20 Best Master Data Management Tools Of 2025
10 Best Master Data Management Tools Shortlist
Here's my pick of the 10 best software from the 20 tools reviewed.
Our one-on-one guidance will help you find the perfect fit.
Data inconsistencies can be detrimental to organizations. Bad data can easily lead to inaccurate analytics, which then negatively affects decision-making.
Fortunately, there are master data management tools that can help companies centralize and manage complex bodies of key information. I’ve put together a curated list of the top MDM platforms that can improve how organizations synchronize data across all data sources.
What Are Master Data Management Tools?
Master data management tools are platforms designed to ensure proper synchronization of master data throughout an organization. Master data refers to the core information shared throughout the company. MDM can technologies provide a single source of truth for data enterprise-wide, and can use automation capabilities to manage and share data across an array of channels.
An MDM tool can also streamline data processes within IT departments. Businesses can even use these solutions to guarantee the accuracy of business intelligence and analytics.
Master data management technologies fall into two categories: single domain and multiple domains. A single-domain MDM specializes in managing master data within one specific domain (such as customers, products, or accounts). On the contrary, a multidomain MDM is a solution for governing all master data across all domains in an organization.
Best Master Data Management Tools Summary
| Tool | Best For | Trial Info | Price | ||
|---|---|---|---|---|---|
| 1 |  OneTrust OneTrust interface shows how users can configure privacy requests and notices according to their needs. | Best for remediation integrations | 14-day free trial | Pricing upon request. | Website |
| 2 |  Oracle Enterprise Data Management The interface of Oracle Enterprise Data Management shows request authoring experience. | Best for large enterprises | Demo upon request. | Pricing upon request. | Website |
| 3 |  TIBCO EBX TIBCO EBX has a data model assistant (DMA) that facilitates the creation and management of data models. | Best for customizations | Not available | Pricing upon request. | Website |
| 4 |  Pimcore Open Source Master Data Management Pimcore Open Source Master Data Management lets you easily define and improve organizational workflows. | Best workflow management | Demo upon request. | Pricing upon request. | Website |
| 5 |  Syniti Master Data Management Syniti’s Data Intelligence Dashboard lets users quickly identify insights and create a variety of reports for their organization. | Best for tracking KPI improvements | Not available | Pricing upon request. | Website |
| 6 |  Ataccama ONE Ataccama ONE lets you view complete and accurate information and directly edit consolidated data as you see fit. | Best for data quality | Not available | Pricing upon request. | Website |
| 7 |  IBM InfoSphere Master Data Management IBM InfoSphere Master Data Management’s interface shows the algorithm setup for a person's legal name. | Best for data profiling | Not available | Pricing upon request. | Website |
| 8 |  SAP Master Data Governance SAP Master Data Governance lets you access, analyze, and develop marketing data using an intuitive user interface. | Best master data domain support | Not available | Pricing upon request. | Website |
| 9 |  EnterWorks EnterWorks can display a library of different products and a product hierarchy that manages syndication. | Best for obtaining product intelligence | No free trial | Pricing upon request. | Website |
| 10 |  Profisee Profisee’s user interface shows golden records with alerts highlighting data quality issues. | Best user-friendly interface | Not available | Pricing upon request. | Website |
Best Master Data Management Tools Reviews
Below is a compilation of the top master data management tools that I’ve found to be the best in certain categories. Check out the list for more information.
OneTrust is a master data management tool providing enterprises with data governance capabilities to protect their privacy and integrity. Users can help their organizations minimize data risk by having complete control and visibility of their master data.
Why I picked OneTrust: I decided on OneTrust because the platform lets you take advantage of integrations to remedy issues with master data. These connectors are particularly beneficial for enterprises that want to ensure their data governance program is on point.
OneTrust Standout Features and Integrations:
Features of OneTrust that I thought were the most useful include its data governance modules on data discovery and data risk mitigation. I found its data discovery module to provide in-depth visibility to organizational data. Meanwhile, its data risk mitigation module improves security by automatically highlighting at-risk data and remediating these issues.
Integrations for OneTrust include Amazon S3, Apache Hive, IBM DB2, Microsoft SQL Server, and PostgreSQL. All these connectors are pre-built in the MDM tool.
