Tag: metrics

Analytics, Business Intelligence, Data Analysis, Data Governance, Predictive Analytics

What Makes a Metric Actionable?

In data and analytics, not all metrics are created equal. Some look impressive on dashboards but don’t actually change behavior or decisions. Regardless of the domain, an actionable metric is one that clearly informs what to do next.

Here we outline a few guidelines for ensuring your metrics are actionable.

Clear and Well-Defined

An actionable metric has an unambiguous definition. Everyone understands:

  • What is being measured
  • How it’s calculated
  • What a “good” or “bad” value looks like

If stakeholders debate what the metric means, it has already lost its usefulness.

Tied to a Decision or Behavior

A metric becomes actionable when it supports a specific decision or action. You should be able to answer:
“If this number goes up or down, what will we do differently?”
If no action follows a change in the metric, it’s likely just informational, not actionable.

Within Someone’s Control

Actionable metrics measure outcomes that a team or individual can influence. For example:

  • Customer churn by product feature is more actionable than overall churn.
  • Query refresh failures by dataset owner is more actionable than total failures.

If no one can realistically affect the result, accountability disappears.

Timely and Frequent Enough

Metrics need to be available while action still matters. A perfectly accurate metric delivered too late is not actionable.

  • Operational metrics often need near-real-time or daily updates.
  • Strategic metrics may work on a weekly or monthly cadence.

The key is alignment with the decision cycle.

Contextual and Comparable

Actionable metrics provide context, such as:

  • Targets or thresholds
  • Trends over time
  • Comparisons to benchmarks or previous periods

A number without context raises questions; a number with context drives action.

Focused, Not Overloaded

Actionable metrics are usually simple and focused. When dashboards show too many metrics, attention gets diluted and action stalls. Fewer, well-chosen metrics lead to clearer priorities and faster responses.

Aligned to Business Goals

Finally, an actionable metric connects directly to a business objective. Whether the goal is improving customer experience, reducing costs, or increasing reliability, the metric should clearly support that outcome.

In Summary

A metric is actionable when it is clear, controllable, timely, contextual, and directly tied to a decision or goal. If a metric doesn’t change behavior or inform action, it may still be interesting—but it isn’t driving actionable value.
Good metrics don’t just describe the business. They help run it.

Thanks for reading and good luck on your data journey!

Analytics, Business Intelligence, Data Governance, Data Strategy, Data Visualization

Metrics vs KPIs: What’s the Difference?

The terms metrics and KPIs (Key Performance Indicators) are often used interchangeably, but they are not the same thing. Understanding the difference helps teams focus on what truly matters instead of tracking everything.

What Is a Metric?

A metric is any quantitative measure used to track an activity, process, or outcome. Metrics answer the question:

“What is happening?”

Examples of metrics include:

  • Number of website visits
  • Average query duration
  • Support tickets created per day
  • Data refresh success rate

Metrics are abundant and valuable. They provide visibility into operations and performance, but on their own, they don’t always indicate success or failure.

What Is a KPI?

A KPI (Key Performance Indicator) is a specific type of metric that is directly tied to a strategic business objective. KPIs answer the question:

“Are we succeeding at what matters most?”

Examples of KPIs include:

  • Customer retention rate
  • Revenue growth
  • On-time data availability SLA
  • Net Promoter Score (NPS)

A KPI is not just measured—it is monitored, discussed, and acted upon at a leadership or decision-making level.

The Key Differences

Purpose

  • Metrics provide insight and detail.
  • KPIs track progress toward critical goals.

Scope

  • Metrics are broad and numerous.
  • KPIs are few and highly focused.

Audience

  • Metrics are often used by analysts and operational teams.
  • KPIs are used by leadership and decision-makers.

Actionability

  • Metrics may or may not drive action.
  • KPIs are designed to trigger decisions and accountability.

How Metrics Support KPIs

KPIs rarely exist in isolation. They are usually supported by multiple underlying metrics. For example:

  • A customer retention KPI may be supported by metrics such as churn by segment, feature usage, and support response time.
  • A data platform reliability KPI may rely on refresh failures, latency, and incident counts.

Metrics provide the diagnostic detail; KPIs provide the direction.

Common Mistakes to Avoid

  • Too many KPIs: When everything is “key,” nothing is.
  • Unowned KPIs: Every KPI should have a clear owner responsible for outcomes.
  • Vanity KPIs: A KPI should drive action, not just look good in reports.
  • Misaligned KPIs: If a KPI doesn’t clearly map to a business goal, it shouldn’t be a KPI.

When to Use Each

Use metrics to understand, analyze, and optimize processes.
Use KPIs to evaluate success, guide priorities, and align teams around shared goals.

In Summary

All KPIs are metrics, but not all metrics are KPIs. Metrics tell the story of what’s happening across the business, while KPIs highlight the chapters that truly matter. Strong analytics practices use both—metrics for insight and KPIs for focus.

Thanks for reading and good luck on your data journey!

Analytics, Business Intelligence, Data Analysis, Data Strategy

Developing metrics for your analytics project

When starting an analytics project, one of the most important decisions you will make is identifying the right metrics. Metrics serve as the compass for the initiative—they show whether you are on the right track, communicate achievements, highlight challenges, uncover blind spots, and ultimately, along with guiding future decisions, they demonstrate the value of the project to stakeholders. But designing metrics is not as simple as picking a single “success number.” To truly guide decision-making, you need a holistic set of measures that reflect multiple dimensions of performance.

