Do we use metrics to drive the value chain or just to fill spreadsheets with numbers and graphs? Understanding the real utility of metrics and the typical antipatterns we can fall into, is crucial to achieve sustainable success.
Metrics are essential for understanding the current state and observing changes over time; they provide information that addresses and minimizes the subjective bias that influences the perception of the state or progress of implementing a change or action. An antipattern is a recurring approach to the same situation that is not only ineffective but often detrimental.
A metric should offer patterns of change over time, segregating those that are irrelevant or that measure parameters simply because they are easy to measure. In this sense, context is critical. Data without context, for example, the number of members available or the intensity of incidents over a period of time, is nothing more than noise.
Over the years, I have fallen into all the antipatterns related to metrics, falling in the vanity of the data itself, without considering neither its relevance, nor its context, or scope. I have come to measure individually the items delivered per individual, without being able to reflect that the most important people are not those who finish tasks but those who help the team collaboratively. I have fed dashboards with unnecessary information just because I had it available and I have not listened to the voices that alerted me of its uselessness. Here are the main antipatterns, based on my experience:
Classic antipatterns
Metrics can have relevance for teams or organizations. For teams we have those that affect value delivery such as process time or tasks delivered per unit of time. For the organization we would have financial metrics such as operational efficiency or ROI, human capital metrics (employee turnover, satisfaction), etc.
A metric should be an indicator for a paradigm shift. A detrimental metric can be counterproductive and divert attention from what is important:
Individual performance metrics: In a team metrics context, measuring individual productivity or comparing members based on hours worked, lines of code or tasks completed harms collaboration and goes against the agile principle of focusing on collective success. What gets measured, gets valued, and that applies to team performance. Individual metrics send the message that what is valued most is individual success. Team members will optimize those metrics, which does not always coincide with the desired results because it often forces a choice between personal gain and what is best for the whole.
Treat metrics as targets rather than indicators: Metrics are meant to provide information and indicators, not to be rigid targets. A common anti-pattern is to treat metrics as performance targets and incentivize teams solely on the basis of their achievement. Metrics should provide guidance, help make informed decisions and achieve meaningful progress. The goal is not to chase numbers, but to understand processes and effect positive change. Emphasize continuous improvement, collaboration and a culture that values learning from data.
Lack of alignment with corporate objectives: Inadequate metrics can lead to efforts that do not provide significant value, so it is important to establish a connection between corporate objectives and the metrics on which it is a priority to focus. The decision-making system must be flexible and adaptable to the needs of the company when priorities change or new initiatives emerge, and metrics must reflect this.
Overlook the feedback: Metrics should be used to drive continuous improvement. The iterative improvement process in Agile requires commitment, perseverance and adaptability.
Collect too much data: The data needs to be relevant. If there have been changes in workflow, new process policies have been introduced, or there are changes in team composition, it is important to look at how these changes affect performance metrics. Data should reflect current conditions and the ability to deliver value, at any level, team or organization, without being influenced by old and irrelevant data.
Collect data for the data itself: Metrics should not be interpreted in isolation. They should be used to continuously improve the process, set goals, identify areas for improvement and track progress over time. Data without context is just a number. Just because a piece of data is easy to obtain does not necessarily make it relevant.
Keep teams on the sidelines: Data belongs to those who generate it; it represents their processes, their practices and, ultimately, their behavior. To create meaningful impact, data generators must take ownership of their metrics.
Focusing too much on numbers: Numbers are just numbers that provide a snapshot of a flow in motion. Focus on trends and the evolution of the data to see that the changes implemented are working.
Continuous improvement initiatives are about doing experiments and measuring how the changes introduced influence the trends in the metrics that affect a system considered as a process. Metrics, used correctly, are an essential tool for driving success and fostering continuous improvement. However, it is essential to avoid these common anti-patterns. We must move towards a more holistic and contextualized approach based on corporate and team objectives, with a culture of continuous learning.
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Jorge Alarcón
Scrum Master at Keepler. “Working with people to build products that solve problems. Digital transformation is the new industrial revolution based on the fractal creation of team-based development systems. I collaborate with companies to understand problems and develop solution strategies.”
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