🚀 This is an early access feature
Mapping a dataset is a crucial step in preparing your dataset for downstream functionality such as project creation.
Mapping creates transparency between:
Your intended use of the dataset
Omnisient’s expectations for how the dataset should be structured (required columns and recommended additional columns)
Where to find Dataset Mapping
Dataset mapping is available in the following areas of the platform:
Location | How to access it |
My Datasets → Unregistered & Available datasets | Dataset row context menu → Map dataset (if no mapping exists) or Edit mapping (if a mapping already exists). Also available from the dataset detail view.  |
Own Data Preparation | Via the Dataset Mapper widget.   |
🔐 Permissions
Dataset mapping is enabled by default for Admin users.
Non-admin users can access the Dataset Mapper if they are assigned the permission.
How Dataset Mapping works
When you open the Dataset Mapper from either entry point, a bottom sheet will open with a two-step flow:
Select
Map
Step 1: Select
In this step, you choose the dataset you want to map, assign its dataset type, and answer a set of questions about the dataset.
Dataset selection behavior
Entry point | Behavior |
My Datasets | The dataset dropdown is pre-populated with the selected dataset. |
Own Data Preparation | The dataset dropdown displays all Unregistered and Available datasets within your organization.
To make it easier to differentiate dataset states, the dropdown includes an icon next to each dataset indicating whether it is Unregistered or Available. |
📚 Dataset types determine which fields are required and which additional fields are recommended during mapping. You can view supported dataset types and mapping requirements here.
Dataset questions
After selecting a dataset type, a set of questions appears. These questions capture information about how your dataset is shaped and how it connects to other datasets in your organization.
The questions shown depend on the dataset type selected. Not all questions apply to all types.
Question | Purpose | Applies to |
Identifies which dimension datasets this dataset links to.
Selecting Customer, Product, or Location determines which additional join fields are required in Step 2. | Transactional, Account Balances, Policy Membership | |
Is your data already aggregated by date? | Captures whether your data is pre-summarised by a time period (daily, weekly, etc.) or whether each row represents an individual transaction or event. | Transactional |
How many levels does your product hierarchy have? | Defines the depth of your product hierarchy from your highest level (e.g. Division) to your lowest (e.g. Barcode/SKU), counting every level in between including both ends.
Minimum 4, maximum 9. | Product |
How often do you plan to upload this dataset? | Records your expected upload cadence (Daily, Weekly, Fortnightly, Monthly, or Yearly) to help track data freshness and identify gaps over time. | All types |
Product hierarchy levels: Increasing the number of levels inserts additional level fields (description and code) into the Additional Fields section in Step 2.
Linked datasets
Selecting one or more dimensions under "Does this dataset join to any of the following?" reveals a sub-dropdown to specify which mapped dataset represents that dimension:
Selection | Sub-dropdown | Datasets shown |
Customer | Select linked Customer dataset | Datasets already mapped as Customer type |
Product | Select linked Product dataset | Datasets already mapped as Product type |
Location | Select linked Location dataset | Datasets already mapped as Location type |
💡 Only datasets that have already been mapped appear in these dropdowns. If you don't see a dataset, ensure it has been mapped first.
⭐ Map dimension datasets first, the datasets you want to link need to exist before you can reference them here.
We recommend following this order:
Map Customer, Product, and Location datasets before mapping Transactional datasets.
Map Customer datasets before mapping Account Balances or Policy Membership datasets.
This ensures the right datasets are available in the dropdowns when you need them.
Linked dataset selections carry forward into Step 2, see Dynamic required fields below.
Step 2: Map
After selecting the dataset and dataset type, Step 2 will automatically populate with the mapping fields for that dataset type. These fields are grouped into:
Required fields
Additional fields
Required vs Additional fields
Field type | Required to save | Notes |
Required fields | ✅ Yes | Must be mapped for the dataset mapping to save successfully. |
Additional fields | ❌ No | Strongly recommended to maximize the usefulness of the dataset. |
⭐ Recommendation
While only required fields must be mapped, we highly recommend mapping additional fields as well to get the most out of your dataset.
Dynamic required fields
When you link a Product or Location dataset in Step 1, additional fields are injected into the Required Fields section in Step 2. These fields represent the join keys that connect your datasets. This is the columns both datasets share in common that allow records to be matched across them.
Lowest level product code / identifier: Added when you link a Product dataset. Map this to the most granular product identifier in your data, such as a SKU or article code. This is the column your datasets will join on.
Site code: Added when you link a Location dataset. Map this to the column that uniquely identifies each location in your data. This is the column your datasets will join on.
If a linked dataset selector is left blank in Step 1, no additional required fields are added for that dimension.
Use as Dedupe
Every row in both the Required Fields and Additional Fields sections includes a Use as Dedupe checkbox. Marking a field as a dedupe field indicates that this column should be used to identify and remove duplicate records.
Validation
As you select columns in Step 2, the mapper validates each selection on change. Each field displays a status indicator:
Status | Meaning |
Red dot | Validation error, this field must be resolved before the mapping can be saved. |
Yellow dot | Warning, this field is marked as a dedupe field but has not been mapped. Map the column or uncheck "Use as Dedupe" to proceed. |
Green dot | Validation passed, this field is correctly mapped. |
Three errors or warnings can appear:
Error | When it appears | How to resolve |
Missing required field
 | A required field has not been mapped to a dataset column. | Select a dataset column for the required field. |
Types don't match
 | The mapped column's data type doesn't match the expected field type. | Select a different column with the correct data type. |
Unmapped dedupe field
 | A field is checked as "Use as Dedupe" but no column is selected. | Map the column, or uncheck "Use as Dedupe" if deduplication on this field is not needed. |
The Apply Mapping button is disabled while any red dots or yellow warnings are present. It becomes enabled once all required fields are mapped, all dedupe-flagged fields are mapped, and all interacted-with fields pass validation. Additional fields that are left unmapped and not marked for dedupe do not block saving.
Data Type icons
Each mapping field is paired with an icon representing its data type.
Use these icons to quickly confirm that you're mapping a dataset column to the correct field type and to ensure your mapping is consistent.
Icon | Dataset Type |
 | Boolean |
 | Datetime |
 | Decimal |
 | Integer / Number |
 | String |