Pros and cons
Pros:
- Review trends to assess data governance strategy effectiveness
- Automatically identify and remediate company data risks
- Discover and classify master data by using advanced AI
Cons:
- Slow data discovery speed
- Can be hard to integrate with other tools
Oracle Enterprise Data Management is a robust MDM tool that empowers users to use accurate master data so they can adapt and respond to change efficiently. You can effectively manage your enterprise data — whether you need to reconcile company metadata, make cloud transfers, or manage mergers.
Why I picked Oracle Enterprise Data Management: I decided on Oracle Enterprise Data Management because its features are designed to support large-scale MDM processes. The tool specializes in connecting disparate applications by providing simple wizards to streamline the complex challenges enterprises often face.
Oracle Enterprise Data Management Standout Features and Integrations:
Features of Oracle Enterprise Data Management that stood out to me were its ability to quickly validate master data and real-time co-authoring. Being able to confirm data nodes and hierarchies immediately can help with decision-making, while its co-authoring feature is beneficial in allowing departments or business units to collaborate effectively.
Integrations for Oracle Enterprise Data Management include DB2, Informix, Oracle, and SQL Server. All these connectors come pre-built with the platform.
Pros and cons
Pros:
- Improve data quality by validating viewpoints
- Monitor ongoing concerns using conversation threads
- Easily connect business apps using dedicated and universal adapters
Cons:
- User interface can be difficult to understand
- Requires in-depth training to use effectively
TIBCO EBX is a data management system that allows organizations to share their master data via a single platform. It follows a unique design approach that combines a range of features for managing and customizing datasets according to your needs.
Why I picked TIBCO EBX: I chose TIBCO EBX because of its flexibility. I found that the customizable applications of the platform enabled rapid deployment when needed. It also allows users to model various domains and relationships, such as with references and metadata.
TIBCO EBX Standout Features and Integrations:
Features of TIBCO EBX that stood out to me were its data modeling and data governance. The data modeling feature lets you model data using any specific rules to meet your requirements. I also found its data governance capabilities effective for centralized control of common artifacts and governance processes.
Integrations are available with popular business platforms such as Microsoft Dynamics 365, Salesforce, SAP, and Amazon RDS. I also learned that TIBCO EBX integrates readily with more than 30 RDBMS, NoSQL, and data warehouses. These integrations are all pre-built into the platform.
Pros and cons
Pros:
- Provides built-in data importation features for XML and CSV formats
- Users can integrate the graphical interface into almost any application
- Customizable data and workflow modeling
Cons:
- No free trial
- Interface may not provide the best user experience
Pimcore Open Source Master Data Management provides users with a robust workflow management system designed to simplify the handling of data quality issues. It also allows users to model complex hierarchies to readily classify data and use it in business intelligence.
Why I picked Pimcore Open Source Master Data Management: I decided to go with Pimcore Open Source Master Data Management because of its ability to improve an organization’s workflow management. I discovered that its features all work together to efficiently categorize, isolate, and resolve master data issues to enhance company processes.
Pimore Open Source Master Data Management Standout Features and Integrations:
Features of Pimcore Open Source Master Data Management that caught my eye includedits powerful workflow management engine. This features a workflow designer for business processes with custom states and actions, as well as messaging and notifications. These capabilities make it easier for stakeholders to understand and adapt to changing business processes.
Integrations for Pimcore Open Source Master Data Management include MySQL, Oracle, DB2, and Microsoft SQL. All of these components are pre-built into the platform.
Pros and cons
Pros:
- Access to historical views for improved data governance
- Use various dataset formats to match industry standards
- Easily define cleansing rules for creating golden records
Cons:
- Documentation for developers needs improvement
- System installation can be complex and tedious
Syniti Master Data Management is another MDM tool that connects organizations to master data that have a financial impact. Businesses that use the platform can expect to improve outcomes through faster master data management processes and effective user collaboration tools.
Why I picked Syniti Master Data Management: I decided on Syniti Master Data Management because of its ability to track KPI improvements. I learned that its enhanced end-to-end MDM process and AI/ML-driven data matching capabilities can assist teams by significantly reducing the data quality errors they encounter.
Syniti Master Data Management Standout Features and Integrations:
Features of Syniti Master Data Management that captured my attention were its ability to link clean master data to a company’s KPI improvements and help them provide value faster. The platform readily connects data improvements for quicker MDM returns while helping users immediately identify financial upsides.
Integrations for Syniti Master Data Management include ODBC, ADO.NET, and JDBC. These connectors are all pre-built into the platform.
