Q 488. Data visualization tools identify data as either a dimension or a measure. What is the difference between the two? Highlight the usage of both in data visualizations using an example. 

 

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Dimension is an independent variable & measure is a dependent variable. Dimension can take any value based on the data type whereas measure is a function of one or more dimensions. In other words, measures are output variables of interest & dimensions are the input variables that need to be controlled to achieve a specific output variable value.

 

Let's take an example of a moving object. It is characterized by speed which can be defined as the distance travelled in a specific period of time. In this case, speed is a dependent variable and can be categorized as a measure. Distance and time are independent variables and can be categorized as dimensions.

 

A 3D contour plot can give us a value of speed for a combination of distance & speed. It helps us to visualize the impact of dimensions on the measure. In addition, main effects & interaction plots in design of experiments are built for measures (response variables) based on different combination of dimensions (control factors).

In data visualisation tools , breaking down the data is grouped into measures and dimensions.

 

Measures

Measures are of type numerical data which are calculated/aggregated. Examples include the sum of revenue, average cost, or profit-per-capita, or non-numeric data that can be counted.

Measures essentially consist of aggregation type associated with them. 

 

Example:

SAP BusinessObjects Lumira application, sets this type to sum.If the chart includes for example revenue by country & the sum is associated with revenue, then SAP BusinessObjects Lumira allows you to customise the prefix/suffix to indicate data, such as CAD, EUR, INR or USD for currency.

 

 

Dimensions

Dimensions consist of categorical data, for example year, product, country, & salary range. Categorical data is defined as nominal data, and is used for denoting discrete values. For Example, the dimension called product type may include the values such as Men’s  Clothing & Women’s Clothing.

In use of ordinal data, the dimension have a fixed order. For example, If the dimension denotes the outcome of a survey result, the resulting value can appear to be Agree , Neutral or Disagree showing up in that implicit order.

In case of interval data, every value in the dimension denotes a range of values. Example includes dimension salary can be divided into the following values of salary ranges: <$30K $30-$60K, $60-$90K, and >$90K.

 

 

How to tell the difference

When we do the aggregation of the object, it must consider the column to be a measure. Example: Sum of Sales can be considered as a measure and Region as a dimension field to split apart the Sum of Sales into the Sum of Sales per Region.

We can create measures from categories by counting their elements. For example, Total Number of Cities visited by the Customers is a measure.

Defining the data in terms of dimension & measure is a common practice when it comes to analyzing & visualizing data. Data Visualization tools particularly Tableau leverages this practice in order to enable effective analysis & interpretation of data.

 

Let us understand the difference between a dimension & a measure along with relevant examples:-

 

Measures:

Measures are basically numerical data which is either calculated or aggregated. Measures can be both discrete & continuous in nature eg: Sales Revenue , Product Cost, No. of Invoices Processed, Sales Quantity etc.

 

There will always be an aggregation type that is associated with measures i.e. we can perform various aggregations to the measures i.e. there are various aggregation predefined in the Data Visualization tools for measures. Some of the typical aggregations are Sum, Average, Count(Distinct), Minimum, Maximum, Variance & Standard Deviation.

 

Dimensions:

Dimensions represent categorical data like year, country, region, product type etc.

There is no predefined aggregation associated with dimensions. 

These work similar to how a GROUP BY clause works in SQL where we are splitting our measure which can be continuous or discrete basis the dimensions eg: Sales per Region, Revenue per Service Line, Product-wise Quantity sold, No of active voters per country etc.

 

 

Note : When it comes to date related data, we can consider it as both a dimension & a measure depending on the type of analysis. Eg: Let’s say we want to analyze the Sales by year & region, then we can aggregate the date on a year basis & consider it as a dimension along with region & then we can slice our data accordingly. In another instance we want to analyze the sales trends over a period of time then we can consider the date data as a measure & we can see the sales data even on daily basis granularity.

 

 

 Thus classifying data into dimensions & measures will help us perform an efficient analysis & visualization of our data.

Data visualization is graphical representation of data by using visual elements like graphs, charts, and maps. Data visualization converts large & small data sets into visuals, which is easy to understand and process.

 

Data visualization tools like Tableau and Power BI helps us to understand outliers, patterns, and trends in a data set.

 

What is a measure? 

A measure is a field that is a dependent variable i.e. its value is a function of 1 or more dimensions. One of the most famous visualization tool Tableau treats any field containing numeric/quantitative information as a measure.

For example. let's consider below bar chart, created in Tableau with the Sales measure from the Sample – Superstore data set:

 

Sales is quantitative data, so by default, Tableau will consider that the field is a measure. 

The value of $8,951,931 is meaningless by itself. It is dependent on context that comes in the form of being broken down by the dimensions.

 

What is a dimension?

A dimension is a field that can be considered as an independent variable. Tableau by default treats any field containing qualitative, categorical information as a dimension.

 

Below are Sales measure from above example, broken down by the dimension of Region:

 

Now that our sales total has been broken down by region, we are able to start gaining insights from the data set.

 

From the above example, we understand that South region has relatively low sales compared to the other regions. This is a descriptive insight that materialized only when we combined measures and dimensions together from our data set.

