Two popular solutions for solving the persistent problems of bad customer data are Master Data Management (MDM) and Customer Data Platforms (CDPs). While the two technologies aim to solve similar problems around poor data, there are important differences in how each platform approaches the challenge of creating a customer 360 data view. Chief data officers (CDOs), business analysts, and marketing specialists alike must weigh up the offering of each. This post highlights the differences between the two software types.
Defining MDMs and CDPs
CDP’s focus is streamlining customer data across sources to drive marketing actions targeted at prospects and customers. With clean and high-quality data as an input, CDPs can aggregate customer information with important interactions such as email clicks, mobile app usage, and customer service to create single customer views. This streamlined data can help marketing teams better segment the customer base, construct targeted marketing programs and ultimately, improve the customer experience.
A customer data platform (CDP) is a marketing system that unifies a company’s customer data from marketing and other channels to enable customer modeling and optimize the timing and targeting of messages and offers. (Gartner)
Master Data Management (MDM) is 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. (Gartner)
MDM’s objective is to provide consistent, clean, and uniform data, and unlock scalable analytical and operational data across the organization to drive business value. MDMs are tasked with raising the quality of various types of data – especially key entity types like customers, suppliers, products, and parts. A popular priority use case is mastering customer data to create a customer 360 view across the organization. Marketing is only one of several departments that might leverage mastered customer data. Another key focus of MDM is reducing the amount of manual effort needed to maintain and clean the data – a machine-learning approach to MDM like Tamr can reduce employee time spent on data aggregation and upkeep by up to 90%.
Key differences between MDM and CDP
In summary, the main difference between CDPs and MDM is where they focus efforts: CDPs focus on syncing sources and destinations, whereas MDM specializes in matching records and transforming the data to be more accurate and usable by the business.
Three key distinctions further explain the gap between CDPs and MDMs:
1. CDPs apply customer data to marketing use cases but can be limited by poor data quality; Modern MDMs focus on improving customer data quality. Traditional CDPs match and aggregate customer and interaction data using easily available fields such as email addresses, phone numbers, and IP addresses. CDP matching techniques often involve single-data point matching, such as tying a web search session to the customer transaction history using the email address that is used to log in. Because of the matching process’s simplicity, CDP’s core function is impacted by data that is only partially complete or if customer record resolution is complex, as in the case of customers of B2B companies with various buying business units across several geographies. Common challenges faced include different names for the same customer, address typos, out-of-date contact info, etc.
Customer data can only be completely leveraged when customers can be consistently identified, with interactions tied to the correct customer and redundant/erroneous duplicates resolved. Most CDPs have some form of customer entity resolution but generally lack the sophistication to properly clean customer data.
Modern MDM solutions navigate this challenge by using accurate machine learning-driven entity resolution, combined with the ability to publish clean data to a wide range of analytical and operational destination applications. The resulting clean identifiable customer data can be further enriched by connecting to external third-party sources, a futile effort with poor data quality. For a decision-maker who is mulling over the two software, the quality of their organization’s data and the difference in how the two tools tackle data quality issues will be key factors to bear in mind.
2. CDPs focus on improving the marketing function, whereas MDMs address cross-organizational customer data quality. CDPs have evolved to become laser-focused on enhancing marketing efforts and improving customer interactions. The targeted focus has pushed CDPs to develop functionality that has driven the software’s popularity with marketing teams. CDPs today can integrate data from a myriad of sources, including web/mobile interactions and data warehouses, and enable marketing outreach through several channels. For example, data on mobile app interactions can feed analytics platforms to understand what notifications and promotions increase a customer’s likelihood of transacting. The solution often sits within the marketing function, sometimes creating a new ‘silo’ of aggregated customer data.
MDMs do not solely focus on enabling marketing but focus more broadly on creating a consistent data layer that is leveraged across teams. With MDMs tools, organizations can move away from data silos and address cross-functional business challenges with a high-quality data arsenal. The end result of an MDM solution is the ability for sales, marketing, growth, finance, and several other groups to leverage customer data by connecting the data to other applications. Given the breadth of use cases, MDMs are less prescriptive on how customer data should be used by marketing – MDMs ensure that the data is a clean, unified and enriched format to flexibly feed marketing tools and decision making. The degree to which the tool is purpose-built, its scope, and the organization’s immediate priorities will determine which tool best fits the bill.
3. While CDPs focus on customer data, MDMs can tackle several data entities. Modern MDMs offer a holistic tool for the data layer across the organization. Customer data often serves as the foundation for mastering other data such as parts, products, and suppliers, delivering a more complete picture of supply to align with demand. For instance, combining mastered customer and product data can improve visibility into product assets and streamline go-to-market activities. CDPs results, though robust, are typically most relevant only to the marketing function that assesses and improves demand. Addressing the entire organization’s customer data use cases with CDPs alone may prove to be insufficient. CDOs need to plan for a more robust end-to-end data strategy that looks to create an agile and usable data layer across the organization. MDM may offer a helping hand here.
Which technology is right for your organization
MDMs and CDPs are materially different in what they excel at, the outcomes they achieve, and the part of the organization that they impact.
If you are looking for a point solution that will sync data across sources to feed marketing, and the data is largely clean, a CDP might be a strong fit. However, if customer data quality is a challenge, or if the customer data needs to feed multiple use cases, a best-of-breed solution like MDM is what you need.Tamr’s cloud-native MDM is used by the Fortune 500 to accelerate business outcomes by providing consolidated, cleansed, and categorized data for the entire organization.
If you’re interested in seeing Tamr’s modern MDM solution, join us at one of our monthly live demo days.