Customer Master Data Management (CMDM) is a critical function for enterprises that want to maintain an accurate, unified, and structured record of their customers. It serves as the backbone for multiple business functions, including sales, marketing, finance, and customer support. Poorly managed customer data can lead to inefficiencies, lost revenue, compliance risks, and poor customer experiences.
Customer master data solutions are designed to address these challenges by providing centralized platforms and intelligent tools that ensure consistency, eliminate duplicates, and enable real-time data synchronization across systems. These solutions help organizations establish a single source of truth for customer information, improving decision-making and enhancing customer engagement across all touchpoints.
With digital transformation, businesses must shift towards a more structured, real-time, and accessible customer master data system. A well-maintained customer master enables accurate reporting, streamlined operations, personalized customer experiences, and improved regulatory compliance.
Customer Master Data Model
A Customer Master Data Model is a structured framework that stores key information about a customer. It is designed to provide a single, unified, and accurate view of the customer across various departments and applications.
Key Attributes of a Customer Master Data Model
Basic Customer Information:
Customer ID – A unique ID assigned to a unique customer entry
Name (First, Middle, Last / Company Name) – First name and last name
Company Name – Only applicable for B2B customers
Customer Type (Individual, Business, Government, Non-Profit)
Contact Information (Phone, Email, Website)
Billing and Shipping Addresses
Financial & Credit Data
Credit Limit
Payment Terms (Net 30, Net 60, etc.)
Tax Identification Number (TIN)
Bank Details
Payment Methods (ACH, Wire, Credit Card, etc.)
Legal & Compliance Information
Business Registration Number
Industry Classification Codes (NAICS, SIC)
GDPR/CCPA Compliance Data
Risk Rating
Customer Interaction and History
Purchase History
Support Tickets and Complaints
Marketing Preferences
Customer Engagement Score
Segmentation and Preferences
Customer Loyalty Tier
Preferred Communication Channel
Special Discounts or Pricing Agreements
Customer Master Data Model in SAP
SAP treats Customer Master as a separate configurable module. It stores customer-related information systematically and allows easy retrieval and maintenance. The data in SAP Customer Master is categorized into different views to facilitate business functions. Some of the default fields available in SAP Customer Master include:
General Information
This includes essential customer details that are shared across multiple company codes and sales areas.
Name (Individual or Company)
Address (Street, City, Postal Code, Country)
Contact Details (Phone, Fax, Email, Website)
Industry Classification (NAICS, SIC)
Search Term (Short description for easy lookup)
Data for Tax Reporting (VAT, GST, Exemption Certificates)
Sales Area Data
This section contains details required for sales transactions and order processing. It is specific to a Sales Organization, Distribution Channel, and Division.
Sales Organization (Defines sales reporting structure)
Distribution Channel (Direct Sales, Reseller, Online, etc.)
Division (Product Grouping)
Pricing Group (Discounts, Special Pricing Agreements)
Shipping Conditions (Preferred Delivery Mode)
Customer Group (Loyalty Tiers, Business Type)
Output Determination (Invoice and Document Preferences)
Company Code Data
This includes financial and accounting details related to the customer.
Reconciliation Account (Links customer to general ledger accounts)
Payment Terms (Net 30, Net 60, Cash in Advance, etc.)
Dunning Procedures (Automated Reminder & Collection Policies)
Credit Control Area (Limits and Creditworthiness Assessment)
Tax Jurisdiction Codes (Based on Customer Location)
Payment Method (ACH, Wire Transfer, Credit Card, etc.)
SAP website has several resources and guides to understand how a Customer Master is structured and guides to customize it in the new S4/Hana
Customer Master Data Model in Other ERPs
Here are some resources to learn more about how Client Master data is managed and configured in other ERP systems.
Oracle ERP: Uses Customer Data Management (CDM) with hierarchical structures to manage relationships. [Developer Guide]
Microsoft Dynamics 365: Uses Customer Entities for 360-degree customer data insights. [Developer Guide]
Salesforce: Has Account and Contact objects for managing customer details. [Developer Guide]
How do Teams Benefit from a Unified Customer Master?
Marketing: Personalized promotions based on customer segmentation.
Example: A retail company analyzes purchase history and customer preferences to offer personalized discount coupons via email and mobile notifications.
Use-Case: A fashion e-commerce brand segments customers based on past purchases and recommends products aligned with their preferences.
Finance: Accurate credit risk assessment and invoicing.
