Master data management (MDM) is a data-led discipline that is often driven by IT or enterprise architects and includes multiple stakeholders. The goal of an MDM solution is to create a single, organization-wide view of data that becomes the trusted and authoritative source to support operational and analytical systems across a variety of use cases. Many MDM solutions are used for creating trusted customer data out of data that is otherwise scattered across an enterprise, is inconsistent, and exists at different levels of completeness.
Customer data platforms (CDPs) are newer on the scene, born from marketing’s need to better understand interactions and more effectively execute prospect and customer marketing strategies. CDPs are packaged software with the sole purpose of improving personalization in marketing campaigns. (Find a more complete definition and criteria of CDPs in this article and at www.cdpinstitute.org.)
The definitions of CDP and MDM show some of the distinctions between these technologies, but to find out where the differences—and similarities—fall, we need to examine two factors: the data and the use cases.
MDM matches and merges customer records by comparing multiple data points, such as first name, last name, address, social security number, and/or phone number. CDPs, which focus on marketing data, can link data such as web session IDs by comparing a single data point such as an email or IP address, and confidently identify a customer or prospect.
The differences in the data also results in different conclusions by each system. The objective of MDM systems is to be able to identify whether two (or more) records are for the same person, product, supplier, location, or other entity in order to gain a single view. A CDP aims to better understand what or why a customer or prospect is doing something so a marketer can take the next best action.
In the example pictured, MDM identified that “John Edwards” and “J Edwards” are the same individual. The CDP identified that John is looking at a camping trip. MDM can confidently complete those records and even merge them into a single record based on governance policies. The CDP can initiate a campaign targeted at John for camping equipment. Both achieve important actions based on data, but for different use cases.
Traditionally, customer MDM has been very focused on customer engagement and driving increased loyalty by personalizing experiences for known customers and expanding into what’s known about prospects. This data is structured, has requirements around creating a single trusted view across sources of customer data, and requires governance processes. The end goal is to provide marketing, sales, support, finance, operations, and legal teams with the most complete and trusted information so they can do their jobs more effectively.
A current focus for many CDPs is to drive interactions with prospects in order to help them to become a customer faster. Data supporting this use case is typically much sparser and may include unknown customers with very little to no identifying attributes. To support this, CDPs use identifiers like email address, cookies, MAC addresses, and more to link the interactions associated with those identifiers and drive prediction models so you can take the next best action or send the right offer at the right time.
As marketing requirements have grown along with customer expectations, the MarTech stack has become more complex. The simple stitching together and linking of data based on exact criteria is insufficient to get the value promised by the first generation of CDPs. Next-generation CDP vendors—many with roots in data management and MDM—have added functionality around data quality and matching to address these new requirements.
For this reason, we are seeing the emergence of a new approach, one that blends the insights and context about prospect and customer behaviors found in CDPs with the ability of MDM systems to tee up the trusted customer profiles that modern companies need.
To fully understand the customer journey from prospects to customers—and provide meaningful engagements along the way—your long-term goal should be an end-to-end platform for capturing and linking all of your organization’s fragmented data. By mastering a contextual understanding of customer interactions, transactions, and intent, you can better address new use cases and answer new questions.
Learn more about the use of CDPs beyond marketing with this whitepaper by David Raab, founder of The CDP Institute.
Get an in-depth view of CDPs in 2020 and beyond from David Corrigan’s blog.
Discover the basics of CDPs in our reference article.
Curious about what MDM brings to an end-to-end data strategy? Our webinar series covers everything from MDM basics to the difference between MDM and data quality.