 

Generally, measure is the number and dimension is what you “slice & dice” the number by. Another rule of thumb is that if it doesn’t make sense to sum up a number, it is likely a dimension.

 Data visualisation tools, such as Tableau, differentiate the Data into two types, namely Dimension and Measure.

 

 Where, while Measure is used for a variable which is Quantifiable and mostly continuous in nature, such as AHT, Turn-around time, sales, cost etc.

 Whereas Dimension is nomenclature used for defining a variable which is qualitative and discrete in nature, such as Region, Team leader, Process name, Agent name etc.

 

To understand the difference better, attached gives an example showing AHT measure against QC score measure & Agent name dimension.

              

 The first graph shows AHT compared to QC scores, where both are measures. And QC is one variable which is continuous in nature and it’s values is in a sequence, hence could be shown in one direction only (80 to 100 or 100 to 80) and can’t be broken.

 The second graph shows the AHT against the Agent name, where agent name variable gives dimension to AHT measure and shows AHT as a quantity, in this case average of AHT, for each individual agent. Here agents are independent of each other, hence their positions on graph could be changed or shown as separate graph entities.

 

Measurements is the assignment of a number to a specific of an object or event, which can be compared with other objects or events. The compass and operation of dimension are dependent on the environment and discipline. In the natural lores and engineering, measures don't apply to nominal parcels of objects or events, which is harmonious with the guidelines of the International vocabulary of metrology published by the International Bureau of Weights and Measures. still, in other fields similar as statistics as well as the social and behavioral lores, measures can have multiple situations, which would include nominal, ordinal, interval and ratescales.Measurement is a foundation of trade, wisdom, technology, and quantitative exploration in numerous disciplines. Historically, numerous dimension systems was for the varied fields of mortal actuality to grease comparisons in these fields. frequently these were achieved by original agreements between trading mates or collaborators. Since the 18th century, developments progressed towards unifying, extensively accepted norms that redounded in the ultramodern International System of Units( SI). This system reduces all physical measures to a fine combination of seven base units. The wisdom of dimension is pursued in the field of metrology.

 

Measurement Instruments and systems 

Measuring systems or instruments comprise a number of rudiments. An element is thus needed in differencing the object and in seeing its confines as well as its frequence. The information is then processed through the system via physical signals. These physical signals are also put in comparison with a reference signal that's known and has been multiplied or subdivided for suiting the range of the dimension that's needed. The reference signal on the other hand is attained from the known volume by calibrating. The comparison, still, can be an analog process where the signals are in a nonstop dimension and are also made equal. Another process of comparison process is known as quantization and this is attained by counting, that's to divide the signal into colorful equal corridor with known sizes and also add up all the number of corridor.
The function of the measuring systems facilitates the introductory process that's being mentioned over. The physical signal is farther amplified to make sure that it's strong for the completion of the dimension. There's also a way of reducing the declination of the dimension during its progress throughout the system, i.e., there can be a conversion of the signal to a enciphered or digital form.

Dimension -The dimension of a fine space( or object) is informally defined as the minimal number of equals demanded to specify any point within it. therefore a line has a dimension of one because only one match is demanded to specify a point on it – for illustration, the point at 5 on a number line. A face similar as a aeroplane or the face of a cylinder or sphere has a dimension of two because two equals are demanded to specify a point on it – for illustration, both a latitude and longitude are needed to detect a point on the face of a sphere. The inside of a cell, a cylinder or a sphere is three- dimensional because three equals are demanded to detect a point within these spaces. 
 
 In classical mechanics, space and time are different orders and relate to absolute space and time. That generality of the world is a four- dimensional space but not the bone
 that was set up necessary to describe electromagnetism. The four confines of spacetime correspond of events that aren't absolutely defined spatially and temporally, but rather are known relative to the stir of an bystander. Minkowski space first approximates the macrocosm without graveness; thepseudo-Riemannian manifolds of general reciprocity describe spacetime with matter and graveness. Ten confines are used to describe string proposition, eleven confines can describe supergravity and M- proposition, and the state- space of amount mechanics is an horizonless- dimensional function space. 

 The conception of dimension isn't confined to physical objects. High- dimensional spaces constantly do in mathematics and the lores. They may be parameter spaces or configuration spaces similar as in Lagrangian or Hamiltonian mechanics; these are abstract spaces, independent of the physical space we live in.

 

Dimension in mathematics 
 
 When we say the confines of a thing in mathematics, it refers to the measure of the size let’s say the height, length, and breadth of an object or the distance or the region of space that's in one direction. In simpler terms, gives us the measure of the length, range, and height of any object in question. 
There are three different types of confines grounded on the object. 
 
 1. One- dimensional or 1D objects An illustration of this is a line member that can be drawn on a face. It's a 1D object because this object has only length without range. 
2. Two- dimensional or 2D objects 2D shapes relate to those objects in figure that have flat- aeroplane
 numbers having only two confines i.e., length and range. 
 
