What is Multi-domain MDM?

Multi-domain MDM manages all different types of master data in one place, bringing the multiple data domains and processes associated with each into a centralized platform. MDM ensures your master data is consistent, complete, and accurate throughout your organization, enterprise systems, and partners. Multi-domain MDM takes that one step further by integrating all your domains into one single platform. A domain refers to the type of data referenced. Some of the most popular data domains are customer data, products, financials, locations, employees, and data about different industries.

Multi-domain MDM Diagram

Organizations used to only implement single-domain MDM solutions. That meant there was one system for each data domain, such as one for customer data, another for products, another for locations, and so forth. This gives you a single view of customers; however, it doesn’t offer a complete view, leading to confusion, errors, and lack of consistency in an organization’s master data. With a single domain system of customer data, you would need to integrate it with the product and services data domains. You might want to use financial or location data for a complete, holistic view of what that customer purchased, what services they used, and all the data about their behaviors and interactions with the company. A single-domain MDM approach puts up barriers to correlate information across multiple domains.

Master data management is more essential than ever. Every transaction in your organization relies on master data: products, customers, employees, suppliers, financial hierarchies, or reference data. Accurate and consistent master data streamlines your operational processes and increases the quality of your reporting and analysis. You need a system that simplifies multi-domain master data management by providing one way to manage, govern, and share all your master data. With multi-domain MDM, organizations can implement any domain needed into the organization using only one set of technologies.

Business Case for Multi-Domain MDM

IT professionals, data scientists, and others who create, manage, and interpret data for a living are increasingly aware of the need for a master data management solution to help ensure that data about key business entities and their classifications (such as customers, products, assets, locations, and employees) are accurate and consistent. Many of these teams realize that the lack of consistency across these entities leads to distortions in analytics. To ensure top-quality reporting and analysis and streamline processes across your business, you need multi-domain master data management. In fact, with a comprehensive multi-domain MDM solution, organizations can:

  • Model any master data, regardless of domain: Organizations can model any master data—including relationships between domains—without buying separate solutions. They can manage any domain of master data such as customers, products, assets, locations, and relationships in the same solution. This saves a lot of time and headaches in ensuring that everything is accurate.
  • Gain everything they need to master their data: A successful multi-domain MDM solution has all the critical capabilities for master data management including workflow, data quality, role-specific applications, stewardship, and more in one solution.
  • Meet the data management needs of its business users: With a multi-domain MDM solution, organizations can meet the master data management needs of its business users, not just its data stewards and developers. It’s important to cater to the needs of the business because mass adoption of high quality, consistent, and accurate master data is critical for program success.
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Organizational Optimization from Multi-domain MDM

Increased complexity around master data is an issue that plagues nearly all organizations. Failure to adequately address these and other issues typically results in increased error rates, diminished user and customer experiences, financial inefficiencies, and higher risks in data governance and cybersecurity. All these issues contribute to a “crisis in information trust,” which slows down information flows in the organization. This model helps to position the business value of MDM through eight different lenses by helping teams identify organizational waste that MDM can address:

  1. Transportation: Many different data integration tools are required to copy data to where it’s needed rather than pull from a centralized source.
  2. Inventory: Everyone creates their own copies of the same, shared master data, creating inconsistency in the organization.
  3. Movement: Individuals manipulating or prepping master data and reference data themselves sometimes relay the same information to multiple systems.
  4. Waiting: Waiting for “someone” to fix the data or waiting for IT to give access to data is one of the root causes of inventory problems.
  5. Over processing: Fixing the same data quality issues in local systems as opposed to addressing problems consistently; and all those checking and rechecking activities that each team performs because of the lack of faith in the accuracy, integrity, and timeliness of the data that they’re passed.
  6. Overproduction: All of the activities used to create more “inventory” or copies of master data everywhere.
  7. Defects: Operational failures such as failed business processes, analytical issues such as incorrect reports, and governance problems such as compliance failures.
  8. Skills: Users wasting their talents and time in manually cleansing, rekeying, massaging, and preparing (often the same) data set in everything from desktop tools like Excel to their own “shadow IT” applications.

By focusing on multi-domain MDM as a way to facilitate communication throughout the enterprise, organizations can identify, categorize, and eradicate waste related to master data—all while addressing important cost issues.

Key Functionalities of a Multi-Domain MDM Solution

Some of the key functionality and capabilities you should be looking for in a multi-domain MDM solution include:

  • Easily configured to meet your organization’s data management requirements, rather than forcing you to adapt your processes (or your data model) to a rigid vendor-defined data model or framework.
  • Easy to use user interfaces, encouraging the participation of business users and subject matter experts who fall along a spectrum of technical sophistication.
  • Complete set capabilities that enable you to establish a data management practice.
  • Meets your deployment requirements, like deploying on premises, in a public or private cloud, or in hybrid environments.
  • Amenable to an agile development method that supports frequent iterations to help adapt to change as business conditions require.

What to Look For in a Multi-Domain MDM Partner

After defining the solution’s functionality, find the right partner to help you build and implement the right solution. Of course, your multi-domain MDM solutions partner must have a platform with all the functionality listed above. But since MDM now is playing an increasingly pivotal role in how you derive the most value from your organization’s data, you should evaluate your potential partner on other criteria as well:

  • Demonstrated MDM expertise over time
  • Clearly articulated and logical technology vision and roadmap
  • Financial staying power
  • Global service and support
  • Comfortable in risk management scenarios
  • Experience in multiple use cases
  • Independently sourced metrics for time to value
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Multi-domain MDM Example Use Case

Let’s take a look at how a multi-domain solution can help a non-governmental organization deliver humanitarian medical care.

Background: When a non-governmental organization (NGO) prepares to deliver essential medical care at what often can be remote, high-stress locations anywhere in the world, time is of the essence. It’s a situation where delays can—and often do—cost lives. Ensuring the availability of timely, accurate, and comprehensive master data is essential.

This NGO had a number of pressing challenges to address, including:

  • Enhancing interoperability across systems for managing supply levels
  • Improving quality of codes lists used to classify medical products
  • Promoting information exchange among technicians
  • Reducing delays between the approval decision and the availability of medical codes
  • Defining common classification of data

This necessitated the design and deployment of a single, central MDM platform to improve inventory level accuracy across multiple distribution centers—consolidating multiple sources of inventory and reference data codes and standardizing common characteristics used by all distribution centers around the world to perform their own translations.

As in the NGO example, in many industries, we see unique, use case, or business context-specific defects that can be addressed with multi-domain MDM, such as:

  • Defects: Inaccurate measures of inventory levels across the three distribution centers due to inconsistency in classifications (also some misclassification).

Similarly, there are multiple wastes that contribute to the defects:

  • Inventory: Multiple sources of inventory and reference data codes.
  • Over-processing: A lack of common classifications requires all distribution centers to perform their own translations.
  • Waiting: Delays in medical code approvals for classifying inventory.

Across industries, defects are the end result of poor master data. Inaccuracy or inconsistency in master data are defects, but in most of the cases, the visible waste is seen and felt in other business processes that rely upon that data. In the first example, the service delivery problems that customers experienced were created by inconsistent customer information. Excess and inconsistent inventories of master data (and other wastes) is also another key issue. In every industry, there are often multiple sources or copies (an “excess of inventory”) of master data through the organization.

Unlike an inventory of parts, however, these independently established sources of master data often end up diverging if effort isn’t put in to maintain consistency. Inconsistency across “inventory” is a key defect in master and reference data and is a root cause in many business processes and analytical failures.