Bulk Import of Options

Learn how to bulk import value and also dependency options for Datasets. 

The bulk import feature allows you to add and update value but also dependency options at once by importing an .xlsx file into a Dataset. 

The term 'Options' encompasses both value options in datasets and dependency options in dependencies. In datasets, it represents the combination of friendly value and abbreviated value, if present. In dependencies, it represents the (sub)set of dataset options that are also part of a dependency. 

Note on abbreviations as key
A value option is often composed of a (friendly) value and an abbreviation. When both are present, abbreviations are used as a key. This means that an option is considered unique or a duplicate depending on the abbreviation.

Example (click here)
Option 1
(Friendly) value: Germany
Abbreviation: de

Option 2
(Friendly) value: Germany
Abbreviation: ger

These are two different value options because they have different abbreviations.

Implications for the imports
Using abbreviations as the key has implications for how the imports work. For example:

Adding new options
Since abbreviations are used as the key, you must not add new value options that have the same abbreviation but different friendly values. This is considered a duplicate error.

Example (click here)
Option 1 (import file)
(Friendly) value: German
Abbreviation: de

Option 2 (import file)
(Friendly) value: Denmark
Abbreviation: de 

This is a duplicate error due to conflicting friendly values. 

Updating an existing value
If the value option already exists, you can update the friendly values in bulk by simply importing the template with new friendly values while keeping the existing abbreviations.

Example (click here)
Option (existing in Accutics)
(Friendly) value: Germany
Abbreviation: de

Option (import file)
(Friendly) value: German speaking
Abbreviation: de

These are considered the same value options and the existing one is simply updated with the new friendly value from the import.

Access to import feature

The import feature can be accessed from various locations when working with a dataset:

• Datasets page: Locate a dataset > ... > 'Import'
• Dataset manager: From the 'Dataset options' section in the 'Dataset' tab > ... > 'Import'
• Input field (Project configuration): Add a select field > Add a dataset to the field > ... > 'Import dataset'

Note: For Shared datasets, the import feature is only accessible from the Shared datasets page under Account settings. This requires an Admin user role. 

Import of value options

Instead of inputting or editing existing value options manually, you can import an .xlsx template instead.

Hence, click the meatballs menu (...) > Import

This opens a pop-up window to let you download and update a template:

1. Download the template

The template is an .xlsx file comprising information about the division (account, division, workspace, project) and a section to be filled in.

2.  Fill in the template

 Simply fill in the columns with the friendly value and abbreviations (if the use of abbreviations was set) to create new options.  

Tips: Do not use the same abbreviation for two different unique values. 
Use the same abbreviation as the existing option if you simply want to update it. 

3. Upload the template

You can either drag & drop or browse to choose a file. Wait till the file is uploaded > 'Review'

4. Review the options

The review stage lets you manage the options you just uploaded. Warnings with a brief explanation will be displayed if the data cannot be accepted. In that case, adjust until all options are valid.

5. Finalize the import

Notice that the new options have been added and/or existing ones have been updated. 

Import of value and dependency options

It is also possible to upload an .xlsx file containing both value and dependency options. This allows you to add/update both simultaneously.

Hence, click the meatballs menu (...) > Import

This opens a pop-up window. Then:

1. Include dependencies and download the template

To include dependencies, click ‘Include dependencies’ > (Select dependency variant) > 'Download template' 

2. Fill in the template

The template includes the dependencies that are part of the dependency variant you selected and two columns to set the value options. Refer to the below section 'Template' for instructions on how to use it. 

3. Upload the template

You can either drag & drop or browse to choose a file. Wait till the file is uploaded.

If the import was not successful, you will be prompted with an error. If the errors are known, you will be able to download a report of the issues. Please, refer to the below section 'Template errors'. Then, review and re-upload. 

If the import was successful, check that the number of newly imported options is correct and continue to 'Review'.

4. Review data

This review stage gives you an overview of all the value and dependency options that have been imported. 

5. Finalize the import

Depending on what you imported, notice that new options have been added, existing options have been updated, and dependencies have been updated too. 

Template 

The template is an .xlsx file divided into two parts:

• Account information

The first part contains information about the account, division, workspace, and project (rows 1 to 4).

Note: If you are importing to a Shared dataset, the division, workspace, and project fields are left undefined since Shared datasets are account-level fields.

• Field values (value options)

This part of the template is pre-filled with the independent datasets you selected to generate the template. Here, you can add the options of the dependent dataset and then, reorganize them to build dependencies.

Remember? A dependency lets you build a relationship where the value options of a dependent dataset A depend on the value option of another dataset B. 

To build this relationship in the .xlsx file, you need to break down the value options by categories and subcategories. If you are familiar with pivot tables, you are essentially building nested rows.

1. Create value option

Create all the value options you want to add/update under the 'Value' and 'Abbreviation' columns. 

2. Add row labels

For each value option, you need to add labels. Labels are used to group data together based on common values.

For example, if you want to build a simple dependency with one independent dataset, the outermost row labels correspond to the values of the independent dataset A and the innermost row labels correspond to the values of dataset B (which are nested within the values of dataset A).

You can also add multiple row labels to create a more complex hierarchy. Hence, the outermost row label is at the top of the hierarchy, and the innermost is at the bottom of it.

Example

Let’s consider a manufacturing company GloveMaster that produces gloves. GloveMaster owns two brands, each with their product categories and specific products. We want to set up datasets that reflects this hierarchy. Hence, where a product dataset depends on the product category and brand. 

Brand 1: Frostbite

• Product Category: Winter Sport Gloves
  • Products: Explorer Pro, Blizz Stormblock, Svalbard GTX

• Product Category: City Gloves
  • Products: Urban Lite, City Touch, Fleece Heattech

Brand 2: Keepr Grip

• Product Category: Goalkeeping Gloves
  • Products: Aqua Shield, Goaliedor Evolution, UltraGrip Elite

Instead of manually inputting all value options and setting the dependencies, we could build this relationship in an .xlsx file and import it. 

Given that the 'Brand' and 'Product category' datasets already exist and that there is already a dependency set between the two, we want to import the options and set the dependencies for the 'Product'  dataset from a template file.

Note: You can find the template of the example attached at the bottom of the article. It includes an empty state, completed, and examples of errors. 

Hence, click the meatball menu (...) of the 'Product' dataset > 'Import'  

Then:

1. Include dependencies and download the template

'Include dependencies' > Select 'Brand' and 'Product category' > 'Download template'

2. Fill in the template

Initial template:

Completed template with new value options and row labels to build dependencies:

3. Upload
4. Review
5. Finalize

Template errors

When uploading the completed template, the process may fail due to two main reasons:

Broken file

When an error occurs without a known cause, it is most likely that something is wrong with the file. Please, make sure that you did not edit the format of the file, alter the main structure of the content, or delete any IDs. 

Invalid data

When issues with the template are known, it means that there are pieces of data that are invalid. Please, download the error report, correct your file where needed, and retry to upload it.

Here are some common errors:

• A value does not comply with dataset requirements

E.g., The dataset accepts only lowercase value options. The values in the file are uppercase.

• A row label is missing for dependencies

E.g., Brand:  'Brand1', Product category: '', Abbreviation: 'prod1'

The product category label is missing.

• Conflicting duplicate values

E.g.,

(Friendly) value: Germany

Abbreviation: de

(Friendly) value: Denmark

Abbreviation: de

The abbreviations, used as the key, are the same but the friendly values are not.