Measures are essential to advancing science, technology, and quantitative research in many disciplines. They have become a cornerstone of commerce, industry, and sports performance, among other things.

A measurement is a procedure for assigning a characterization (usually a numeral) to empirical properties, according to rules. These rules must be mutually exclusive and exhaustive.

## Definition

Measures are used to quantify data for the purpose of obtaining actionable insights. In order to be a true measurement, data must accurately reflect the desired outcome. It also must be verifiable, so that the results can be compared to available references.

A measurement is a set of observations that reduce uncertainty and produces a quantity:

The International System of Units (SI) defines seven fundamental units of measure. These are the kilogram, metre, candela, second, ampere, kelvin, and mole. The SI definition is an artifact-free one, meaning that the units are defined by reference to a constant rather than some physical object that serves as a standard.

Mathematically, the concept of measure is a generalization and formalization of geometric measures and other notions such as magnitude, mass, and probability. It is related to integration theory and probability theory. A measure is semifinite if it is closed under countable conical combination, and it is locally realizable if it has a finite measure zero.

## Meaning

The meaning of a measurement depends on the concepts it is trying to capture. For instance, a measure of work effort is a quantified indicator of speed, dexterity and repetition. Measurements are usually defined on a scientific basis and overseen by independent agencies. They are also defined according to specific rules that make the outcome meaningful. These are called the logical or operational definitions of variables.

The metric system uses seven fundamental units to quantify size, volume, area and intensity: the kilogram, metre, candela, second, ampere, kelvin and mole. They are defined without reference to a standard artifact that would be subject to deterioration and degradation.

The main difference between a measurement and a metric is that measures give you a vague estimation of any business activity, while metrics offer more information about the performance of an entire business. Metrics help you identify what areas you need to change to achieve your goals and track the progress over time.

## Variation

The variation of a measure is a number that describes how spread out a set of data values are from each other. This number is often much higher than the mean, which is the central value of the data.

This is because the variance takes the difference between all of the data values and the mean, then squares it. This results in a number that is less sensitive to the size of the values than other measures like the range, which involves only the smallest and largest numbers.

For example, if both sets of scores have the same mean score, the range for section A would be 5, but the range for section B might be 10, which makes it obvious that the scores in section B were more spread out than those in section A. This is also called dispersion, and is a key characteristic of data sets. It is important to understand this concept when interpreting results and making decisions.

## Applications

Measures and metrics serve a variety of purposes in business. They can be used for analyzing and tracking trends over time or to quantify and gain insight into specific processes. However, they must accurately reflect what they are supposed to quantify in order to be useful.

The measurement process requires a physical signal that discriminates the object or quantity being measured and compares it with a reference signal of the same kind. The measuring device itself may power the signal or it may require interaction with an external source of energy, such as a battery, light bulb or electromagnetic field.

Unlike calculated columns, measures are context-dependent and their values change in response to selections on rows, columns and filters of a visualization. This makes them ideal for dynamic, ad-hoc calculations that are used for data exploration. However, they can also consume RAM memory when not in use.