Adverity for Improvado users

This guide is designed to help users familiar with Improvado to start using Adverity.

Introduction

This guide explains how concepts and processes in Improvado are comparable to the features available in Adverity. Our aim is to make it as simple as possible for you to migrate from Improvado to Adverity.

To start your migration from Improvado to Adverity, take a look at the guide on our website: Adverity or Improvado?

Structure

Adverity’s workspaces give you optimum control and flexibility when organizing the workspace structure within your organization. In Adverity, the workspaces in your organization are set up in a tree structure, with one root workspace and multiple child workspaces. These come with the following benefits:

• Settings and elements within the platform can be used across multiple workspaces in your organization.

• Data Mapping in Adverity helps you to harmonize data that you collect from different data sources throughout your organization.

For more information about workspaces in Adverity, see our guides to Creating workspaces and Workspace settings.

The table below compares the structures used in Improvado and Adverity. The diagram shows a visual comparison of the workspace structure in Improvado and Adverity.

Improvado structure	Adverity structure
Individual, unconnected workspaces on the same hierarchical level	Hierarchical tree structure with workspaces on different levels
Limited options to share data and some objects between workspaces	Configure and use datastreams, authorizations, transformations and Data Mapping across workspaces
Users can be assigned to one or more specific workspaces	Use data and other content from child workspaces in your current workspace

Collecting data

In Adverity, you create authorizations that allow you to collect data from your chosen data sources. You collect this data using datastreams.

For more information about collecting data in Adverity, see our guide to Creating a datastream.

The table below summarizes the differences between Improvado and Adverity, and the advanced data collection settings that Adverity offers. The diagram shows a visual comparison of the data collection process in Improvado and Adverity.

Improvado	Adverity
Select the accounts you want to collect data from	Authorize Adverity to access your data source using your credentials, or ask someone else (with or without an Adverity account) to authorize access for you - use an authorization to access your accounts without needing to choose them every time
One data table contains multiple ‘orders’ (individual reports for each account from each data source)	Creates a single datastream to collect the selected fields and present them in a data extract
Fetches historical data for up to 730 days	Allows you to choose how much historical data to collect, when to schedule these data fetches, and how often to collect data in future. Some data sources have limits on historical data fetches.
Select fields to collect and how often to refresh the reports	Offers complete customization and control over what fields are fetched from the data source and allows you to schedule fetches to keep your data up-to-date

Managing data

In Adverity, you apply Data Mapping to the data you collect in order to harmonize the information you fetch from different data sources. This means you can easily compare and visualize similar data, such as costs and clicks, from different data sources.

For more information about Data Mapping, see our guide to Applying Data Mapping to a datastream.

Once you have collected your data, you can use Adverity’s transformations to transform the data to meet your needs. Adverity offers standard transformations and custom scripts, which let you completely customize the data you have fetched.

For more information about transformations, see our guides to Using standard transformations and Using custom script transformations.

The table below summarizes Improvado’s custom fields and the ways you can manage data in Adverity. The diagram shows how Data Mapping can help you create a single source of truth for all your data in Adverity.

Improvado	Adverity
Uses the Improvado Marketing Common Data Model (MCDM) to structure data into ‘recipes’ for business use cases	Complete flexibility in how you add, remove and change fields in your data
Recipes can be applied to data tables	Data Mapping harmonizes data from all your data sources
	Standard transformations - a user-friendly interface to help you transform your data
	Custom script transformations - a wider range of transformation options using Python

Sending data to other tools

After collecting your data and making sure it meets your needs using Data Mapping and transformations, use Adverity’s destinations to send your data to other tools for further analysis.

Connect a destination to Adverity to send data to a wide range of external tools and databases. When you want to load data into your destinations, you simply choose the data you want to send and load it into your choice of external tool or database. For more information, see our guide to Loading data into destinations.

The table below summarizes the options available in Improvado and Adverity when sending data to other tools.

Improvado	Adverity
Transfer data into a destination independently from your data fetches	Assign one or more destinations to each datastream to automatically load data into these destinations when you fetch new data
Other tools cannot pull in data from Improvado	Some destinations, e.g. PowerBI and Tableau, automatically pull data from Adverity so you always have the latest data in your BI tool
	Proactively choose the data you want to load into an external destination