What is the common data model and why it matters

Introduction: The Data Fragmentation Challenge

If you’ve ever worked in a business where information is scattered between different teams and tools, you know how tough it can be to get everyone on the same page. These days, organizations handle data from so many applications, departments, and external partners. It’s pretty common for information to get trapped in silos—maybe in separate databases, spreadsheets, or even cloud apps—making it really difficult to see the big picture. This kind of fragmentation can slow down your team, create more manual work, and lead to mistakes or outdated data. As companies in the U.S. and beyond look for ways to modernize and automate, it’s clear that having a standard way to manage, share, and analyze data isn’t just a nice-to-have—it’s essential.

It’s worth considering that these issues are even more common in businesses that have grown through mergers or acquisitions, or those that have been around long enough to accumulate a mix of legacy systems and new cloud solutions. Imagine a company with customer info in a CRM, sales data tucked away in an ERP, and marketing results living in a separate platform. Bringing all that data together for reporting or analytics often means lots of manual work, custom-built connectors, or complicated ETL (Extract, Transform, Load) processes. Not only does this slow down decision-making, but it also raises the risk of errors and compliance headaches—especially if you’re operating in regulated industries like healthcare, finance, or government.

What is the Common Data Model: Core Definition

The Common Data Model (CDM) is Microsoft Corporation’s answer to this challenge. CDM is a standardized and flexible framework created to tackle the headaches of fragmented data in business environments. What it does is pretty straightforward: it gives you a collection of clearly defined data entities, attributes, and relationships—think of things like accounts, customers, and products. With these definitions in place, CDM creates a shared language for your data, so it’s much easier to move, integrate, and analyze information across different systems and platforms.

Microsoft built CDM as a key part of its broader data strategy, and it’s also foundational to the Open Data Initiative—a partnership with Adobe and SAP that’s all about improving data interoperability between enterprise applications. Even though CDM is rooted in Microsoft’s ecosystem, it has caught on as a sort of standard throughout the data world, thanks to its open approach and community-driven improvements. By laying out a universal schema for business data, CDM helps companies tear down the walls between their systems, making it possible to exchange information smoothly, whether that’s internally or with outside partners. For example, if a software company builds an app using CDM, it’s much easier to plug into other CDM-compliant solutions, which can save a lot of time and money during deployment.

How Common Data Model Works: Technical Foundation

Schema structure and entity definitions

At the heart of CDM are standardized schemas. These are blueprints that define entities like Account or Customer, along with their attributes and how they connect to other entities. The model comes with hundreds of pre-built entities for all sorts of business domains, but here’s the key takeaway: you’re not stuck with what’s provided. CDM is designed to be extensible, so your organization can tweak or add to the model as needed. Each entity definition spells out exactly what kind of data should be stored, helping you achieve consistency no matter what systems you’re using.

For instance, the Account entity might include fields like Account Name, Account Number, Address, and Industry. The Customer entity could have Contact Information, Purchase History, and Loyalty Status. Having this level of detail means that, no matter where your data starts, you can map it to a consistent structure—which makes integration and reporting a whole lot easier.

JSON-based metadata system

A big part of what makes CDM powerful is its metadata system, which relies on the JSON schema format. This is what describes each entity, its properties, data types, and how it relates to others. The beauty of using JSON is that it’s both human- and machine-readable, so everyone from data engineers to business analysts can understand what’s going on. Plus, it’s easy to integrate with modern cloud services and development tools.

• Developers and data engineers can use standard tools to create, adjust, or validate data models.
• JSON-based schemas are widely supported by open-source and commercial software.
• CDM is a flexible solution for organizations with a mix of different IT setups.

Azure Data Lake Storage integration

CDM works hand-in-hand with Azure Data Lake Storage Gen2, Microsoft’s cloud-based storage service. When a company uses CDM, their data gets stored in organized folders within the data lake, right alongside those JSON-based metadata files. This setup is scalable, secure, and cost-effective, making it easy to handle large amounts of data. It also means that other Microsoft services—like Power BI or Power Apps—can tap into, process, and analyze standardized data directly from the data lake, with no extra steps.

Azure Data Lake Storage Gen2 is built to handle both structured and unstructured data, supporting everything from advanced analytics to machine learning and real-time dashboards. By saving your information in a CDM-compliant format, you make it instantly accessible to a wide range of Azure services, so you don’t have to worry about duplicating or transforming data just to get your work done.

Key Components of Common Data Model

Standardized entities and attributes

CDM’s core strength is its library of standardized entities and attributes that cover just about every business scenario you can think of. These include familiar objects like Contacts, Campaigns, Invoices, and Products. Each entity lays out the essential data points you need. This kind of standardization really cuts down on the hassle of custom integration when you’re connecting several systems.

Example use cases:

• A retail business can use the Product entity to bring together inventory data from its online store, physical point-of-sale system, and supply chain software.
• Marketing teams can evaluate campaign effectiveness across multiple channels without spending hours reconciling different datasets.

Manifest files and entity documents

CDM organizes its metadata with manifest files and entity documents.

• Manifest file: Main entry point to a group of related entities, showing which ones are present and how they’re connected.
• Entity document: Details about each entity—its attributes, data types, and any rules or constraints.

This modular setup makes it much easier to manage, grow, or share your data models across different projects or teams.

Vocabulary and semantic mapping

To make sure your data keeps its meaning as it moves from one system to another, CDM supports vocabulary and semantic mapping.

• Map source data fields to standardized CDM concepts.
• Maintain business context and data integrity.
• Reduce confusion and support compliance (e.g., GDPR, HIPAA).