Why a Holistic View Matters

Analytics projects sometimes fall into the trap of focusing on only one type of metric. For example, a project might track quantity (e.g., number of leads generated) while ignoring quality (e.g., lead conversion rate). Or it may measure cost savings but fail to consider user satisfaction, leading to short-term wins but long-term disengagement.

Develop Metrics from Multiple Dimensions

To avoid this pitfall, it’s critical to develop a balanced framework that includes multiple perspectives:

  • Quantity: How much output is produced? Examples include number of units produced, sales revenue, or number of new customers added.
  • Quality: What is the quality of the output? Examples include accuracy rates, defect counts, or error percentages.
  • Time: How long does it take to achieve the output? Or in other words, what timeframe is the quantity and quality measured over? Is it Sales revenue per hour, per day, per month, or per year?
  • Costs: What resources are being consumed? Metrics might include infrastructure costs, labor hours and costs, materials costs, or overall project spend.
  • Satisfaction: How do stakeholders, customers, or employees feel about the results? Feedback surveys, adoption rates, product ratings, and net promoter scores (NPS) are common ways of identifying this information.

Each of these perspectives contributes to the full story of your analytics project. If one dimension is missing, you risk optimizing for one outcome at the expense of another.

Efficiency, Effectiveness, and Impact Metrics

Another way you can classify your metrics to achieve a holistic view is with three overarching categories: Efficiency, Effectiveness, and Impact.

  • Efficiency Metrics
    • These measure how well resources are used and answers “are we doing things right?“. They focus on inputs versus outputs.
      • Example: “Average work hours per product” shows how quickly work gets done.
      • Example: “Cost per customer acquired” reflects the efficiency of your sales operations.
    • Efficiency metrics often tie directly to quantity, cost, and time.
  • Effectiveness Metrics
    • These measure how well goals are achieved—whether the project delivers the intended results, and answers “are we doing the right things?“.
      • Example: “Customer satisfaction” demonstrates how happy customers are with our products and services.
      • Example: “Actual to Target” shows how things are tracking compared to the goals that were set.
    • Effectiveness metrics often involve quality, satisfaction, and time.
  • Impact Metrics
    • These measure the broader business or organizational outcomes influenced by some activity.
      • Example: “Market share and revenue growth” shows financial state from a broader market and overall standpoint.
      • Example: “Return on Investment (ROI)” is the ultimate metrics for financial performance.
    • Impact metrics communicates how we are doing with our long-term, strategic goals. They often combine quantity, quality, satisfaction, and time dimensions.

The Significance of the Time Dimension

Among all the dimensions used in metrics, time is especially powerful because it adds critical context to nearly every metric. Without time, numbers can be misleading. Just about all metrics are more relevant when the time component is added. Time transforms static measures into dynamic insights. For instance:

  • A quantity metric of “100 new customers” becomes far more meaningful when paired with “this month” versus “since company founding.”
  • A quality metric of “95% data accuracy” is less impressive if it takes weeks to achieve, compared to real-time cleansing.
  • A cost metric of “$100,000 project spend” raises different questions depending on whether it’s a one-time investment or a recurring monthly expense.

By always asking, “Over what time frame?”, you unlock a truer understanding of performance. In short, the time dimension transforms static measures into dynamic insights. It allows you to answer not just “What happened?” but also “When did it happen?”, “How long did it take?”, and “How is it changing over time?”—questions that are generally crucial for actionable decision-making.

Time adds context to every other metric. Think of it as the axis that brings your measures to life. Quantity without time tells you how much, but not how fast. Quality without time shows accuracy, but not whether results are timely enough to act upon. Costs without time hide the pace at which expenses accumulate. And satisfaction without time misses whether perceptions improve, decline, or stay consistent over an initiative’s lifecycle.

The Significance of the Timeliness

Another important consideration is timeliness. Metrics must be accessible to decision makers in a timely manner to allow them to make timely decisions. For example:

  • A metric may deliver accurate insights, but if it takes three weeks to refresh the data and the dashboard that displays it, the value erodes.
  • A machine learning model may predict outcomes with high accuracy, but if the scoring process delays operational decisions, the benefit diminishes.

Therefore, in addition to deciding on and building the metrics for a project, the delivery mechanism of the metrics (such as a dashboard) must also be thought out to ensure that the entire process, from data sourcing to aggregations to dashboard refresh for example, can all happen in a timely manner to, in turn, make the metrics available to users in a timely manner.

Putting It All Together

When developing metrics for your analytics project, take a step back and ensure you have a comprehensive, multi-angle approach, by asking:

  • Do we know how much is being achieved/produced (quantity)?
  • Do we know how well it is being achieved/produced (quality)?
  • Do we know how fast results are being delivered (time)?
  • Do we know how much it costs to achieve (costs)?
  • Do we know how it feels to those affected (satisfaction)?
  • Do we know whether we are efficiently using resources?
  • Do we know whether we are effective in reaching goals?
  • Do we know what impact this work is having on the organization?
  • And for the above questions, always get a perspective on time … when? over what timeframe?
  • When are updates to the metrics needed by (real-time, hourly, daily, weekly, monthly, etc.)?

By building metrics across these dimensions, you create a more reliable, meaningful, and balanced framework for measuring success. More importantly, you ensure that the analytics project supports not only the immediate technical objectives but also the broader organizational goals.

Thanks for reading! Good luck on your analytics journey!