gather360 Introduction

gather360 Introduction

The dataset settings define your structured data table, fields, validation and transformation.

This dataset can accept data uploads from contributing suppliers or users when published. When data uploaded are submitted into a dataset, gather360 manages the data upload in accordance with your settings.

A dataset has three possible states: live, draft or archived. Users can build new datasets or amend existing datasets.

Target Field

A target field is a column within a dataset. Target fields have four possible formats:

  • Integer

  • String

  • Date

  • Number

gather360 automatically verifies that fields in data uploads are in the same format as the target field.

Data Rules

Data rules govern the quality checks and format of your data. The user configures these rules to ensure data meets the desired target state. There are four types of data rules:

Mapping Rule

A mapping rule defines how source fields relate to target fields.

Filter Rule

A filter rule enables the user to define which rows of data should enter the data store.

Validation Rule

A validation rule specifies logical conditions that data must meet to enter the data store.

Transformation Rule

Transformation rules change data fields to ensure data is in the correct format. These rules can split or concatenate values and change field formats.

Data Error

A data error happens when uploaded data does not meet the conditions listed in a validation rule. gather360 flags data errors to the supplier or user uploading the data. There are two types of data error, a Warning and a Critical Error.

  • Warning: gather360 flags the data error to the uploader but won't prevent submission. The error can be optionally resolved and re-tested. If the user chooses not to fix this error, the data submission will be flagged and made available for analysis in the data layer.

  • Critical Error: gather360 flags the data error to the uploader and will prevent the data upload from being submitted. The user must resolve the error and repeat the validation test before they can submit data.

Data Requirements

Data requirements are a collective term for the settings in a dataset.

Data requirements include the data fields required, the structure of the data table, validation rules and transformation rules to be applied, and any required mappings.

Data requirements also explain the business purpose of the data set and/or data product required.

  1. A text description of the purpose of the data required and the end data product

  2. A text description of the current challenges faced in getting the data for this data requirement

  3. A list of the fields required and the expected data type, ideally with sample values

  4. A list of required validation rules

  5. A list of required transformation rules

Data Supplier

A data supplier is a team or organisation that provides data to your business but does not have a user account.

Data suppliers can be internal teams or external partners and vendors. Suppliers have a restricted account, so they can only view pending requests, upload data and view their previous data uploads.
The gather360 interface is designed to make it easy to collaborate with data suppliers. 

It does this in four different ways. 

1. Automating communication to suppliers to remind them what data is due, or alert them when data is overdue. 
2. Providing a detailed understanding of data requirements, and alerts when there are issues with data quality.
3. Enabling the supplier to resolve errors with data in a user-friendly, audited environment.
4. Enabling the supplier to take responsibility for technical changes like updated mappings, and configure their own automated ingestion method.

Data Request

A data request is a way of asking for a data upload in advance. gather360 tracks and reports on the status of all data requests within the system.

Data requests are assigned a supplier, who is the person or organisation responsible for providing the data, and have a due date. A data request can be single or recurring on a scheduled basis.

A data request has three possible statuses:

  • Pending: the due date is upcoming, and the requested data is not submitted.

  • Overdue: the due date has passed, and the requested data is not submitted.

  • Submitted: the requested data has been uploaded and submitted to the dataset.

  • You can request data from internal users or external data suppliers. Data suppliers cannot submit data to your dataset without a pending request.


A schedule is a cadence for a recurring data request. Schedules can be created independently of data requests, as some are utilised for multiple requests.

Users can configure daily, weekly or monthly requests.

Audit Certificate

Every row of data that is processed using gather360 has a unique Audit ID, called the g360 fingerprint. This ID is a complete record of where data originated from, how it was quality assured, and any processing actions that took place. This record is a gives a thorough and complete view of the context of each data point, including:
  1. The original row uploaded
  2. The supplier responsible for it & time of upload
  3. All dataset settings that were applied at the time (fields, mappings, validation and transformation rules)
  4. How these settings were tested
  5. Amendments that were made to correct errors
  6. The final committed row

KPI Dashboard

The KPI dashboard is a collection of metrics to measure and monitor the performance of your data supply chain.

These KPI metrics are a roll-up average of all the activity within your organisation's workspace, with associated drill-down metrics.

Data preparation time

This dashboard KPI measures the average time to prepare your data in the past four weeks.

It records the number of seconds, minutes and hours that elapse between when data was due and when preparation activities were completed, and data is committed to storage. Every data upload submitted to your workspace has a preparation time, and these are averaged to create your overall KPI.

Overdue requests

This dashboard KPI measures the number of open requests for data in your workspace that have surpassed their due date.

You can assign these requests to a supplier or a member of your workspace.

Delivered requests

This dashboard KPI counts the data uploads added to your workspace in the past four weeks.

Certified rows

This dashboard KPI counts the total number of rows uploaded to your workspace in the past four weeks. Each row is 'certified' as it has a unique Audit ID certificate detailing the provenance and quality assurance rules applied to it.

Rules coverage

This dashboard KPI calculates a percentage quality score for your datasets. The quality score compares the total number of fields in your datasets to the total number of rules and applies a status of 'High', 'Medium' or 'Low' coverage based on the number of rules you have in place.

Data Storage

gather360 processes data, without storing it. Data that is processed using gather360 is stored within a cloud storage account that is owned by the users' organisation. Data that is in the processing stage is stored separately to the final data output.

gather360 integrates directly with Snowflake and Microsoft Azure, alongside other options. 

Data Products

Data products are the end business use case for your data. Data products can be in the form of a dashboard, a report, an alert, a forecast, or any other data utilisation.

Datasets power data products. Data products can consist of one or many datasets.

Data Recipe

Data recipes are saved pre-configurations for business use cases in gather360.

These recipes include all dataset components, such as descriptions, fields and rules, end-state data views and template analysis packs.

Recipes install in one click via the gather360 marketplace.

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