Configure a Dataset - Dataset Manager

Learn how to configure both local and shared datasets using the Dataset manager. 

Note: Permission to edit a Dataset manager is reserved for Admin, Project manager, and Editor roles. If you are assigned a Contributor role, you'll have view-only access. 

A Dataset is a set of value options that can be added to select fields (Dropdown and Multiselect) to build Input fields. A dataset can be configured and managed from its dedicated editor - the Dataset manager.

Use case: When handling large numbers of options, datasets enable the creation of reusable sets, eliminating the need for constant manual input or importing from a file. This helps you streamline the process and centralizes the management of all your value options.

Please note that this also means that any edits made within the Dataset manager will affect all fields to where the dataset is added. 

Access to the configuration of Input fields is reserved for Admin and Project manager roles. However, even as an Editor user, you can configure and edit both options and dependencies using the Dataset manager. This ensures that the Create form is not altered to deviate from the project taxonomy while also granting you some flexibility.

Local and shared datasets 

Datasets can be either local or shared. Local datasets are created and managed within a specific project. On the other hand, shared datasets are created and managed at an account level and then inherited to a project.

Regarding the Dataset manager functionality, it's worth noting that there are differences between local and shared datasets, but overall the interface and process remain consistent. 

Here's an overview of the differences: 

• From a project level, access to the Dataset manager of a Shared dataset is limited to view mode. Shared datasets can be created and managed only from the Account level.

• Additionally, the Dataset manager for a Shared dataset includes an extra tab labeled 'Shared in', which provides insight into where the shared dataset is used across live project configurations.

Note: In this article, dataset is mostly used to denote both local and shared datasets. However, if needed, shared dataset is explicitly specified to differentiate it from local datasets based on context. 

Datasets manager structure

The Dataset manager is divided into two primary configurations, each serving distinct functions:

Dataset tab

• Dataset options

This includes all basic operations such as adding, editing, sorting, and ordering. Additionally, it also includes bulk import and export functionalities. 

• Settings

Configure preferences on the use of abbreviations and apply rules to your value options.

• Allowlists / Denylists

Create subsets of the datasets options that can be applied to different fields when the dataset is in use.

Dependencies tab

• Dependencies tree

In this section, you can establish dependent relations among datasets.

• Allowed options

Define the options that are available for the dataset based on the dependencies you built. 

Note: When you work within the Dataset manager, any edits made to it will affect all instances of that dataset, even if it is used in different fields. However, dependencies work slightly differently as they must be explicitly included in a field. Hence, if you intend to use a new dependency for a dataset in use, ensure to include them from the field where the dataset is being used.

Used in / Shared in tab

It provides an overview of where a dataset is currently in use. 

Dataset

Dataset options

In this section, you can manage dataset options.

Here's what you can do:

• Adding

Click 'Add option' to create a new one. Newly added options are placed at the bottom of the list and marked with a 'New' label. To view an option you just added, you can click on 'Go to bottom' from the confirmation snack bar.

Note: Remember to review any dependencies if you are adding new options to a dataset that is part of one. 

• Editing

Simply click on an option to edit it. To save the edit, you can either click on the checkmark (✓) or press Enter. 

• Deleting

Click on the trash icon on the right to delete an option. If you delete an existing option, it will be highlighted in red, giving you the chance to restore it before publishing.  

• Restoring

For existing options, you also can restore them after you edit or delete them. Hover over the restore icon to see which values will be restored. The pipeline character (|) separates friendly values from abbreviations.

• Reordering

Drag and drop the options (from the dots on the left) to easily reorder them.

• Sorting

To sort, click on the meatball menu (...) > 'Sort alphabetically'

To maintain data consistency when handling options, especially when it is essential to save changes and publish them at once. This also means that all changes you make will be updated across the project immediately upon publishing.

Note: Adding, editing, deleting, and restoring can also be done directly from the 'Allowed options' when working with dependencies. 

Importing dataset options

It is also possible to import and review options from a .xlsx file instead of adding them manually.

Click the meatballs menu (...) > 'Import'

This opens a pop-up window to let you either download a template or upload your own file. Follow the instructions on the screen. 

Note: You can also include dependencies when importing options from an .xlsx file.  

From the import modal, click 'Include dependencies' > Select up to 3 dependencies > Proceed as usual.

