Measures and metrics are handy tools for businesses, allowing them to quantify and analyze data. However, it is important to ensure that your measurements accurately reflect what they’re supposed to in order to gain actionable insights.

To do this, it is essential to establish clear measurement guidelines and regularly collect data. Additionally, it’s important to understand when to use measures and when Calculated Columns are a better fit.

Units of Measurement

There are several different units of measurement. Each unit is used to measure a physical quantity. These quantities include length, weight and volume. The main system of measurement in the world today is the metric system. This system uses a base unit of a meter for length and kilograms for mass, with smaller units of centimetres and millilitres derived from these.

Traditionally, measurements were made using a number of different objects. For example, a table could be described as “long,” but it wasn’t always possible to tell how long. When different systems of measurement were compared, it became important to have consistent and standardized ways of measuring things.

Fortunately, modern units of measurement are agreed upon by scientists around the world. For example, a meter is a certain amount of length and there are defined rules for how the number is derived. This makes the comparison of measurements easier and more reliable. The metric system also provides a way of expressing the results in terms that are meaningful to everyone.

Measurement for Improvement

Measurement is often used to make things better. It can help us understand the system and find out where things are going wrong. However, it is important that we use the right measures for our purposes. If we choose the wrong ones, then they will not help us improve things.

During a Quality Improvement (QI) training, participants learn how to identify an improvement opportunity, plan and design a measurement system, collect, analyze, display and interpret data, and use different tools to evaluate progress. They also learn about the three types of measurements for QI, outcome, process and balancing, which each need a clear operational definition to be effective.

Previous research has shown that many healthcare professionals struggle with measuring for improvement. In this video, Vardeep Deogan explores the reasons for this reluctance and provides advice for how to overcome it. For example, she suggests using simple techniques such as run charts to track changes over time, rather than trying to collect large amounts of data at one point in time.

Choosing the Right Measures and Metrics for Your Business

The terms measures and metrics are often confused with one another, with the two even being referred to as the same thing at times. It’s important to understand the difference because metrics essentially take raw data and provide it with context. This makes them orders of magnitude more useful than basic raw numbers alone.

Choose measures that align with your business goals and objectives. Define the target for each metric and ensure it is SMART, meaning specific, measurable, attainable, relevant and timely. Determine how each metric will be collected and analyzed, whether manually using surveys or via automated tools like analytics software.

Be aware of the pitfalls of vanity metrics that give you a false sense of achievement but fail to translate into actionable insights. For example, measuring the speed at which you respond to customer inquiries can boost your ego but won’t improve performance. Choosing the right metrics requires a thorough analysis of your unique business aims and objectives.

Using Measures for Predictive Analysis

Measures and metrics can be collected in a variety of ways depending on the type of data being measured. For example, surveys may be appropriate for qualitative measurements, while automated tools might be more suitable for quantitative information like sales figures.

Prediction models can be assessed with a number of different measures. Traditional measures include the Brier score to indicate overall model performance, the concordance (or c) statistic to evaluate discriminative ability and the area under the ROC curve to assess classification accuracy (see Fig. 1).

In addition to providing insight into predictive model performance, these measures also help to identify any potential bias in the data. However, interpreting results of binary prediction models can be more challenging than for other types of predictions. One option is to present the results of binary predictors in a 22 confusion table also known as a contingency table. This is often easier to interpret than the ROC curve, although it cannot capture all aspects of predictive model performance.