Master data management uses a combination of tools and business processes to ensure the organization’s master data is complete, accurate, and consistent. Master data describes all the “relatively stable” data that is critical for operating the business. This includes semi-permanent information about products, locations, employees, customers, etc. For example, if a specific customer purchases several products at different times, an invoice must be created for each purchase, but the customer’s address remains the same for several years.
The management of master data ensures it is a consistent and reliable source of accurate information. Once created, this master data acts as a trusted resource for organizations. Master data management establishes a single master data file for each customer, as well as products, business accounts, etc. – these categories are referred to as data domains.
Master data management (MDM) normally includes a variety of data domains. Listed below are examples of the different types of data domains that are often managed with a modern master data management platform, with examples of each.
- Customer data: Demographics, preferences, contracts, and purchase history
- Product data: Pricing, attributes, descriptions, and specifications
- Supplier data: Vendors and suppliers contact information and contract terms
- Employee data: Job titles, performance, and salary or hourly pay
- Location data: Geographic locations, such as boundary data and addresses
- Asset data: Physical assets – real estate, equipment, and vehicles
- Financial data: Account balances, invoices, financial transactions, and payments
- Reference data: Codes, regulations, and standardized reference information
The practice of master data management shares the goals (and many of the processes) of Data Governance in creating a trusted view of the business’s master data.
Master Data Management Tools and Technology
The use of technology in managing master data includes software solutions that automate how the organization’s critical data is managed and shared. Master data management platforms are normally used to provide the tools supporting data quality, integration, reconciliation, and enrichment when creating master records.
Recently, artificial intelligence has started being used to identify, coordinate, and merge data from across an organization. The data is then cleaned (becoming high-quality data) and shared with the systems and applications needing it.
The merging process allows the records to be examined by the AI for inconsistencies. The metadata attached to the files should show the data’s source and provide a trail of its use to find out where the errors occurred and correct the problem. A policy of transparency helps to show how each master record has been created or modified.
Master data management tools can be used to track master data and offer insights about the organization’s operations and processes. A master data management platform can be used to gather information from multiple departments and domains, which allows management to determine areas of concern, increase productivity, and improve the business’s return on investments.
Master Data Management Platforms
Selecting a master data management platform, with tools that fit the needs of the organization, can be complicated. The various versions should be researched and compared to find the most appropriate solution. (Warning: a master data management platform can turn out to be a single-domain, focused on product or customer data.)
Master data management platforms support the tools needed to manage master data.
Listed below is a selection of master data management platforms, but there are many others available that may offer a superior solution for your specific needs, so a little additional research may be worthwhile. For example, the SAP Master Data Governance platform offers both Data Governance and master data management, which might be a great fit for your organization’s needs.
- The Pimcore platform is open-source and consolidates master data across a variety of differing systems. The platform works with any of the master data domains (product, customer, IoT, etc.) and supports workflow management, data quality, hierarchy management, and rich content integration. It requires MySQL as a foundation, which is also open-source.
- Profisee is a master data management platform that supports data stewardship, visual relationship management, data quality rules, and workflow management. It can be accessed through the cloud, and can also be downloaded to computers.
- The IBM InfoSphere Master Data Management platform manages all aspects of an organization’s master data and provides users with a unified view. This platform supports compliance with the Data Governance program’s rules and policies and is available in two versions (the standard and the advanced). Both versions are available on-premises or in the cloud.
- Stibo Systems offers a master Data Management platform that allows its users to connect data based on the business’s specific needs. Their platform consolidates the domains of master data into a single source. It is described as easy to implement and can be deployed in the cloud or on-premises.
- The OpenDQ master Data Management platform is designed for small to medium-sized businesses. It is scalable and will expand to fit the business’s Data Management needs. OpenDQ seems to be well designed, but the phrase “zero license cost” may create confusion. This platform is not free, and is a paid-for service.
Minimizing Installation Stress for the MDM Platform
Implementing a master data management platform doesn’t have to be difficult. The implementation process can be divided into distinct phases. These phases include important steps, such as researching appropriate software, creating a model or template, testing the arrangement, and deploying the software.
This series of steps helps to ensure the smooth and effective implementation of an MDM system that aligns with the organization’s specific needs and objectives.
1. Selecting the platform: Choosing a platform that is compatible with the business’s system promotes the easy implementation of a master data management platform. For example, if the organization uses Microsoft software as the foundation for their e-business, the IBM InfoSphere MDM platform should minimize frustration during the installation process and reduce the number of errors, crashes, and bugs that might develop.
If, however, the business uses a mixed variety of software, other options are also available. The Pimcore open-source platform is flexible and can be fine-tuned to work with the unique qualities of your organization’s data processing systems. Cloud-based master data management platforms are designed to integrate with a variety of software systems, are often used as an easy solution, and provide access to a large number of MDM tools.
2. Designing the master data model: This step involves actually designing and developing a model of the MDM program (template to work from). Sagar Sharma, co-founder and CTO of Credencys Solutions, has written a very thorough series of steps for developing a master data model.
3. Testing the model: Before deploying the chosen master data management platform, it is important to perform rigorous testing to make sure it functions as expected. Data quality and business rules must be checked to ensure they will function properly. The testing phase should be coordinated with the platform vendor (unless it’s open-source). The process may involve running several test scenarios that are based on real data samples. Any issues or bugs that are discovered during this phase must be corrected to ensure the system is reliable.
4. Deployment: Once the testing phase has been completed and the master data management platform has been shown to perform well, the next step is deploying the platform. The deployment process should be organized to minimize any disruptions to the flow of business. Training may be necessary for the data steward and end users.
5. Maintenance and improvement: The last step in implementing an MDM platform focuses on long-term maintenance and improvement. As the business evolves and the customer base shifts, it becomes important that the master data management solution also adapts and evolves. Regular monitoring will help in identifying any areas needing improvement, and regular maintenance will help to ensure the system functions optimally. This may involve updating the data models, refining the organization’s business rules, and integrating any new data sources.
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