Learn more about importing a dataset here. 

Exporting dataset options

You can also export your dataset options to an .xlsx file.

Click the meatballs menu (...) > 'Export'

Tip: Publish the dataset before exporting to ensure that all current data options are included. 

Settings

Use abbreviations

It adds a column on the right of the value where you can define abbreviations. 

Abbreviations are normally used in the final output, e.g., in tracking codes, and are shown in full (friendly value) within the platform to ease readability for users.

Best practice
Whether to use abbreviations or not depends on the structure of your output. 

For example, given a tracking code:
1. If you plan to use ID values (gclid, fclid, cid, etc.), there is no need to use abbreviations.
2. If you do not use ID values but plan to combine parameters, you should consider using abbreviations.

Indeed, if you want to capture a lot of information without using IDs, you may end up with an overly long tracking code that combines numerous query parameters. Abbreviations can be used to avoid this.

Rules

Rules enforce simple validations on your value options, either correcting the user when typing or displaying a warning if something does not comply. 

Note: When abbreviations are in use, the rules apply only to the abbreviated values. 

Here is a list of available rules for Datasets and their standard behavior:   

• Letter case: Enforced while typing.
• Limit character range: A warning is displayed.
• Replace characters: Enforced while typing.
• Set allowed characters: A warning is displayed.

Note: Rules do not apply retrospectively; they take effect on new options or after you make an edit to existing ones.

Allowlists / Denylists

An Allowlist / Denylist is used to define a subset of dataset options that are explicitly allowed or hidden. By applying these lists, you can ensure that only relevant choices are displayed in different fields where the dataset is utilized. This ensures data consistency while allowing flexibility in dataset usage.

Learn more about Allowlists / Denylists here. 

Dependencies

Dependency variant

A dependency variant serves as a unique configuration of dependencies within a dataset. By creating different sets of dependencies, you can tailor the relationships between datasets to suit different needs in various scenarios. This allows for flexibility while ensuring consistency in the data across various fields.

Think of dependency variants as containers where you can store distinct configurations of your dependencies.

Dependencies define the relationships between different options in datasets. For example, when a dataset is used in a dropdown B, dependencies determine which options are available based on the selection made in another dropdown A.

By creating different sets of dependencies, you can customize how these relationships between these fields work in different situations. For instance, you might want one set of dependencies for a specific field and a different set for another field in the Create form, even if they both use the same dataset. 

To learn more about configuring dependencies within, proceed to the section here below. Alternatively, you can learn more about dependency variants here. 

Note on terminology:
• Dependency variant
Variation of a set of dependency relations within a dataset. Each variation represents a unique configuration of dependencies that can be tailored to different needs. E.g, Variant 1: Dataset C depends on Dataset A. Variant 2: Dataset C depends on Dataset B.

• Dependency relations/dependencies
It denotes the dependent relations between datasets. E.g., Dataset C depends on dictating dataset A.

• Permutations/dependency combinations
It denotes the table of permutations between datasets. In simple words, the specific sets of selections in the dictating dataset that determine different combinations of allowed options in the dependent dataset.

• Dependency options
It denotes the (sub)set of value options in the dependent dataset that are part of a dependency.

• Allowed options
The options available in the dependent dataset based on the combination of options selected in the datasets it depends on. 

Example
Variant: Variation 1

Dependencies: 
Dataset C options (a, b, 1, 2, #) depend on the selected option in Dataset A (Letters, Numbers) 

Permutations: 
• For option 'Letters' of Dataset A, the following options are available for Dataset C 'a, b' 
• For option 'Numbers' of Dataset A, the following options are available for Dataset C '1, 2' 

Dependency options: 
'Letter, Numbers' and 'a, b, 1, 2'

Variant: Variation 2

Dependencies: 
Dataset C options (a, b, 1, 2, #) depend on the selected option in Dataset B (Characters, Symbols)

Permutations:
• For option 'Characters' of Dataset B, the following options are available for Dataset C 'a, b, 1, 2'
• For option 'Symbols' of Dataset B, the following options are available for Dataset C '#''

Dependency options
'Characters, Symbols' and 'a, b, 1, 2, #'

Dependencies

A dependency is a relationship where an element (dependent) relies on the changes in another element (dictating).

In Accutics Standardize, a dependency allows you to define which set of options are available for a certain dataset B (dependent) based on the selection made in another dataset A (dictating).

