Understand the Benefits of the Data Model

Datorama is now Marketing Cloud Intelligence. 

Learning Objectives

After completing this unit, you’ll be able to:

• Explain how data is naturally harmonized using the Marketing Cloud Intelligence data model.
• Make sense of overarching entities in Marketing Cloud Intelligence.

So far you’ve learned what a data model is, how data models work within Marketing Cloud Intelligence, and how data streams facilitate data ingestion into the system. In this unit, we look at the benefits that the Marketing Cloud Intelligence data model offers. 

Data Harmonization by Mutually Mapped Dimensions

In digital advertising, the same campaign is usually active on more than one platform and associated with more than one data type. So the campaign entity (along with other ads entities such as site and media buy) is available in various data stream types. Mapping campaign name values from different sources to the same campaign name dimension in Marketing Cloud Intelligence—regardless of the data stream type, the source, or the number of sources—automatically harmonizes your data at the campaign name level for matching value, across the different sources.

For example, if the same campaigns are active on Google Ads, Marketing Cloud, and Google Analytics, when visualizing this data, you’ll be able to see measurements from all sources for matching campaign name values.

There are several benefits to this automatic harmonization of data in Marketing Cloud Intelligence.

• It structures and organizes your data and establishes clear relationships within it.
• It unifies business insights from different sources.
• It merges multiple data sources and formats, allowing these sources to be seamlessly compared and analyzed using uniform criteria.

Overarching Entities

For the purpose of higher-level analysis and classifications, the Marketing Cloud Intelligence data model has a set of entities that exist in all the data stream types, called overarching entities. Overarching entities are workspace level entities, not data stream level. This means they don’t just apply to data from a particular data stream, but rather to all the data in the workspace. This allows data from multiple sources to be classified or grouped by their shared values. This image illustrates the overarching entities and their relationship with the main entity of each data stream type.

An example of an overarching entity is the product entity, which harmonizes data across all data streams for matching product values. This image shows the same two products with data integrated from Facebook Ads, Marketing Cloud, and Google Analytics.

Overarching entities are at a higher hierarchical level than specific entities such as the campaign entity. A single product may be associated with multiple campaign values, which don’t necessarily have to be identical within their source platforms, as long as the product value itself matches. So, for example, you may be running campaigns on Facebook and Google Ads with different campaign name values in each source, both promoting the same product. These won’t automatically harmonize at the campaign name level since the values don’t match. But because it’s the same product, if its value is mapped to the product overarching entity in both cases, data from either source is harmonized for the same product. 

The beauty of the Marketing Cloud Intelligence data model is that for marketing-oriented data, matching dimension values are automatically harmonized, and matching measurements are automatically aggregated across multiple data sources. 

In this module, you learned what a data model is, how data is categorized into dimensions and measurements, how data relationships are defined, how data streams are structured and the benefits of the data model. Now you’re ready to manually map your data correctly using the data model if the need arises, as well as understand the ways in which your data from various sources can naturally harmonize.