Telling Your (Data) Story

Storytellers usually design and select characters who have the right level of depth and complexity needed to further a story. Similar to storytellers, both data scientists and visualization experts interpret data —their "characters" — to help them answer questions and relay the right information.

Before creating your first visualization, you want to identify the questions you need to answer using data. Then, use the data fields in Analytics to find the data — or the "characters" — needed to help you answer those questions.

In Analytics, the characters are called Dimensions and Measures.

About Dimensions and Measures

Dimensions and Measures are types of preexisting data fields used to build visualizations.

A Dimension can be thought of as a group or bucket of data.
A Measure is information about that bucket of data.

A Simplified Example on How Dimensions and Measures Work (Especially for Non-Data Scientists)

SCENARIO

In a refrigerator, you see an array of food: fruits, vegetables, and cookies (or biscuits in British English). These food groups can be thought of as Dimensions because they are individual groups of data.

Now, let's say you want to know exactly what fruits, vegetables, or cookies there are in this refrigerator. You could ask the following questions:

How many pieces of fruit are in this refrigerator?
How much of that fruit are bananas?
How many pieces of fruit and vegetables are green? (Naturally green, of course, not molded or otherwise bad!)
Are there more cookies (or biscuits) than fruit and vegetables?
Are there more chocolate cookies (or biscuits) than lemon ones?
Are there more lemon cookies (or biscuits) than actual lemons?

These questions could be thought of as Measures because you are seeking information about one type of food against itself and/or each other.

Now, let's take a look at how Dimensions and Measures work in Analytics.

Viewing Dimensions and Measures in an Explore

Let's say that you want to know how many work orders are in different work order statuses. In this scenario:

the Dimension is the Work Order Status: Open, In Progress, Completed, Invoiced (much like fruits, vegetables, and cookies).
the Measure is the information you want to know about the work order statuses — in this case, the number of work orders (or Work Order Count). Questions such as "How many work orders are in the Open or In Progress status?" or "How many work orders are not yet Invoiced?" can be answered using this Dimension.

When selecting a Dimension or Measure you can do the following:

Click a data field to activate it.
- Notice on the left that WO Status is activated because the background is white, however, WO Count is not yet activated.
- Activated data fields appear in the Data area.
Hover over a data field and click Filter or Pivot to activate these abilities with your data using that data field.
- We will cover this as we move through the workflow. Or, see Filtering and Pivoting Data for more information.
Hover a field and click the Gear icon to use that Dimension as a Measure data point.
- For example, the WO Status Dimension gives you a list of work order statuses, but when you click the gear and select Count, you will retrieve the number of statuses.

Example: Selecting Data

Over the next few pages, we will follow an example of building a story using data. We start with a simple scenario and then build in more complexity as we move through the workflow. Follow along with your data, noting that your data will be different from what is shown.

Example: Selecting WO Status and WO Count

In this example, we want to see the number of work orders in each status. It will look like this:

In the Workorders Explore, expand the *Workorders list.
Click the Dimension WO Status.
1. Make sure to click the word WO Status, not Pivot or Filter.
Click the Measure WO Count.
1. Make sure to click the word WO Count, not Pivot or Filter.
Click Run on the top-right of the page. You will see the data for those data points listed.

Most visualizations will have at least one Dimension and one Measure.

Next section: Checking Out the Data

Now that you understand what Dimensions and Measures are and have seen what they look like when selected, let's dig a little deeper into the data.

In this Article

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