While there is some overlap between customer data readiness and master data management (MDM) for “party” entities (customers, members, patients etc.), the differences in approach, data managed, and outcomes are significant. And MDM and data readiness can be complementary: When an MDM is used to optimize customer data for CX or AI use cases, data readiness is an essential component.
An MDM creates an accurate customer “master” so that applications across the enterprise – data governance, supply chain, CRM, customer service and finance – are working with the same core understanding of each customer. In contrast, data readiness provides accurate, detailed, and current data to drive business (especially customer-facing) use cases.
Generally speaking, there are three key distinctions to make between data readiness – a data readiness hub – and an MDM solution.
Distinction #1: How Fast is Data Changing?
The first major difference between MDM and data readiness is the data itself! MDM is concerned with “master” data that defines the entity it is mastering: people, products, machines, and so on. Master data tends to be “slow-moving.” A name, an address, and birth date change slowly – or infrequently – if at all. Data readiness on the other hand cares a lot more about “fast-changing” data, i.e. customer attributes related to time, to money, to customer behavior, etc. Buying choices, social trends, and consumer interactions all fall into this category – and the history of the frequent changes tell a story about each individual customer. This is one important piece of context – the “long tail” – which distinguishes it from the slow-moving data.
Data that an MDM cares about tend to be core attributes attached to a person (or another party entity) – name, address, phone number, age. Compound attributes such as lifetime value (CLV) may be included, but an MDM does not calculate CLV, nor does it care about the transactions that contribute to CLV.
For instance, a healthcare practitioner will typically have all of the master data related to a core patient record. But the MDM will not care about how that patient record relates to the EHR system, nor have any information related to a patient’s pharmacy benefit manager (PBM). Making sense of the patient journey is where a data readiness hub comes in, which will use all relevant data to manage the journey.
The same holds true in retail. A pair of jeans will have attributes that are both fixed (color, size) and fluid (how many sold, how many in stock). Changing data may be tied to a product, but it may also relate to a customer’s interest in the product – time on the product page, the last visit to the website, order history, etc.
Customers, patients – and their attributes – are continually changing, and the story those changes reveal about a customer or patient journey are typically outside the scope of an MDM.
Distinction #2: Right Data vs. Fit for Purpose Data
Think of continually changing attributes – the “fast-moving” data – as “decorations” that provide the context that a marketer or business user of data needs to fuel AI and CX.
The need for a contextual understanding brings us to a second distinction; whereas an MDM and a data readiness hub both have an interest in having the right data, only the latter has a vested interest in ensuring that the data is fit for purpose, i.e., it is observable, it is actionable, and the right aggregates, models and calculations are attached.
Data lineage also fits into this category. An MDM solution will store an email address and potentially the source it came from; data readiness will attach a score for its trustworthiness, know when the email was last used with respect to a customer record, when an email to that address was last opened, etc.
Metadata fits into this realm to provide a layer of context to the underlying data. And while an MDM will define and own the standards for things like currencies (U.S. dollar vs. Euro) and measurement (meters vs. feet), these are also vital in the construct of data readiness. A data readiness hub will inherit the use of how an MDM defines standards, and carry over any changes. An understanding of these and other fields are important for determining how to interact with a customer where there may for instance be records for a primary address, a secondary address, etc.
Distinction #3: A Flexible vs. Fixed Understanding
The third key distinction between an MDM solution and a data readiness hub is that the latter must parse data in a way that an MDM might not necessarily care about. Interpersonal relationships, for instance, such as whether a unified customer profile represents an individual in the context of a household or organization, or whether data is PII or PHI and is thus subject to different rules for how to engage with the customer are both important to a data readiness hub, but not necessarily to an MDM solution.
An MDM is Not Data Readiness
Understanding the important differences between an MDM and a data readiness hub helps to answer the burning question of whether a company with an MDM solution needs a data readiness hub. The answer is – it depends. If delivering a personalized customer experience is not required as a customer data use case, or if an organization does not plan to use AI in a customer-facing way, then an MDM might suffice. The same might hold true if a “unified customer profile” only needs to include core “master” attributes like email address, name and primary phone numbers.
But if a customer-focused organization wants to have a contextual understanding to drive differentiated AI and CX use cases, an MDM solution is not enough. A company that tries to use an MDM to provide data readiness will be taking on a lot of (data) headaches. Data will not be in the cadence of the customer, will not be detailed and contextual, and will not be actionable – it will not be fit for purpose.
Data Readiness + an MDM
A company that has an MDM but cares about having its data both right and fit for purpose will, at a minimum, have to add all of the things that fall under the data readiness heading, i.e., the “decorations,” the customer behaviors, the customer history, permissions, interpersonal relationships, etc. That is where the data readiness hub comes in.
Beyond providing a more contextual, nuanced and rich understanding of the customer, a data readiness hub will also drive new and better information into an MDM. For instance, a company may want to know how many high-value customers it has. An MDM, however, does not care about individual transactions, which are a useful metric in calculating the value of a customer. In trying to calculate a number, the company polls different departments, but each has its own metrics for determining value-related figures – number of sales, average sale, cut-off dates, etc. Conversely, a data readiness hub that continually makes data fit for purpose can provide an updated number of high-value customers as part and parcel of its underlying purpose of developing a real-time, continuously updated unified profile accessible across the enterprise.
MDM & Data Readiness: Complementary, Not Redundant
While MDM and a data readiness hub both work with customer data, their purposes and strengths are fundamentally different:
- MDM ensures accuracy and consistency of core customer records across systems – a foundational need for governance, compliance, and operational integrity.
- Data readiness makes data fit for purpose, adding the context, timeliness, and behavioral insights needed to drive personalized CX and AI-driven use cases.
- Data readiness complements MDM, enhancing its value by continuously updating unified profiles with actionable insights, customer signals, and real-time context.
If your organization’s goal extends to more than just managing data to include using data to drive experiences, data readiness is not just optional, it’s essential.
