Get to know Adverity

Welcome to Adverity! This guide provides a high-level overview of how data moves through the platform and links you to the essential how-tos.

What can I achieve with Adverity?

Before you start working with Adverity, your marketing data may be spread across a number of different data sources, such as Facebook Ads or Google Ads. With no easy way to compare data from different data sources, it can be challenging to make sense of your marketing data.

With Adverity, you can collect the data you want, transform it so that it meets your needs, and easily compare, analyze, and present marketing data from as many different data sources as you like.

Data in Adverity is organized by workspaces. For help choosing the right structure, see Best practices for workspace structure.

Platform areas at a glance

Before getting started, it is helpful to understand how to navigate in Adverity. The platform is divided into three platform areas - Connect Data, Manage Data and Use Data. Each platform area contains multiple pages.

In Adverity, simply click on the name of a page in the platform navigation menu on the left side of the screen to go to the selected page.

Connect Data

Configure datastreams, manage authorizations, and monitor performance in Activity. For operational insights, see Introduction to the Activity page and Using the Performance Manager.

Manage Data

Create and reuse transformations, maintain the Data Dictionary, manage monitors, and manage mapping and value tables.

Use Data

Query data using Data Conversations, manage destinations, and work in Explore and Present.

Adverity data flow overview

This section provides a high-level overview of how data moves through Adverity.

Authorize access

Enable secure connection to your data sources and destinations. Allow Adverity access by creating or selecting an authorization (reusable across datastreams).

If you want to learn more about authorizations, see Authorizations overview.

Collect data with a datastream

Create your collection pipeline and first data extracts. Configure a connector and authorization, choose fields, and run a fetch. Data is stored in local storage as data extract files.

If you want to learn how to create a datastream, see Creating a datastream.

If you want to configure fetch types and scheduling, see Manual, Smart, and Custom fetches.

Monitor data quality (Optional)

Catch issues early, right after collection. Use monitors to detect anomalies before processing and loading.

If you want to learn how to configure data quality checks, see Introduction to the Data Quality page.

Transform data (Optional)

Prepare data for consistent mapping and downstream use. Apply transformations to clean, enrich, and standardize values before loading.

If you want to learn how to enrich and clean data, see Transformations overview.

Map source fields to target fields

Ensure only standardized target fields flow to analytics and destinations. Use Data Mapping so similar concepts across sources align (for example, “spend” and “cost”). Only mapped target fields can be loaded and analyzed.

If you want to learn how to harmonize fields, see Harmonizing data.

Store data in a warehouse for Adverity analytics

Load data into a warehouse that powers Data Conversations as well as Explore and Present. Use a warehouse managed by Adverity or connect your own external warehouse.

If you want to learn how to load data into a warehouse, see Load data into a warehouse.

Load to additional external destinations (Optional)

Make standardized data available in external tools. Send data to BI tools, files, or databases (for example, Looker Studio, Tableau, S3, BigQuery).

If you want to learn about sending data to external tools, see Destinations overview.

Get data insights with AI (Optional)

Get insights using natural language on data loaded to a warehouse.

If you want to get access to your data with natural language, see Data Conversations.

Analyze and present (Optional)

Explore answers interactively and share results with stakeholders. Build widgets in Explore and assemble dashboards in Present using mapped data from ADS or your warehouse.

If you want to create analyses, see Explore overview.

If you want to build dashboards, see Present overview.

First-time setup essentials

Use this checklist to complete the essential setup. Each item links to the full guide and a Getting Started page covering the step, when available.

1. Create a workspace

   Set up your environment for data collection.

2. Set up an authorization

   Give Adverity permission to collect data from your data source.

3. Create a datastream

   Create the pipeline that fetches data from your chosen data source with the right fields and schedule.

   Covered in Getting Started: Creating a datastream.

4. Fetch and preview data

   Run a manual or scheduled fetch and preview collected data extracts to verify content and structure.

5. Apply Data Mapping

   Harmonize source fields into standardized target fields so data from different sources can be compared and loaded.

   Covered in Getting Started: Applying Data Mapping.

6. Transform and validate data (Optional)

   Make sure data meets all your requirements:

   • Transforming data in Adverity

   • Introduction to the Data Quality page

7. Load data into a warehouse

   Load data into a warehouse so it is available for Data Conversations, Explore & Present.

   Covered in Getting Started: Loading data into a warehouse.

8. Use your data for analysis

   Once your data is ready, use it to get insights—starting with Data Conversations, work in Explore & Present, or send data to external tools:

   • Using Data Conversations

   • Introduction to Explore

   • Introduction to the Present page

   • Setting up destinations

   Covered in Getting Started:

   • Loading data into external destinations

   • Creating widgets and a dashboard

What’s next?

Continue the Getting Started path: Creating a datastream.

Or, explore some deep dives:

Transform your data

Enrich or modify data to meet your needs. See Transforming data in Adverity.

Monitor data quality

Make sure the collected data meets your standards. See Introduction to the Data Quality page.

Create and edit target fields

Define standardized dimensions and metrics used in mapping and analysis. See Creating and editing target fields.

Use Default Data Mapping

Use and adjust connector-provided defaults to accelerate mapping. See Default Data Mapping.

Manage workspace structure

Organize teams and access using a scalable workspace hierarchy. See Best practices for workspace structure.

Manage users in Adverity workspaces

Configure user settings and permissions. See Managing users.

Performance optimization

Monitor task health and remedy bottlenecks across fetch, transform, and load. See Using the Performance Manager.