Use Measures when you need dynamic calculations that change with user actions like filtering or slicing. Use Calculated Columns when you want static values or when the calculations are too complex to be solved using measures.
Establishing metrics requires careful thought to ensure that they accurately quantify the goals you hope to achieve. They should also align with your organization’s vision.
Definition
Measures are the values that can be summed and/or averaged. Examples include sales, leads, distances, temperatures and weights. They are often used alongside dimensions, which are the categorical buckets that can be used to segment or filter the data.
Historically, the notion of a measurement has been a concrete one: any assigning of numbers to objects or events counts as measurement. This characterization fits a wide variety of perceptual and linguistic activities, but it is not broad enough to cover all scientific inquiry.
In mathematical terms, a measure is a countably additive set function with the properties sigma finiteness and finite additivity. For instance, the Lebesgue measure on a symplectic manifold is a measurement. A measure with negative values leads to signed measures (see the section on generalizations). Finitely additive measures are known as contents, and they have important applications in geometric measure theory. They are also associated with notions such as the Stone–Cech compactification and Banach limits.
Purpose
Measures are used to collect data, calculate values, and display them in a visual way. They can be qualitative or quantitative. In order to be useful, a measurement should be repeatable and timely. It should also be able to indicate critical trends.
Measurements are defined without reference to a physical object that serves as a standard. This is referred to as artifact-free definitions.
A good example is the kilogram, which is an artifact-free unit of weight. Other examples include the meter, candela, second, ampere, and kelvin.
Measures perform dynamic calculations on the fly and can be used across multiple visualizations and data sections. However, they require more memory to store and compute than calculated columns. To create a measure, select a table and click New measure in the Calculations group on the Power BI Desktop ribbon. Then enter a DAX formula in the formula bar. You can create a measure in any table, but it’s easier to find if you create it where you plan to use it.
Significance
In a data context, measures are raw numbers that can be summed and averaged to produce metrics, such as total sales or website traffic. Metrics are more specific and focus on outcomes, providing insights that can be taken into action.
In principle, a test of significance can detect a real signal in the noise produced by measurement error and other sources. However, the noise may be so great that it overwhelms the signal, and the resulting significance will be misleading.
To reduce this effect, researchers should ensure that their instruments and environments are as consistent as possible. This can be done by measuring the same quantities on multiple occasions to compare results, or by calculating intraclass correlation coefficients (ICCs) for self-report measures. ICCs provide information about whether the variation between individuals in a sample is mainly due to measurement error or other factors. This is known as temporal stability. This is one of the epistemic criteria of scientific validity.
Implementation
In a business context, the term implementation is used to describe the rollout of plans and projects. This may include the development of new technologies, training employees, and obtaining funding. It also includes the use of new processes to reach desired goals and improve operational efficiency.
The number of measures available for measuring implementation outcomes has increased dramatically over the past two years. For example, the number of measures assessing feasibility, a measure that can predict whether an intervention will be successful, has doubled since 2015. However, the overall growth in measurement development is uneven among outcomes.
In order to move beyond the current state of measurement, quality measures must be developed in a similar manner as evidence-based interventions (EBIs). This requires that the constructs to be measured are defined and clearly communicated. In addition, the measurements should be based on sound psychometric criteria. This will enable us to understand the relationships between variables and their impact on implementation outcomes.