The use of the word “versus” would imply that these two technologies – Customer Data Platform (CDP) and Master Data Management (MDM) – are competing against one another. This is just not the case. While they do share some similar functionality, they are neither mutually exclusive nor inclusive of each other. To better understand both the similarities and differences between these two technologies, we’ll start by defining them.
Master Data Management
Gartner defines MDM as, “…a technology-enabled discipline in which business and IT work together to ensure the uniformity, accuracy, stewardship, semantic consistency and accountability of the enterprise’s official shared master data assets.” But what does that all mean? Let’s break that down based upon the five “V’s” of big data: Volume, Variety, Velocity, Veracity, and Value.
MDM handles multiple domains imperative to the business that stretch beyond the customer and include both internal and external data. Its chief objectives are to unify data across the organization and to create a standardized data taxonomy. That standardization is then adopted throughout the organization and affects everything from asset management, finance, and HR to marketing and analytics. That is to say, MDM can include everything related to products, inventory, procurement, and sales while also including employee information, payroll, and locations.
Given the variety of data included in MDM, it is implied that we are talking about extremely high volumes from multiple source systems including payroll, CRM, data warehousing, and external partners and/or providers.
Accuracy is key, especially when we are talking about an enterprise solution with so many downstream dependencies. Collecting disparate data and organizing it is not enough. MDM strives to create that single source of truth to ensure both semantic consistency as well as data accountability.
MDM empowers different departments (with different corporate interests) the ability to work together under a single master data pool. Data can be shared with a variety of analytical, operational, and marketing applications.
MDM solutions do quite a few amazing things, but speed isn’t always their strong suit. As one can imagine, implementing a solution that encompasses so many facets of the business can take a significant amount of time. An organization has to be fully committed to the solution and procure the proper resources. Even then they can face lengthy implementation timelines with extended discovery, documentation, design, and implementation phases.
Customer Data Platform
Once again, Gartner defines a CDP as, “… a marketing technology that unifies a company’s customer data from marketing and other channels to enable customer modeling and to optimize the timing and targeting of messages and offers.” The CDP Institute further defines a CDP as, “… packaged software that creates a persistent, unified customer database that is accessible to other systems.” Most CDPs classify data into three buckets; profiles, engagement, and other.
A Profile can contain as much, or as little, personal information as the organization has on an individual. Data collected through multiple sources can use identity resolution algorithms to combine like data and create a “master record.” What this means is that CDPs are capable of handling unknown consumers and later resolving them to known customers. A combination of online and offline data is unified to create a single marketing view of the customer.
Core to any CDP, Engagement consists of almost any action a consumer can or will have with an organization. This can include sales, site visits, social interactions, survey responses, CRM responses and much, much more. It determines brand affinity and is used to drive 1:1 personalization across all marketing channels via audience orchestration and journey activation. It even drives analytics, AI and machine learning by cross referencing the different engagement types.
Vague, but no less important. The Other data classification is for anything that does not pertain to the customer nor to the engagement data. A few examples include product catalogs or retail locations. This information can be used in 1:1 personalization, AI, and ML to drive product or engagement recommendations.
Customer Data Platforms are marketing platforms focused on the customer experience. A CDP creates a golden marketing record for both known and unknown (anonymous) consumers. It applies AI and ML to help orchestrate audience selection and drive activation throughout marketing channels. An MDM solution is a business tool that extends beyond a single domain to include cross-organizational data with an emphasis on veracity.
MDM implementations are lengthy and require highly technical resources throughout the implementation lifecycle. Those same resources are required as new sources are introduced. Moreover, a change in MDM strategies could require fundamental process changes for the business which can derail an implementation, slow adoption and increase costs. CDPs can provide quick implementation timelines that can have an organization online and recognizing revenue in weeks instead of months. The UI typically caters to marketing professionals and provides out-of-the-box data connectors with drag-and-drop interfaces capable of identifying and unifying new data sources.
An MDM is more traditionally a back-end technology. A CDP is a front-end, UI driven solution. MDM can layer in analytics, data sciences, segmentation, orchestration, and even activations, but require additional products and/or tools, whereas a CDP can provide many of these in their marketer-focused UI.
Both MDM and CDP are built to handle extremely high volumes of data. An MDM solution will process those large volumes of data in batch while CDP can process data in both batch and real time.
With so many differences, it is easy to lose sight of the things they have in common. For example, both offer data unification capabilities at scale. They both offer identity resolution for improving data quality. They can both be used to drive audience orchestration for marketing purposes.
Complementary, not Contradictory
Any data-driven technology solution is only as good as the data put into it. CDPs are no different. This makes an MDM solution the perfect data source for any CDP implementation. With a focus on quality, MDM helps ensure that the data provided for marketing meets organizational standards. It can provide familiarity for those CDP users not accustomed to working in marketing specific technologies. CDPs can layer in marketing specific identity resolution algorithms that are separate from the organizational definition of an individual.
However, MDM is not a requirement for a CDP. A CDP by itself can be capable of data unification, data aggregation, AI and ML segmentation, audience orchestration, channeled activations and analytics. Even if the data sources included in the implementation are less than perfect, smaller hygiene solutions may be more beneficial and faster than taking on a full MDM implementation.
When determining what is best for you and your organization, a good rule of thumb is to answer this question, are we looking for a marketing solution, an IT solution, or both?