For example, “Client_ID” in one system and “CustomerNumber” in another can both be mapped to “CustomerID” in CDM, ensuring consistent analytics and reporting.

Common Data Model in Microsoft Ecosystem

Power BI integration and dataflows

Power BI, Microsoft’s flagship analytics platform, uses CDM to make data prep and reporting much simpler. By building CDM-compliant dataflows, organizations can create reusable transformation logic and guarantee that all dashboards and reports are pulling from consistent, standardized data. This not only speeds up business intelligence projects but also helps everyone trust the insights they’re seeing.

Example:
A multinational company can map all incoming sales data to the CDM schema, delivering unified executive dashboards without manual reconciliation or custom code.

PowerApps and Power Platform benefits

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Microsoft Power Apps, as part of the broader Power Platform, takes full advantage of CDM to streamline business app development.

• App creators can use a library of standardized entities, saving time and reducing mistakes.
• CDM enables easy integration with Power Automate and other Microsoft services for automation and workflow solutions.

Example:
An HR team can quickly build a leave request app using Employee and Leave entities from CDM, ensuring seamless data sharing with payroll, benefits, and compliance systems.

Dynamics 365 native support

Microsoft Dynamics 365 has CDM built right in. This native support means data generated in Dynamics 365 is ready to be shared with other applications or analytics tools, creating a more agile and connected business environment.

• Immediate access to standardized data models for core business entities.
• Developers can rely on a consistent data structure for custom solutions or third-party apps.

Business Benefits and Use Cases

Improved data consistency and quality

Implementing CDM leads to:

• Standardized definitions across all systems
• Reduced duplication and conflicting data
• Easier, more informed decision-making

This is especially valuable for compliance with standards like SOX or HIPAA.

Accelerated application development

CDM provides a ready-made library of entities and attributes, allowing developers and analysts to:

• Focus on building business value
• Move faster in low-code environments like Power Apps

Example:
A manufacturing company can launch a quality control app quickly by reusing CDM entities for Products, Defects, and Inspections.

Enhanced analytics capabilities

CDM’s standardized structure enables:

• Easier aggregation of data from multiple sources
• Seamless ingestion and transformation in Power BI
• Consistent schemas for machine learning and AI projects

Example:
A global retailer can unify sales, inventory, and customer data for predictive analytics and connect it to Azure Machine Learning for advanced modeling.

Industry-specific implementations

CDM is designed to be flexible, supporting industry-specific versions and extensions:

• OHDSI Common Data Model for healthcare
• Custom extensions for finance, manufacturing, government, and education

Implementation Process and Best Practices

Planning and assessment phase

• Evaluate current data sources
• Identify key business entities
• Align stakeholders on project goals

Early involvement of both business and technical teams ensures the solution meets everyone’s needs.

Schema mapping and transformation

• Transform legacy formats and align field names to CDM standards
• Use tools like Power Query and dataflows for automation
• Validate data for accuracy and completeness

Setting up clear mapping rules and using data profiling tools helps avoid migration issues.

Governance and maintenance strategies

• Establish policies for schema versioning, data ownership, and quality assurance
• Monitor changes in data sources and update mappings as needed
• Train users on CDM concepts

Leverage tools like Azure Policy and Microsoft Purview for compliance and data cataloging.

Common Data Model vs. Other Approaches

CDM vs traditional data warehouses

CDM vs other canonical data models

• CDM offers a broad, open, and extensible framework covering multiple domains.
• Open-source schemas on GitHub encourage collaboration and transparency.
• Can be extended to include industry standards like HL7 (healthcare) or FIX (finance).

When to choose Common Data Model

• Integrate data from multiple sources
• Standardize business definitions
• Enable smooth reporting and analytics
• Invested in Microsoft technologies, or need hybrid/cross-platform support
• Planning to use low-code/no-code tools
• Preparing for AI and machine learning projects

Getting Started with Common Data Model

Prerequisites and requirements

• Access to Microsoft cloud services, especially Azure Data Lake Storage Gen2
• Familiarity with Power Platform tools
• Basic understanding of JSON schema formats
• Stakeholder alignment and clear project objectives

Assemble a cross-functional team (data architects, business analysts, IT admins) for guidance and compliance.

Step-by-step implementation guide

• Identify key business entities and map them to CDM standards
• Create or extend schemas using available tools and templates
• Migrate or transform data into CDM-compliant structures and store in Azure Data Lake
• Test and validate data for accessibility and accuracy

Rolling out CDM in phases—starting with a pilot project—helps refine processes and build expertise.

Resources and community support

• Microsoft documentation
• CDM schemas on GitHub
• Forums, user groups, and official support channels
• Industry organizations like DAMA and the Open Data Initiative
• Webinars, conferences, and online communities for ongoing learning and networking

Frequently Asked Questions

What is the main advantage of using the Common Data Model?

The main advantage is standardized data structures, which simplify integration, improve data quality, and enable seamless analytics across platforms.

Can CDM be used outside the Microsoft ecosystem?

Yes, while CDM is deeply integrated with Microsoft tools, its open schema and community support make it adaptable for hybrid and cross-platform environments.

How does CDM support regulatory compliance?

CDM’s standardized definitions, traceability, and semantic mapping help organizations maintain accurate, auditable records required by regulations like SOX, HIPAA, and GDPR.

Is CDM suitable for small businesses or only large enterprises?

CDM is scalable and can benefit organizations of any size, especially those looking to modernize, automate, or unify their data management.

Where can I find templates or examples to get started with CDM?

You can access templates, documentation, and sample data models directly from Microsoft’s official CDM documentation and the GitHub repository.