This glossary explains the meaning of the most important terms used in Adverity.
Adverity Data Storage contains data that you have collected using your datastreams, provided you have enabled Adverity Data Storage for your datastreams. You can work with the data that you have loaded into Adverity Data Storage in the Use Data platform area. For example, you can visualize this data in Data Explorer and Dashboards or send it to BI tools using data shares.
You do not need to load data into Adverity Data Storage in order to load it into external destinations.
For more information, see Loading data into Adverity Data Storage.
For more information, see Setting up an authorization.
Most give sAdverity access to multiple items that are linked to the , such as accounts or profiles. In Adverity, these items are known as Billing Objects. When you create an , you choose which Billing Objects Adverity can access.
For more information, see Combining data extracts using Bundle.
Calculated metrics are metrics created based on a combination of other metrics, such as a sum or a ratio. Calculated metrics can be loaded into Adverity Data Storage but not external destinations. Adverity displays calculated metrics in green with the sign.
For more information, see Calculated metrics.
A dashboard is a collection of widgets organized to present your data. You can share a dashboard to collaborate on it with other Adverity users, and to allow external users to view it.
For more information, see Creating a simple dashboard.
For more information, see Introduction to the Data Dictionary page.
A data extract is a file that contains the data collected during a fetch. A fetch can create one or more data extracts. Data extracts are saved in CSV format.
For more information on viewing and downloading data extracts, see Working with data extracts
Data Mapping maps source fields to target fields in a datastream to apply standard naming conventions. This enables you to easily compare similar data from different data sources. Only mapped fields can be loaded into Adverity Data Storage or a destination.
Adverity automatically maps source fields for which Default Data Mapping is defined.
For more information, see Sending data to BI tools with data shares.
A data source is an external tool that contains the data that you want to collect using Adverity (for example, Facebook Ads or Instagram Business). Create a datastream to configure your data collection settings and start fetching data from your data source.
A datastream is how you collect data from a data source in Adverity. You can create multiple datastreams for the same data source, for example to collect data from different accounts in separate datastreams.
The datastream configuration defines the following settings, among others:
The data that Adverity collects
The time period for which Adverity collects data
How often Adverity collects data using this datastream
For more information, see Creating a datastream.
Default Data Mapping
Default Data Mapping automatically maps source fields to target fields. Adverity provides Default Data Mapping for the most commonly used source fields for a number of connectors. You can configure Default Data Mapping for any source field that you have fetched in Adverity.
For more information, see Default Data Mapping.
An external destination is an external tool (e.g. Looker Studio) into which you can load the data you have collected in Adverity.
If you use the Management API, destinations are called Targets.
For more information, see Introduction to the Destinations page.
A dimension is a field that contains text values (strings). Dimensions cannot be used in mathematical operations. Adverity displays dimensions in blue. All fields in Adverity are either dimensions or metrics.
An enrichment is a set of instructions that defines how to transform your data. Enrichments must be assigned to a datastream in order to enrich your data.
There are two kinds of enrichments:
Adverity's standard enrichments, which are a user-friendly way to create commonly used enrichments.
Custom scriptenrichments, which allow you to transform your data using a range of instructions.
For more information, see Enriching data in Adverity.
A fetch is the process of collecting data using a datastream. There are two types of fetches:
Manual - start a single fetch manually
Scheduled - schedule regular, automated fetches or schedule a one-time fetch to run at a specific time
For more information, see Manual and scheduled fetches.
An instance is your Adverity infrastructure environment. An instance can be hosted by Adverity, in your cloud repository, or on-premise. You can reach your instance using its URL, e.g. https://my-organization.datatap.adverity.com/my-workspace.
The Management API lets you use Adverity through API requests, instead of the Adverity user interface.
For more information, see Getting started with the Management API.
A mapping table is a table that links source values to target values. Mapping tables are used in enrichments. For example, you can use a mapping table to map country codes (e.g. DE) to country names (e.g. Germany) to include this new data in your data extract.
For more information, see Creating and applying mapping tables.
A metric is a field that contains numerical values. Metrics can be used in mathematical operations. Adverity displays metrics in green. All fields in Adverity are either dimensions or metrics.
The platform areas, which each contain a number of pages. Generally speaking, you perform the following tasks in each page:on the left is divided into three
Connect Data, made up of the following pages:
Datastreams - collect data from your data sources
- create and manage your s
Activity - view the status of your tasks
Manage Data, made up of the following pages:
Enrichments - create enrichments to transform your data
Data Dictionary - manage your target fields and calculated metrics
Use Data, made up of the following pages:
Data Explorer - create widgets to analyze and visualize your data
Dashboards - create dashboards to present your data
Destinations - load your data into external tools
Data Shares - send your data to BI tools
Depending on how Adverity is implemented for your organization, you see all or some of these features. To get access to Adverity platform areas and pages, please contact us.
For an overview of the Adverity workflow and UI, see Get to know Adverity.
The Performance Manager shows all the scheduled and manual tasks that were performed in your current workspace within the last 24 hours with useful extra information, such as alerts and recommendations. Use the Performance Manager to identify opportunities to optimize configurations or schedules.
For more information, see Using the Performance Manager.
The root workspace is the top-level workspace in your organization, as shown in the example below. Users who are assigned to the Administrator group in the root workspace have additional permissions. For more information about user permissions, see Managing user permissions.
For more information, see Setting up storage for data extracts.
A target field is a field in Adverity that you can use to harmonize data from different data sources. Target fields can be loaded into Adverity Data Storage and external destinations. Source fields are mapped to target fields according to the Data Mapping you apply to your datastream.
For more information, see Creating and editing target fields.
You can see all tasks performed using a datastream in the datastream overview, or see all the tasks performed in your current workspace in the Activity page.
For more information, see Introduction to the Activity page.
A unique data row is a single row of data that is collected in a datastream after any enrichments are applied. A row is considered to be a unique data row if at least one of the fields in the row contains a value that is different from all other collected rows of data.
A user's monthly volume of unique data rows is determined by counting only the new unique data rows created in a calendar month. Rows that have been refetched, and rows that are identical to previously fetched rows will not be counted as unique data rows.
For more information, see Measuring Adverity usage with unique data rows.
A value table is a list of values that you can use for various purposes in Adverity, such as populating mapping tables or configuring data collection settings.
For more information, see Creating value tables.
Visualization data set
In Data Explorer, a visualization data set is a set of values that you select from the data table to include in the visualization.
A widget is an individual element on a dashboard. Use Data Explorer to create table and chart (visualization) widgets based on your data. You can also add image, video, and text widgets to your dashboard.
In Data Explorer, you create and edit widgets in a view. Only views that are added to a dashboard as a widget are saved.
To get started creating widgets, see Creating simple widgets.
A workspace is where you collect, enrich, and work with your data. You can configure Adverity settings for your workspace. Your organization may also use workspaces to manage access rights. Workspaces are created in a tree structure, where child workspaces may inherit some settings and permissions from the parent workspace.
For more information, see Creating workspaces.