Adobe’s Data Prep for Data Collection

Our team was given the opportunity to be invited to the Adobe Data Prep for Data Collection beta program, which included increased features on their data streaming tool that looks like it will be helpful for users. This blog will give an overview of what the Data Prep for Data Collection tool is able to do.

XDM & Data Prep for Data Collection; What are they?

Before we get into the details, we need to understand what XDM is. XDM is Adobe’s Experience Data Model that utilizes a new standardized approach intended to help detail out customer experiences. The XDM system has schemas that support real-time customer profiles, machine learning, and customer Journey Analytics inside the Adobe Experience Platform (AEP), amongst others. Data Prep is a service for the new Adobe Experience Platform Edge Network, that lets us map different variables from your data layer or JSON object to the XDM schema and make them available for use in places such as the tags for Analytics, Target, Audience Manager, and even Experience Platform.

Providing meaningful data points through AEP lets Adobe make decisions on the fly. This data can be passed to Target to inform decisions on A/B testing on the fly. Or to Audience Manager to create traits without having to send files through FTP to the Adobe Servers. This gives companies and users quicker access to customer data to make better and faster decisions within Adobe products.

Creating A Schema

First a schema must be set up so that various Field Groups become available for mapping through the Datastreams. These Field Groups are pre-defined JSON objects that can have data mapped to them. Once the Field Groups are created through the schema, they become available as target fields that you can map data to using the Data Prep for Data Collection tool. Field Groups are reusable fields that contain variables that define one or more of the attributes for the schema.

An already set-up SchemaThe sets of behaviors used to build the dataset schema are called classes. These classes will help define business objectives. Those objectives can be used to inform the tracking and tagging for the site. This tracking can be done on the back end or on the site as the data is still going through the SDK to pass data to the appropriate tools.

The Datastream is where the magic happens for uploading a data layer as a JSON object. Once that data object has been verified, Source Fields are created from the provided JSON object and those are then available to be mapped to the Field Groups created from the schema. The uploading of a JSON object and the mapping tools are the parts that make up Data Prep for Data Collection.

JSON Object Uploader (File & Input Field)Source Fields are created from the uploaded data layer or JSON object provided by the user. Without providing that JSON object, mapping to the Field Groups cannot be done. Note that two Field Groups are required: a timestamp and an ID that must be set during the mapping. If those two Field Groups are not set, then data cannot be collected.

Mapping Tool (Source Field & Target Field groups)Calculated Fields are a powerful new feature available only through Data Prep. This new feature gives users functions and operators that can be used to manipulate data without having to write code. Once the Calculated Field has been created it becomes available as a Source Field that can be mapped to the Field Groups there were created from the schema.

Calculated FieldOverall, Data Prep for Data Collection is a great tool to add to your Adobe tool belt. Being able to take a properly structured JSON object or data layer and map it through an interface so that the data is passed along to the proper Adobe product is an excellent way to capture data points. This looks to be the future for Adobe, and I am excited to leverage this update to its fullest potential.