Context in Industrial data visualization

Get a better understanding of what is context in industrial data visualization

Downtime duration Pareto
Figure 1. Downtime duration of machine parts.

About context in data visualization

Understanding context in dashboarding is essential and sets you on the path to success when it comes to creating visual content [1].

Today, we will cover this important topic in our journey towards data visualization.

A loopback in its history

Actually, data is visualized since a long time if we don’t consider technologies used for it nowadays. In fact, Physical artefacts such as Mesopotamian clay tokens (5500 BC !), Inca quipus (2600 BC !) and Marshall Islands stick charts (n.d.) can also be considered as visualizing quantitative information [2].


data visualization in history
Figure 2. Mesopotamian clay tokens. Denise Schmandt-Besserat

Even then, data was visualized in order to point out information through its content and context.

Content

Content in data visualization is the measure. For example, we have a battery producing energy. Energy is our content. Content itself is not relevant in Data visualization. A dashboard indicating only the energy a battery produces does not contribute into decision taking. That is where, context comes into play.

Etymology of context

Context ( from Latin contextus, con-’together’ + texere ‘to weave’.) means “the construction of”. So context is the environment containing our content.

Lets see together how to understand it better in order to use it in the most appropriate way when creating our dashboards.

Context

Understanding context should not be underestimated. Indeed, it is the first point that stands out when starting creating dashboards to visualize data. Thus, we should begin our visualization process once we are sure we got our context properly defined. Our visualization process starts by identifying our context in our data source’s columns.

Lets set things up

It is necessary to keep in mind what type of analysis we will do when creating our dashboards in industrial data visualization: It is explanatory analysis. Our result should explain our audience what we have obtained throughout our visualization process.

Who

Knowing our audience improves the quality of the message we are willing to share throughout our Dashboard. The more specific our target audience is, the more our Dashboard will be significant to them. For instance, in Figure 3. below, our audience is surely not our marketing department.

What do you want to show?

At this point, we are the master of our data. After showing our audience our widgets, we should make them know or decide something related with our data. We need to ask ourselves questions such as “ who, what, when, how” in order decide our context.

Of course, we need to be aware that the type of context we want to choose is available in our data source and is meaningful with our content.

A few examples

Dashboard Pareto bar graph
Figure 3. Downtime of machine parts.

In our example above, I see downtime duration of machine parts. Our context here is “machine parts”. For instance, I can explain to my audience ( technical support team manager ) that the packer had a downtime of 407 hours compared to a filter who had 71. My message here is to point at him that eventually my packer has a excessively high downtime value compared to other parts.


line graph dashboard
Figure 4. Product prices of product X and Y during a given datetime.

In Figure 4. our context is time. We can notice that we can’t give the same type of explanation by reading this example compare to the one in Figure 3. In this one, I can explain when my product X had higher prices then my product Y.

Indeed, in Figure 3, we have no explanation related to datetime.

Prevent confusion

Our context will be displayed in our widget. It is also important to adapt our diagrams according to our content and context.

Example

In order to see an example using content and context, we can check out this article about Sankey diagrams.

Conclusion

We can conclude by saying that understanding context in industrial data visualization is crucial. It is the As of Heart which determines whether our visualized data will be meaningful or not to its target audience.

References

[1]Cole Nussbaumer Knaflic. Storytelling with Data : A Data Visualization Guide for Business Professionals. Hoboken, New Jersey, Wiley, 2015.

[2] Schmandt-Besserat, Denise. How Writing Came About. Austin, Tex. Univ. Pr, 2006.

Figure 2. Schmandt-Besserat, Denise. How Writing Came About. Austin, Tex. Univ. Pr, 2006.

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