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How to Balance Data and GoodJudgment in Effective Performance Managementby@yaw.etse
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How to Balance Data and GoodJudgment in Effective Performance Management

by yaw.etseMay 20th, 2024
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Vanity metrics often obscure the subjective nature of performance management. Being data-informed means using data as a valuable tool to support well-reasoned arguments, not as the sole determinant of decisions. By focusing on meaningful metrics that truly reflect performance and contribution, organizations can boost morale and intrinsic motivation.
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Performance management season always brings a push for data-driven approaches, and it’s usually not lost on people managers the truly subjective nature of the process.


As previously written, I think embracing this subjectivity can be more beneficial than attempting to mask a subjective process with vanity metrics. There still is an opportunity to improve flawed data-driven approaches by being data-informed when data supports good arguments rather than replaces them.


The Pitfall of Vanity Metrics

Vanity metrics are appealing because they provide quantifiable data that seems to offer clear insights. However, they often obscure the subjective nature of performance management.


This reminds me of the ongoing discussion on the distinction between math and science.

Math relies on deductive reasoning, starting with basic principles and logically deducing consequences. If you accept the initial premises, the conclusions are certain.


In contrast, science uses inductive reasoning, drawing general conclusions from observations.


For example, if every swan observed is white, one might conclude all swans are white, but a single black swan disproves this theory (1).


The Advantages of Being Data-Informed

Being data-informed means using data as a valuable tool to support well-reasoned arguments, not as the sole determinant of decisions, e.g.:


  1. Comprehensive Understanding: Data provides insights, but a comprehensive understanding of the factors driving performance is essential. Are all factors driving your metric well understood? If not, data may be misleading (2). By being data-informed, you use data to enhance your understanding rather than replace it.


  2. Supporting Good Arguments: Good arguments are often based on a mix of data, context, and expertise. By supporting arguments with data, you ensure that decisions are well-rounded and consider multiple perspectives. This holistic approach is more robust than relying solely on data, which might overlook important nuances (2).


  3. Encouraging Experimentation: Can you run an experiment to test your assumptions? Controlled experiments are invaluable for establishing causality. When data is used to inform hypotheses and guide experiments, it becomes a powerful tool for validation and learning (2).


  4. Enhancing Communication: Data can help communicate complex ideas clearly and persuasively. Good data storytelling combines narrative with explanatory visuals, making insights more accessible and actionable (3).


  5. Maintaining Motivation: Metrics that align with an individual’s or team’s convictions can enhance motivation. By focusing on meaningful metrics that truly reflect performance and contribution, organizations can boost morale and intrinsic motivation (2).


Challenges with Overemphasis on Data

While data is invaluable, an overemphasis on data-driven decisions can lead to:

  1. Misinterpretation: Without proper context, data can be misinterpreted. It’s crucial to ensure that data is understood correctly and used appropriately (2).


  2. Streetlight Effect: Organizations may focus on easily measurable improvements, neglecting more significant but harder-to-measure changes. This can lead to superficial enhancements rather than meaningful progress (2).


  3. Suspension of Disbelief: Introducing metrics in areas where they don’t belong can lead to a culture of ignoring the limitations and meaninglessness of certain data points (2).


  4. Weak Leadership: Relying too heavily on data can be a sign of ineffective leadership. Strong leaders should be able to use their judgment and observations to make decisions, using data to support rather than dictate those decisions (2).


The Value of Good Arguments

Good arguments, even without extensive data, can often lead to better decisions.

Here’s why:

  1. Correlation vs. Causation: Basic statistical principles remind us that correlation does not imply causation. It’s possible to make great decisions with good arguments and minimal data, but bad arguments can easily lead to poor decisions, even with good data (3,2).


  2. Cultural Impact: An overemphasis on data can harm organizational culture. Metrics can lead to superficial improvements at the expense of more nuanced, meaningful changes (2).


  3. Holistic Decision Making: Good arguments consider a wider range of factors, including context, expertise, and judgment, which are often overlooked in data-driven approaches (2).


Embracing a Balanced Approach

Data has its place and can be a useful tool for supporting arguments. However, it should not be the sole basis for decision-making. Strong arguments, grounded in observation and theory, often provide a more reliable foundation for decisions. Resist the temptation to rely solely on data, and maintain a healthy skepticism towards metrics that promise easy answers.


Being data-informed rather than data-driven means using data to enhance and support well-reasoned arguments. This balanced approach ensures that decisions are both informed by data and enriched by context and expertise.


Performance management benefits from a balanced approach that leverages data to support strong arguments. By being data-informed, organizations can make better decisions that consider both quantitative and qualitative factors.


Embrace the subjectivity of the process and use data as a tool to enhance, not replace good arguments. This approach will lead to more meaningful, well-rounded decisions that align with organizational goals and values.


For further reading, check out my other writings:

  1. University of Houston, THE ENGINES OF OUR INGENUITY: Math Versus Science ↩︎
  2. Richard Marmorstein: Be good-argument-driven, not data-driven ↩︎
  3. Forbes: 8 Pitfalls In The Data-Driven Decision-Making (DDDM) Process ↩︎