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Goal

As explained in Effective Data Analysis

A desired outcome that an organization seeks to achieve through its activities and decisions.

A goal is an organizational outcome people want to achieve. In analytics, it gives a metric a purpose: a team chooses what to measure because the result bears on a decision or an improvement it wants to make. A target is different—it states a desired value for a metric over a period of time. section 7.2 section 7.4

To turn a goal into a useful measure:

  • Agree with stakeholders on a specific operational definition. Broad aims such as supporting customers or making an application easy to use need observable measures. section 7.2.1
  • Check that the metric is measurable, relevant to the goal, and aggregated over an appropriate period. Its underlying data must be trustworthy. section 7.2.1 section 7.4
  • Establish a baseline from historical trends and customer segments; compare suitable external benchmarks before proposing an achievable target. section 7.2.2 section 7.4
  • Revisit the target as evidence accumulates. An unreachable target can discourage teams, while an easy one may not prompt meaningful improvement. section 7.2.1 section 7.4
Table 7.2 Many metrics are colloquially discussed as concepts and thus require stakeholder collaboration to create specific operational definitions.
High-level concept
Specific metric
The engineering team is productive
Percentage of tasks completed on time
Customers are supported in their needs
Percentage of customer problems and questions resolved on the first contact (first call resolution, or FCR)
Creating an easy-to-use application
Median time to complete the setup flow for new users

The table pairs broad organizational aims with specific metrics, showing why stakeholders must agree on what a goal will mean in practice. section 7.2.1

Customer retention

A 100% customer retention target sounds clear but may be unrealistic. Examine past retention or churn, comparable organizations, and factors a team can influence before choosing a target. A fixed rate of improvement may also become infeasible over successive quarters. section 7.2.1 section 7.2.2

How Effective Data Analysis covers it

How deep it goes
Develops a practical process for translating goals into measurable metrics and achievable targets, rather than a formal theory of organizational strategy.
Examples
Uses customer retention targets and a table mapping broad aims to measures such as first-call resolution.
What it assumes
Assumes an organizational question and access to stakeholder and performance context; introduces the metric-design checks needed to make a goal measurable.
What you can do after
Practice/perform goal setting by defining relevant metrics and checking proposed targets against history, benchmarks, and feasible actions.
How it connects
Links goals to SMART metrics, operational definitions, baselines, benchmarks, and performance targets.

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This explainer was generated by Manning's knowledge graph from the text of Effective Data Analysis. The section links point to the parts of the book it draws on.