team’s happiness and engagement and remove blockers to improve team processes.
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