Create Unified Individual Profiles
Learning Objectives
After completing this unit, you’ll be able to:
- Explain the benefits of unified profiles.
- Describe how to create unified profiles.
Data Cloud
Data Cloud is a powerful tool that can help unify your data across multiple systems. Unified profiles in Data Cloud combine data from those sources into a single profile based on user-identified identity resolution rules within a ruleset. However, to create those unified profiles, your data needs to be mapped correctly. In this module, we cover the concepts around data and identity—including unified profiles, data modeling, the Customer 360 Data Model, and identity resolution data mapping requirements. With an understanding of these important data unification concepts, you’ll be ready to make the most of Data Cloud.
Data and Identity
First, let’s watch an overview of data and identity.
Want to learn more about how to create your company’s data strategy? Check out the Trailhead module Customer Data Platform Strategy.
A Unified Profile
Meet Rachel Rodriguez, a customer (and super fan) of outdoor gear and apparel retailer Northern Trail Outfitters (NTO). NTO has data about Rachel in multiple systems like a customer profile in Commerce Cloud and Marketing Cloud Engagement, a customer support case history in Service Cloud, and more. However, each system has different information about her (like different email addresses). We call these unique pieces of data contact points (phone number, email address, or physical mailing address).
Customers, like Rachel, are represented by multiple contact records and system-specific profiles across various systems, which is necessary for each cloud and product to operate independently. For a marketer or service rep, connecting the dots in order to send a marketing campaign to Rachel or find a single view of her support history, can be tricky.
That’s where Data Cloud data mapping and identity resolution can help. A unified profile is composed of data from multiple sources linked together using identity resolution match and reconciliation rules. If the same data exists in multiple places, profiles are linked together based on established rules.
With identity resolution rules in place, NTO’s view of Rachel Rodriguez includes a unified ID from multiple sources.
Even better, as new profiles are added or existing ones are updated, you can view unified individual information from a tool called, Profile Explorer. Ensuring that the data you have is the most accurate representation of Rachel.
Creating a Unified Profile
So, how does it work? Regardless of whether you or another colleague is setting up your data, it’s helpful to understand the following steps and concepts before you begin your data modeling and mapping. So let’s review the implementation steps to get you from raw data to a unified profile.
Step |
Description |
|---|---|
Ingest raw data from data sources. |
Data is added from bundles, data extensions, Amazon Simple Storage Service (S3), and other systems as is. After raw data is added into the Data Cloud as a data stream, the data needs to be mapped to the data model. |
Map and model data. |
Customer 360 Data Model is the behind-the-scenes tool that allows data from multiple sources to be standardized into a readable format that can be easily mapped. Data from your data stream needs to be mapped to objects, like Party Identification and Individual, in order for identity resolution rulesets to work. |
Create identity resolution rulesets. |
After modeling and mapping steps are complete, create identity resolution rulesets. Match and reconciliation rules are added to help look for and unify profiles across your various data streams. |
Create unified profiles. |
After rulesets are set up, the system creates unified profiles that can be used for segmentation and in activations. |
Discuss Your Data
Now that you understand the concept behind unified profiles, what’s next? To be successful, it is important to spend time analyzing the data you want to use in Data Cloud. 
Grab your team, a whiteboard, and discuss the following questions.
- Where is your data located?
- List all locations including spreadsheets, S3, Salesforce CRM, Marketing Cloud Engagement, and so on.
- Do you have an asset inventory created for each data source?
- How do you identify individuals in each of your data sources?
- Do you use email, name, birthday, or a system ID?
- Do you use contact keys, lead IDs, or subscriber keys as a unique system identifier?
- What data is shared across systems?
- Are you consistently using first names, last names, or email?
- What does your customer journey look like?
- Have you mapped out every customer interaction?
- What data do you need for each of those interactions?
- What data do you truly need for audience segmentation?
- How is the data quality in each source?
- Are there misspelled words?
- What data is often missing (birthdays, phone numbers, or something else)?
Don’t skip this part! We promise it’s worth your time. Understanding your data is key to a successful Data Cloud implementation. In the next unit, we cover important data mapping considerations in order to create identity resolution rulesets.
Resources