Example: A financial institution uses a unified customer view to assess creditworthiness by analyzing transaction history, outstanding loans, and payment behaviors.
Use-Case: A multinational corporation ensures timely invoice payments by identifying customers with delayed payment history and adjusting credit limits accordingly.
Operations: Streamlined order fulfillment and improved customer service.
Example: A logistics company integrates customer data across different platforms to ensure efficient order tracking and delivery.
Use-Case: An automotive parts supplier reduces shipping errors by verifying customer address accuracy and purchase history before dispatching orders.
Compliance: Better adherence to data protection laws.
Example: A healthcare provider ensures compliance with GDPR by maintaining a single, secured customer data repository that aligns with data privacy regulations.
Use-Case: A banking institution regularly audits customer records to ensure KYC (Know Your Customer) compliance, reducing regulatory risks.
Getting Started with a Customer Master Data Project
Typically, customers that approach us for a Customer Master Data project have a sub-optimal configuration for managing their customer data, this invariably leads to a lot of legacy issues in the database that needs to be remedied before new processes are set in place.
So a Customer Master Data Project typically follows a two-pronged approach;
Addressing Legacy Data
Assessment & Identification:
Conduct a data audit to locate duplicate, redundant, and outdated records.
Example: A telecom company identifies and merges duplicate customer records to prevent redundant billing.
Data Standardization & Normalization:
Define standard formats for names, addresses, and contact details across all systems.
Example: A multinational retailer unifies customer address formats to ensure consistent shipping data.
Data Cleansing & Validation:
Utilize tools such as Trillium, Informatica, and Talend to cleanse and validate records.
Use-Case: A financial institution leverages Informatica MDM to validate and correct customer tax IDs.
Governing Future Customer Data
Establish Data Stewardship & Ownership Models:
Assign dedicated roles for data governance and oversight.
Example: A healthcare provider assigns data stewards to manage patient record integrity.
Leverage AI-Driven Data Enrichment Tools:
Use external data sources like ZoomInfo and Dun & Bradstreet to enrich missing details.
Use-Case: A B2B manufacturer enriches incomplete customer profiles with real-time company financials.
While it is possible to execute a customer master data project in-house, it generally requires a plethora of software platforms, SaaS subscriptions, accesses to several private databases and much more.
In most cases, the know-how, technical bandwidth and availability of resources for revamping a Customer Master is limited and it’s a far better option to work with a specialist to address the nuances of the project.
In any case, here are a few master data platforms that can help with addressing Customer Master Data
Software Solutions for CMDM
MDM Suites: Informatica MDM, SAP MDG, IBM InfoSphere MDM
Example: A global logistics company integrates SAP MDG to centralize customer master data.
Data Enrichment Tools: Experian, Melissa Data
Use-Case: A credit agency uses Experian to validate and append missing credit history details.
Data Governance Platforms: Collibra, Alation
Example: A legal firm ensures compliance by managing data policies through Collibra.
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Strategies for Managing a Customer Master
In addition to the above-mentioned processes, there are a few strategies worth knowing that can easily help govern and normalize client data.
Here are a few such approaches.
Adopt a Single Source of Truth Approach
Master data systems rely on a single golden record [learn more], a final version of the truth; Since customer data resides independently on several disparate systems, a CRM, a CDP and other similar data sources, it is wiser work on a single database to avoid duplication and other issues
Implementing Role Based Access Controls
One of the leading causes of poor data quality in a Customer Master, or any multi-domain master data system for that matter, is poor data stewardship;
Natural lethargy and poor quality control when it comes to data entry and data edits are prone to occur and this is precisely organizations need to implement a role-based system assisted by technology-led processes to validate entries, alert conflicting information, enrich missing data autonomously and trigger alerts for approvals depending on the configured approval matrix
Deeply Embed AI Systems in Processes
Only perhaps a handful of industries and disciplines are as affected by the introduction of Artificial Intelligence as Data management is.
One of the biggest cost-centers and challenges in MDM implementations were directly tied to human efforts. Since it was extremely tricky for an algorithm to “understand” an MRO record and process it without human intervention.
With the rise of Artificial Intelligence, however, there are several independent use-cases for AI in MDM, namely
1. Autonomous Classification: AI systems can automatically, extract details like first name, last name, apartment number, street name and country of origin of a Customer Record from unstructured free text
2. Enrichment: Continously running AI-agents are capable to detect missing information for any given client master record and can populate missing information from first or third party data sources without any human intervention; this works particularly well when integrated with APIs directly as we’ve discussed further below.
