Time, quantity, quality, nominal, ordinal… data types used in Industry
Figure 1 Planetary Movements, Depicted as Cyclic Lines on a Spatial-Temporal Grid, by an Unknown Astronomer in a Transcription of Commentary of Macrobius on Cicero's In Somnium Scipionis, 10th or 11th Century A.D. Reprinted in .
Brief start about Data
Effective conviction and persuasion are a result of speaking with clear and accurate data. Data is the basis of reasoning and a source of factual information.
Without data, decisions are blind and dangerous. If we can't decide we cannot move. This would be disappointing for Leonardo Da Vinci who once said “ Life is movement “.
Figure 2 Trendline showing interest over time for the word “data”
Figure 3 Map showing interest by region in 2022 for the word “data
As we can see in Figure 2 and 3, data is worldwide, remains popular and we might conjecture based on the trend line that it shall stay so for a while.
It also means that there is a massive amount of articles, citations, and definitions out there on the web. Thus, we need to clearly define our notions.
In this article, we are going to explore the history of data, set up the notions, list data types, and clarify the subjectivity of data types.
Rollback in History
According to Merrieb-Webster’s dictionary, the first known use of “datum” was in 1646 meaning “something given or admitted especially as a basis for reasoning or inference”.
Online etymology dictionary informs us that from 1897 its meaning evolved as numerical facts collected for future reference.
Just a side question: Have we heard about Ishango bones?
Huylebrouck in “Africa and mathematics” mentions that  the oldest mathematical finding is the Ishango rod dating to 20.000 years before present.
Figure 3. The Ishango bone on exhibition at
the Royal Belgian Institute of Natural Sciences
First things first, let us set our common conversation ground. It is important to keep in mind that this topic is significantly popular, thus we might hear or read different words having almost the same definition, or synonyms.
One piece of advice would be to check when discussing with your client or teammate if you have a common language for mutual understanding and a solid base when developing your project.
Just to illustrate, let us look at what data means.
(1) Data: is a collection of values that people can only observe when they look at it.
“Hello”, 2, 3.5 , null, “3.2 pH”
(2) Meaningful data: A collection of values belonging to a set of contexts and which “talks” to the audience when presented. Questions, curiosity, pattern finding thoughts pop up in our heads when we encounter meaningful data contrary to just data.
[context][collection of values ] → [Worktime] [10a.m , 11a.m, …]
[context][collection of values ] → [Machine state] [“working” , “stopped”,”paused,”...]
[context][collection of values ] → [Start time] [“12a.m” , “15p.m”,”19p.m,”...]
[context][collection of values ] → [End time] [“15a.m” , “17a.m”,”14a.m”...]
Extra question: what would be an appropriate chart to visualize the given example ?
Data source table : is a table composed of rows and columns containing data and context in which meaningful data is obtained by the product of the latters. Our data source table may be generated by different Connections or “sources” such as Json, databases which receive data from actors we define in (7).
Figure 4. A table containing a collection of data associated with context.
Figure 4., demonstrates to us 4 collections of data 1,2,3, and 4 associated respectively with “TagName”, “Building”, “Product”, and “UnitPrice” contexts.
We can intuitively assume that this table represents information gathered from a production factory when both context and data are correctly provided.
Row : is a horizontal line composed of a collection of values associated with a context given in each column forming the row. See figure 4.
Column: is a vertical line composed of a collection of values associated with the only given context representing the column mostly indicated in the header section. See figure 4.
Data field: is a cell in the table containing a data value. See figure 4.
Actor: is a data provider. In industry, our actors are usually sensors and software.
Element: is an entity and one of the fundamental building blocks of data visualization. We refer to it as “datum” as well. It actually is represented as a row in our table in figure 4. It is the transformation of the values given in elements that are used later for visual encoding.
Now that we have a common language, let’s discuss data types.
According to “Data Types, Graphical Marks, and Visual Encoding Channels” written by Jeffrey,  data values can represent different forms of measurement.
Our problem is to identify our forms, in other words, our data types that help us define our “comparison types”.
In this crucial section, we shall clarify data types, explain that data could belong to multiple data types, and provide a few examples of data types we use in industrial use cases.
Why types, why categories?
Actually, the oldest reference trying to explain the need of categorizing data draws back to 1946 and S.S. Stevens states in “On the Theory of Scales of Measurement” that  the British Association for the Advancement of Science debated the problem of measurement [...] and reported upon the possibility of “quantitative estimates of sensory events” meaning simply: Is it possible to measure human sensations.
In industry, we categorize data in different types to show off relations, comparison, deviation, proportion, and distribution when building widgets.
Identifying a data type
Nominal data is used to categorize data and compare the equality of values.
Figure 5. Example of categorical data
Product A, Product B, and Product C are values representing categorical data. We can only compare if Product X is equal to Product Y or different.
Ordinal data contains values we can use to compare them in a specific ordering
Figure 6 Example of ordinal
data using number-based values
Figure 7 Example of ordinal
data using text-based values
In these examples, we can compare values such as “Is the year 2000 greater than the year 1999 ?” or “Is pressure high thus greater to a threshold value? “
Quantitative data contains values in which we can perform differences between these such as finding distances or proportions
Figure 8 Example of quantitative data
We can see that Year is the same example used to illustrate ordinal data previously. We will clarify this point in the upcoming section “The problem of Measurement”
Meanwhile, in this example, we can ask questions such as “ How many years have passed between 2000 and 1997 or what is the proportion between 1999 and 1998”.
Temporal data is used to demonstrate intervals or punctual moments in time in which our data fields are "valid". In other words we use temporal data to demonstrate occuring events, actions, or
It is one of our main data types in Industries as time is crucial for operations in the factories.
Figure 9 Example of quantitative data
Date-times can be standardized or not. One might use ISO date-time format or just date strings such as "Saturday, January 22, 2022".
Yet is is recommended to have formatted date-times. Below we have a list of formatted date-times examples:
Quarter: Quarter 1, Quarter 2, Quarter 3, Quarter 4
Quarter Year: Quarter 1 2022 , Quarter 2 2021, Quarter 3 2020, Quarter 4 2022
Month : April, May, Juin ...