Pros and cons
Pros:
- Install Knowledge Packs to improve your MDM process
- Scalable with any business process, system, or data type
- Acquire clean master data and track KPI improvements
Cons:
- Can be difficult for beginners to learn
- Information from logs could provide more details
Ataccama ONE is a master data management platform that lets you flexibly consolidate company master data from various sources. Its user-friendly UI and built-in artificial intelligence ensure quality data.
Why I picked Ataccama ONE: I decided on Ataccama ONE because of its data quality capabilities. I found that this MDM platform’s data standardization is quite robust, while its AI helps organizations maintain master data accuracy by continuously improving its matching rules.
Ataccama ONE Standout Features and Integrations:
Features of Ataccama ONE include an integrated data catalog and data quality engine. I like that the catalog lets you collaborate with colleagues to improve data assets, while the built-in data quality engine is beneficial in cleansing and enriching master data.
Integrations for Ataccama ONE are available with file systems such as S3, ADLS, and HDFS and standard file formats like Excel, JSON, and XML. All these connectors are pre-built in the platform.
Pros and cons
Pros:
- Leverages AI to automate processes and save time
- Provides users with automatic data classifications
- Integrates with a wide range of data tools and sources
Cons:
- Online documentation needs improvement
- Results visualization feature can be more dynamic
IBM InfoSphere Master Data Management is an MDM tool that lets you proactively manage and monitor your master data. It’s available in 3 deployment models: on IBM Cloud Pak for Data, on IBM Cloud, and on-premises.
Why I picked IBM InfoSphere Master Data Management: I decided on IBM InfoSphere because it allows users to create an accurate, virtual profile of their master data by leveraging its built-in algorithms. It also lets you acquire and showcase data in visual formats, making it easier for business users to analyze.
IBM InfoSphere Master Data Management Standout Features and Integrations:
Features of IBM InfoSphere Master Data Management that stood out to me were its intuitive user interface and policy management. Its functional dashboard offers a summarized view of organizational data that’s beneficial for profiling. Meanwhile, users can readily address policy violations using its policy management capabilities.
Integrations are available for Amazon S3, BigQuery, IBM Db2, Microsoft SQL, Oracle, and more. These connectors are all pre-built into the platform.
Pros and cons
Pros:
- Excellent data encryption capabilities
- Supports a range of third-party integrations
- Proactively monitor data quality using various configurations
Cons:
- Logging system can be improved
- Managing metadata can be difficult
SAP Master Data Governance is an MDM platform that specializes in obtaining master data that users can view, manage, and share in a unified solution. Its data management capabilities are based on its proprietary SAP Business Technology Platform.
Why I picked SAP Master Data Governance: I decided on SAP Master Data Governance because it has extensive support for a wide variety master domains. I also like how it provides access to pre-built data models and workflows for quick deployment and knowledge sharing.
SAP Master Data Governance Standout Features and Integrations:
Features of SAP Master Data Governance that caught my eye were its ability to enhance the development of data quality rules and synchronization of master data. I found that you can use its machine learning feature to improve your data quality rules while you can easily sync your master data across solutions.
Integrations are available with other SAP applications such as SAP S/4HANA, SAP S/4HANA Cloud, SAP Business One, and SAP ERP. These connectors are all native to the tool. You can also integrate the platform with third-party data sources using the SAP Integration Suite.
Pros and cons
Pros:
- Task automation capabilities for managing master data
- Machine learning features to enhance the development of data-quality rules
- Merge master data from SAP software and third-party sources
Cons:
- Platform lacks mobility support
- Lacks online documentation for certain features
EnterWorks is an MDM tool that enables enterprises to connect their databases with their applications to create golden records. The platform boasts a low-code design with the ability to improve revenue growth through accurate product intelligence.
Why I picked EnterWorks: I chose EnterWorks because its features are geared towards promoting revenue growth for businesses. I found that it lets you obtain product intelligence based on various domains such as customer, sales, and location.
EnterWorks Standout Features and Integrations:
Features of EnterWorks that caught my eye were its Product Information Management (PIM) and Digital Asset Management (DAM) modules. These capabilities make the platform an ideal hub for analyzing product intelligence and creating customer-centric products.
Integrations for EnterWorks include Oracle, SQL Server, DB2, MySQL, and PostgreSQL. These pre-built connectors are readily available on the platform.
