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Step 2: Mapping a Dashboard Data Source (CX)

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What’s a Data Source, and Why Do I Need To Edit It?

Now that we’re inside the Dashboard Data tab, it’s time to talk about data sources. A data source is just the data you want to report on in your dashboards. They can be the data you collected in surveys or a CSV of data you upload. Data sources are required in order to populate your widgets with data.

Dashboard Data Mapper

When you add your first data source, many fields will be mapped automatically, and you can technically work with these default settings. But understanding data sources can also help you make edits that will enable the kinds of widgets you want to customize your dashboard with.

Dashboard data with lists of fields

Below are a couple of common examples of changes you can make in Dashboard Data.

Example: When you add a survey data source for the first time, all your fields will be named something like “Q1 – Overall, how satisfied are you with the products our company makes?” when it would probably be faster to just call this field “Product Satisfaction,” so it shows up nicely on graphs.
Example: When you have a CSAT question in a survey, your dashboard may think of this as categorical data, and only offer widgets optimized for categorical data. In contrast, you would probably prefer to look at a satisfaction rating as a numerical scale, so it can be pulled into widgets that work better with numeric scale data. You can do this by changing the field type to Number Set.

In the next section, we’ll break down fields so it’s easier to understand the kind of edits you can make to them.

Understanding Fields

Fields looks like rectangles with segments. From the left to the right: a field type column, a name, a blue gear button for recoding, and a gray dropdown for mapping

In this section, we’ll break down the parts of a dashboard field, highlighted in the screenshot above.

Field Type

This is one of the most important aspects of a field that you need to pay attention to. A field type is the format your data takes. Is your data number or text format? Are the values infinite (like a first name field) or finite (like a scale that only goes from 1-6)? The field type you choose changes the widgets that are available to you.

Here’s a quick overview of the different field types:

  • Number Fields
    • Numeric Value: A number field that has a very large or infinite set of values (e.g., any number between 0 and infinity).
    • Number Set: A number field that has a finite set of values (e.g., the set of numbers from 0 to 5). Typically used with scales, such as the Likert 1-5-point scale.
    • Date: Customized calendar date fields.
      Qtip: You can collect date data in any format you want – you just have to tell your dashboard how to read it. For more information, see Date Field Format.
  • Text Fields
    • Text Value: A one-to-two-word text field that has a very large or infinite set of values. Used for open-ended form questions such as asking for names or email addresses.
    • Text Set: A one-to-two-word text field that has a finite set of values. For example, instead of using a scale from 1-5 in a Number Set field type, mapping the labels “Dissatisfied” to “Satisfied” to Text Set widgets.
    • Multi-Answer Text Set: Multiple select questions that ask respondents to check all items that are applicable to them.
    • Open Text: Longer blocks or strings of text, such as a request for open-ended feedback.

For a guide to changing field types and a table showing the widgets compatible with each field type, see Field Types & Widget Compatibility (CX).

Field Name

The name of the field. These field names can be pretty long by default, explaining both their source and the full text of the field name. For example, “Embedded Data – Department” instead of just “Department.” The field name you configure in this mapper decides the field name you see when choosing a field to map in your widget, so it can make the widget-editing process easier if you shorten these.

Screenshot of adding fields to a widget, but one of the fields has a long and unwieldy name

Recoding Values

Every field comes with values. If a satisfaction question is the field, the values may be:

  • Very Satisfied
  • Somewhat Satisfied
  • Neither Satisfied nor Unsatisfied
  • Somewhat Unsatisfied
  • Very Unsatisfied

Or, if in a number format, this same scale may be:

  • 5
  • 4
  • 3
  • 2
  • 1

But what happens if you want to switch from the text of the satisfaction scale to the numbers? Well, first, you change your field type. But secondly, you can recode the values.

When recoding values, a new window opens. Each of the values is listed, with an open field next to it that allows you to edit values

The recode editor gives you the freedom to assign numeric values to labels and assign labels to numeric values. But that’s not all this editor is useful – you can also use it to group values, exclude values from analysis, edit the names of text values, and more!

See Recoding Dashboard Fields (CX) for more details.

Mapping a Field

Gray dropdown on right of a field highlighted

To map a field is to specify where in your data source the field should draw its data from. When you add your first source, several fields are mapped automatically, so you don’t need to worry about this step. However, if you add a new field, you will need to map it manually. This is as simple as clicking the dropdown and selecting from the list of fields.

Mapping is a fairly straightforward concept that is especially important when you work with multiple data sources. For example, if you run separate (but similarly written) CSAT surveys every year, you may want to merge fields, which means you make sure that for each data source you have, the same dashboard field is mapped. So your CSAT score for the 2020 and 2019 surveys both contribute to one CSAT field in your dashboard. See Managing Multiple Sources for more details.

Field where there are two gray mappings to the right, because there are two sources being pulled into the same field

The Effect of Data Edits on Live Dashboards

New window that opens after you save your changes using the button on the bottom-right of the mapper

It’s important to clean up your data sources before you start to customize your dashboard widgets, and it’s even more important to do this before your dashboard goes live. However, you may not be able to help making data source updates after your dashboard is live.

Some dashboard edits can make the dashboards temporarily unavailable. This means widgets may not show any data. This can happen in the following circumstances:

  • When there are lots of responses in the data source(s). (This is the most likely cause.)
  • When the data source has many data fields. Not just many questions, but many matrix tables, side by sides, text entry forms, or multi-row sliders, which each result in multiple columns of data.

Whenever you make dashboard data edits, it’s also important to consider the impact this may have on:

Qtip: Be strategic about the time of day you edit your data set – think about your primary audience, and when they’re less likely to be online.