3. Data Scoring: We’re still quite far from the day when an army of AI agents will autonomously manage your customer master, so a manual human review is something that’s pretty much indispensable at the moment.
However, AI-systems can help drive efficiency by scoring data records, making human-reviews a lot more scalable and effective
Integrate APIs with First & Third Party Databases
A customer data is large and complex with several fields spanning personal information, banking details, legal records and even firmographic details for b2b customers.
It’s very common for these records to not contain “full Information” and software-led systems that automatically leverage APIs provided by third party sources for a variety of information; Some of which include;
1. Contact Databases: Databases like ZoomInfo, D&B store individual user data replete with email addresses, phone numbers etc
2.Tax & Financial Information: D&B stores transactional and financial information like TIN number
3. Credit Information: Equifax, Transunion and other bureaus also provide credit reports and other related information that can be integrated into the customer master via APIs
In large enterprises, first party data sources like the CRM, the CDP are storehouses of information and can be excellent sources of information that can be relayed back to the CMD system.
Customer Master Data VS Customer Relationship Management
While both Customer Master Data Systems and CRM systems manage customer information, they serve different purposes and offer distinct functionalities within an organization and are used for different use-cases by different teams.
Here are a quick excerpt detailing the difference between the two.
| Feature | Customer Master Data System | Customer Relationship Management System |
|---|---|---|
| Purpose | Provides a centralized, authoritative repository of customer data, ensuring consistency and accuracy across the organization. | Manages interactions with current and potential customers, focusing on sales, marketing, and customer service activities. |
| Data Type | Stores core customer attributes such as identification numbers, legal names, contact information, and financial data. | Captures dynamic data including customer interactions, purchase history, service requests, and marketing campaign responses. |
| Users | Utilized by data governance teams, IT departments, and systems requiring validated customer data. | Primarily used by sales, marketing, and customer service teams to manage day-to-day customer interactions. |
| Integration | Acts as a data source for CRM systems, ensuring that all customer-related applications access consistent and accurate information. | Consumes data from the Customer Master Data System to provide users with up-to-date customer information during interactions. |
Customer Master Data VS Customer Data Platform
Customer Data Platform is quite a novel concept and it emerged around 2013 when the term was first coined by David Raab, a marketing technology consultant and founder of the CDP Institute.
CDPs were introduced to address the growing need for unified, persistent, and accessible customer data across multiple marketing, sales, and customer service channels. The goal was to eliminate data silos and create a single, comprehensive view of customers that businesses could use to improve personalization, targeting, and customer engagement.
So it’s easy to see the confusion on account of overlap in objectives around Customer Master Data & a Customer Data Platform. The table below details some of the differences between them
| Category | Customer Data Platform (CDP) | Customer Master Database (CMD) |
|---|---|---|
| Primary Purpose | Enhances marketing and customer engagement through real-time data collection and segmentation. | Ensures data governance, accuracy, and consistency by maintaining a single source of truth. |
| Focus | Customer experience & personalization | Data integrity & governance |
| Data Sources | Marketing platforms (CRM, social media, website interactions, email campaigns, ad platforms, customer service logs). | Enterprise-wide sources (ERP, CRM, billing, customer support, external databases). |
| Data Processing | Aggregates, unifies, and segments customer data for real-time use in marketing and engagement. | Cleans, standardizes, de-duplicates, and manages customer data to ensure accuracy. |
| Use Cases |
– Personalized customer experiences – Marketing automation & targeting – Customer journey tracking – Omnichannel campaigns |
– Data accuracy and consistency – Regulatory compliance (GDPR, CCPA) – Unified customer records across ERP, CRM, finance systems – Ensuring trusted data for enterprise use |
| Real-Time vs. Static | Works in real-time, capturing dynamic customer interactions for personalization and analytics. | Provides a static but authoritative customer profile, ensuring accuracy for transactional and operational systems. |
| Ownership & Users | Marketing & Customer Experience teams use it for personalized engagement and analytics. | IT, Data Governance, and Compliance teams manage it for enterprise-wide data reliability. |
| Integration Scope | Integrates with marketing automation tools, analytics platforms, and digital advertising systems. | Integrates with ERP, CRM, financial, supply chain, and operational systems to ensure data accuracy. |