Pros and cons
Pros:
- Access to in-depth product information
- Low-code design allows users to adapt quickly
- Easy access to tools and data that drive revenue
Cons:
- No free trial
- Can throw up errors in some jobs
Profisee provides organizations with an MDM tool for improving data quality through an intuitive user interface. Its UI employs machine learning, assisting users with detecting issues and cleansing master data from a single source.
Why I picked Profisee: I chose Profisee because it has the most user-friendly interface out of all the MDM tools I’ve analyzed. Its visual relationship management feature helps users to gain deep insights that help them make informed business decisions.
Profisee Standout Features and Integrations:
Features of Profisee that I found most interesting were its data visualization and modern UI. Both capabilities allow the platform to provide a singular view of data from different sources so users can address discrepancies immediately.
Integrations for Profisee include Microsoft Azure, AWS, and Google Cloud. All these integrations come pre-built into the tool.
Pros and cons
Pros:
- Platform can scale depending on need
- User-friendly interface allows for fast deployment
- Quickly identify and cleanse discrepancies in master data
Cons:
- Customer support needs improvement
- Performance for data insertion tasks could be better
Other Master Data Management Tool Options
Apart from the platforms above, I’ve selected a few more master data management tools that are worth considering for other select purposes:
- Semarchy xDM
For measuring ROI
- Informatica Multidomain MDM
Multidomain features
- SAS MDM
For historical views of master records
- Magnitude
For enterprise insights
- Riversand Platform
For creating unified customer experiences
- Boomi
Data modeling speed
- Tamr Mastering
Machine learning MDM algorithms
- Reltio
Out-of-the-box data models
- Unidata Master Data Management
For maintaining statistics
- Teradata MDM
User interface for data entry
Selection Criteria For Master Data Management Tools
Here’s are the primary selection criteria I used to develop my list of the best master data management platforms for this article:
Core Functionality
The core functionalities of a master data management tool were among the first things I considered. Before I added each tool to my list, I made sure it could:
- Identify and cleanse data issues
- Provide extensive data modeling capabilities
- Employ AI and automation features
- Perform in-depth data governance
- Have the capacity to connect with various data sources
Key Features
Top-notch master data management tools can deliver the essential functions I mentioned above through a specific range of features. The following are crucial characteristics that an organization’s tech leadership should know to identify reliable MDM platforms:
- Master data domain support
- In-depth data profiling
- Centralized data governance
- Intuitive user interface
- Workflow management
- Virtual golden profiles
- Visual business analytics
Usability
Apart from the core functionality and features, I also assessed MDM platforms that provide users with an ideal user experience. The tools that were easy to use had the following attributes:
- Supports data imports from various systems
- Compatible with standard data formats
- Provides proper modeling capabilities
- Intuitive presentation of business analytics data and visualizations
Most Common Questions Regarding Data Management Tools (FAQ's)
Here are the answers to commonly asked questions about MDM systems:
What are the main types of master data?
The main types of master data are customer, product, and financial master data. Customer master data primarily covers individuals’ information like names and addresses, while product master data normally includes product descriptions and codes. Lastly, financial master data encompasses all essential financial information of an organization, such as general ledger accounts and profit centers.
What is the difference between an MDM tool and an ETL tool?
An MDM tool aims to manage master data within a company, while an ETL tool is mainly for extracting, transforming, and loading data across different systems. Master data management platforms focus on helping users manage data quality. On the other hand, ETL systems facilitate data integration.
The most common features provided by MDM platforms include data cleansing, consolidation, and synchronization. Meanwhile, ETL tools mainly provide the ability to extract data from sources such as Software as a Service (SaaS), APIs, and databases.
Do MDM tools use AI?
Yes, MDM tools can use AI to enhance data quality. Many vendors develop their master data management platforms to use machine learning. They do so to improve various MDM aspects, such as data modeling, cleansing, and governance.
These machine-learning models can detect discrepancies in master data and automatically flag them for remediation.
Final Thoughts
The global market size for master data management is predicted to reach $39.4 billion by the year 2028; this number is a huge leap from its previous market size of $12.3 billion in 2018.
Yet despite the expected growth, bad data continues to plague many enterprises, with organizations spending an average of $12.9 million every year managing poor-quality business data.
One approach to resolving this dilemma is by leveraging the right MDM tool. Your chosen master data management platform must have capabilities that align with your needs and objectives.
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