To illustrate, consider a Create form with two dropdown fields - Field A and Field B where the dataset in Field B depends on the dataset used in Field A. The options you see in Field B will change depending on the choice you made in Field A. 

For instance, selecting 'Letters' in Field A will display 'A' and 'B' options in Field B, while selecting the 'Numbers' option in Field A will display '1' and '2' in Field B. This interconnection illustrates the concept of dependencies.

To create dependencies, navigate to a `Dependency variant´ tab of the dependent dataset. Then:

1. Build the dependency relation

Search and select any dictating datasets > 'Publish'

Note: You can add up to 3 dictating datasets.

2. Define a combination of options

Select the options in the dictating fields that should lead to the allowed options for the dependent dataset. 

3. Assign allowed options

Assign allowed options by linking the option of the dependent dataset to the current selection of options in the dictating datasets.

4. Set different combinations

Repeat steps 2 to 3 as needed. 

5. Publish

Note: Ensure you have existing dataset options before building a dependency.

Adding new dependencies

You can create new dependencies for a dataset that is already in use. However, keep in mind that each field using the dataset will need to be reviewed if you wish to also include the newly created dependencies.

Navigate to the field where the dataset is in use > Click the cog icon to access its settings > 'Include dependencies'

Note: For shared datasets, you can find a list of all places where they are in use under the 'Shared in' tab. Hover over a row to access the 'Open' button and be redirected to the specific instance. 

Learn more about the include dependencies setting here. 

Editing dependencies

Once you add dictating datasets in the dependency and then publish, the section will lock.

At that point, you can edit the dependency options (create new combinations, manage allowed options, etc.) but you cannot edit the relation itself (adding or removing an independent dataset).

To edit the structure of the relationship, you'll need to reset the dependency first and then start over.

Resetting dependencies

The dependencies of a dataset can be reset only when they are not used in any field in the project configuration, including both live and draft versions.

To reset dependencies, simply click the more menu (...) from the 'Dependencies' tab > ‘Reset dependencies’.

If the dataset is in use, you’ll need to exclude its dependencies from all fields where it was added first. 

Hence:

1. Locate the field where the dependencies are in use in the project configuration
2. Click the cog icon to access the field settings
3. Uncheck 'Include dependencies' > 'Apply'
4. (Repeat for all other fields)

Once you excluded the dataset dependencies from all fields, you can proceed with the reset as explained earlier. 

Note: For local datasets, when attempting to reset dependencies that are in use, a modal will list all fields where they are applied. Simply click on an entry in the list to be redirected to the relevant field.

For shared datasets, you can find a list of all places where the shared dataset is in use under the 'Shared in' tab. Here, a column also indicates whether that instance also applies dependencies or not. Hover on it to access the 'Open' button and be redirected to the specific instance.

Creating and managing dataset options

You can also create, edit, and delete options directly from the dependency variants tabs. This streamlines the process, eliminating the need to first create dataset options and then update dependencies. 

When adding allowed options for different dependency combinations:

Create a new dataset option

Click 'Add option' > 'Create new option' > Enter a value (and an abbreviation) > Enter

Edit an existing option

Simply click on a value > Edit it as needed > Enter

Delete an existing option

Click the more menu (...) next to the option > 'Delete'

Note: Deleting an option (trash icon) permanently removes it from the dataset. This affects all fields and dependencies where the dataset option was in use.

Removing an option means removing it from 'Allowed options' in dependencies and it only affects that particular combination.

Other actions

From the more menu, you can also access other actions: 

• Import

This allows you to import to the dataset with dependencies from an .xlsx file.

• Export

It exports the dataset with dependencies as an .xlsx file. 

Used in / Shared in

The 'Used in' or 'Shared in' tab within the Dataset managers outlines where each dataset is used. This allows you to know which fields or projects may be affected by any edits or need to be reviewed to adjust dependencies. 

For local datasets, open the Dataset manager > Switch to 'Used in'

For shared datasets, open the Dataset manager > Switch to 'Shared in'

Here you'll find a table detailing information about where the dataset is used. Each row corresponds to an instance and the columns include information about its location and which dependency variants are in use. 

Hovering on an instance will show an ‘Open’ shortcut. This redirects you to the specific instance, enabling you to quickly navigate to it and make adjustments.