 3. Three- dimensional or 3D objects These are solids numbers or objects that are generally appertained to as 3- dimensional numbers or objects or shapes and they correspond of three confines – length, range, and height.

 

Key Differences could be as below,

 

• In the English language, the word dimension can have further than one meaning. Dimension can mean the length, breadth, or height of an object or 3 confines in physics. still, it can also mean or explained how an object takes up space in the 3 said confines. dimension on the other hand is the act or results of what the confines of the object are( volume). 
•  A dimension is purely a power within a polynomial expression which describes, for case, degrees of freedom of the factors of a certain unrestricted system. Measurement is the difference between equals within a dimension! 
•  Confines on a delineation can be specified exactly, although the usual practice is to have a tolerance since you can not make effects exact. Measurement implies a tool to estimate the confines of an living object. Measures have forbearance too, grounded on the essential delicacy of the tool. The tool making the dimension has to be accurate enough to determine if a part is made within the specified forbearance.

 

Like Data types in the basis statistic, we also have 02 types of data in the Data Visualization which are 1. Qualitative, 2. Quantitative

So in the tableau, when we import data set from source, it gets divided into 02 category which are “Dimension” & “Measure”. Both factors play vital role in the data visualization.

Dimensions represent Qualitative data like Name, type, Binary etc.

Measures represent Quantitative data like Sales, Production, temperature etc.

Dimension	Measure
Qualitative/Categorical	Quantitative
Cannot aggregate	Can aggregate
Field Blue Colour - Discrete	Field Green Colour - Continuous
Independent Variable	Dependent Variable
Min, Max, Sum not Work	Will Work
Duplication of the data is Possible	Not Possible
Not dependent on the measures	Dependent on the dimensions

  

 

Dimensions: Which answers the who, what, where, and when of our data

The data that contains qualitative information are categorized as dimensions. These are expressive attributes, like a category of product, address of the customer, or country of origin. We can say, Dimensions can contain numeric characters (like an alphanumeric customer ID) but are not numeric values (It wouldn’t make sense to add up all the ID numbers in a column, for example).

Let us think in this way: if we can’t (or wouldn’t) compute a field, it’s a dimension.

Eg. Title of the product, category of products, vendor list, etc.

 

Measures: Which are the numerical fields that we can compute

The data that can be quantified are categorized as measures. Fields like subtotal of the order, the number of items purchased, or duration spent on a specific page. “Hence measures are computable”. Say we have a measure, quantity of items purchased: we can do things like calculating the average quantity ordered, sorting by descending quantities, sum all quantities, and so on.

Eg. Price of a product, Customer rating for the product, etc.,

 

Note: Date fields are dimensions too. Eg, The Year of production will be a dimension because calculating min/max/sum here will not help. Instead, we may group this date according to the year of manufacturing.

 

Dimensions	Measures
It is an independent variable.	It is a dependent variable.
It is not dependent on the measure.	It is dependent on the dimension.
Adding to the filter will give us insight into the data, it is beneficial to add this in the filters.	Adding to the filter will not give us many insights of the data.
We can’t aggregate it.	We can aggregate it.
Min, max, and sum won’t work.	Min, max, and the sum will work.
It is used to compare the data.	It is a metric that we use to compare the dimension.
It may contain duplication of data.	It does not contain duplication of data.
Headers are generated when added to the rows or columns.	Axes are generated when added to the rows and columns.
It contains qualitative and categorical information.	It contains quantitative data.
It describes data records.	It cannot describe data records.
It cannot be continuous and discrete.	It can be continuous and discrete.
It is not possible to get several records because aggregation does not apply to it.	Due to the aggregation feature, we can get the number of records present in the database no matter how huge the dataset is

Dimension can have names, dates which are qualitative in nature, whereas measures can have numeric, which is quantifiable. 

Possible combination of discrete and continuous is viable with dimensions and measures. So, we can have discrete dimension, continuous dimension, discrete measure, and continuous measure as possible data types. 

 

In both continuous measure and discrete measure, aggregation (sum, average, count, min, max, percentile, std.dev, variance etc) is possible. However, aggregated value is shown as continuous data value in continuous measure, whereas in discrete measure, aggregated value is shown as categorical value. 
 

Dimension Examples (descriptive field):
Client Name
Client Segment 
Client ID
State
City 
Country
Postal Code

 

Measure Examples (numeric field):
Profit
Unit Cost
Orden Quantity
Sales 
Salary

 

Most of the data visualization tools, auto detects data types, for instance, Tableau automatically detects as well as represents data types as symbols. 

 

Differences:

For Instance, I have considered Tableau Data Visualization tools for reference to give elaborate difference between Dimension and Measure 

 

 

Below are some of the examples of effective usage of dimension and measure in terms of overall data visualization dashboard.

 

Example - Sales Dashboard

 

Example - Marketing Dashboard

 

Example - Revenue and Customer Distribution Overview 

 

Dimension and Measures are the key point of any data visualization tool as it plays a major role while driving with data sets. 

Kaviraj has provided the best answer to this question and has been selected as the winner. 

 

It is worth reading all the answers even thought the definitions are same, the examples quoted are different and that makes it an interesting read.