What is Master Data Management?
Master Data Management (MDM) is a method used to define and manage the critical data of an organization and provide a single point of reference, leading to fewer errors and less redundancy in business processes. Master Data Management tools collect and aggregate different types of data into one source and act as one clean and consistent source of truth for modern, data-driven organizations. Simply put, MDM streamlines data across departments and systems within an organization. Businesses using MDM enjoy peace of mind that the data throughout the enterprise is accurate, up-to-date, and consistent.
How Does Master Data Management Work?
MDM solutions cleanse, transform, and integrate data from multiple sources. As data sources are added to a system, MDM uses a uniform set of identifiers and extended attributes to cleanse and then classify the data. So how do you identify what data should be included in your MDM solution?
First let’s take a look at the types of data typically found in corporations:
- Master Data – This is the core data that is essential to operating a business. It describes transactions, customers & prospects, locations/addresses, and products. This data changes infrequently, and the items included must be carefully considered.
- Hierarchical Data – This data describes relationships between elements with 2 or more levels, such as a product and its raw materials, or a parent company and its subsidiaries. MDM tools help define and maintain data hierarchies.
- Meta Data – This is data about other data. It often describes data attributes, such as size, author, or creation date. It is typically kept in a repository or XML documents.
- Transactional Data – This data describes transactions such as sales, invoices, claims, and service tickets.
- Reference Data – Master Data that is used to categorize other data is called reference data. Examples include states, countries, continents, currencies, time zones etc.
How to choose what to include as Master Data?
Master data is the most complex and most valuable data to your organization, and therefore must be carefully managed. When choosing what data to include as master data, one should consider the behavior, lifetime, complexity, and value of a given entity. Master data should be categorized within the context of business needs so that accessing and utilizing the data is intuitive and painless for all users.
Challenges of MDM
Master Data Management is an enterprise-wide endeavor that must accommodate the needs of all business functions, and it is not always easy to pull off such a complex, yet vitally important feat. There are several challenges in MDM implementation:
Lack of Effective Data Governance – Hundreds of decisions must be made in the course of a master data project, and if there is not a well-defined decision-making process, a project can fail because politics prevent effective decision making. Having an established set of data governance rules with strictly assigned ownership helps you control how long data is kept, how it’s maintained, and how changes are authorized and audited.
Stakeholder Engagement – MDM is not a front-end system, and only a few data stewards typically engage with it. Because of this, there is a risk of MDM being seen by business users as a purely IT initiative, and they may not immediately see its value. Stakeholders need to be constantly engaged and informed about MDM performance and business benefits.
Straying from MDM requirements – When building your MDM system, it is tempting to push some requirements onto it that cannot be handled by other systems. Requests to maintain an attribute in MDM even though it doesn’t fall into the Master Data category can lead to compromises and bad design decisions, and it is very difficult to correct down the road.
Loss of Data – The algorithms used to link, merge, and consolidate multiple records into a single golden record can lead to data loss. Survivorship implies that some data is used while other data is discarded, and this loss can negatively impact business processes downstream.
Misalignment with Enterprise Data Initiatives – MDM must be coordinated with other enterprise data solutions such as data quality management and data governance so that all three can work together synergistically. Often though, MDM activities are isolated within individual departments or groups, which prevents them from capitalizing on existing data management tasks and programs.
MDM is not an “if we build it, they will come” type of technology investment that IT can push on the business. The business must take ownership of the definitions and measures of quality, and work in tandem with IT to develop the appropriate solution. As I mentioned, MDM is a business capability, not a technology solution, and it’s a business capability that cannot succeed without the business playing an active role. – Rob Karel, Forrester Research
Key Features of a Master Data Solution
Your Master Data Management solution should include the following features:
- Matching & Linking – In order to avoid duplications and variations on the same data, your MDM solution should transform data from a multitude of locations into a single, accurate record. Without this feature you risk alienating customers and skewing your analytical results.
- Ability to Apply Business Rules – Business rules allow you to create, apply, and manage your data governance policy. They should be centralized so that when a change is made to a piece of data it automatically updates everywhere that data exists.
- Ability to Manage Location Data – You need to be able to manage location data and place restrictions on its usage and access for certain geographies in order to comply with regulations such as GDPR.
- Steadfast Security – Role-based security policies allow you to define access & edit rights to data, helping you keep your data safe and secure at every touchpoint.
- Data Enrichment – Your MDM solution should regularly cleanse and streamline data in order to provide richer customer & product records and more insightful analytics.
Founding Pillars of a Strong MDM Program
Building a strong Master Data Management program isn’t as simple as installing a new piece of software—your data needs to be hand-curated and relevant to your business needs.
A successful Data Management Program should be built around these pillars:
- Governance – Make sure your data inputs are well-defined to ensure accurate and certified master data.
- Management – Constantly measure for data quality and improvement.
- Policy – Require strict adherence to MDM policies & standards.
- Process – Have a defined process across the entire data life-cycle.
- Consent Management – In order to comply with data regulations, your solution needs to provide a 360° view of each individual customer that clearly outlines what data usage they have consented too.
Business Benefits of Master Data Management
Eliminate Redundancy – MDM removes redundancy by consolidating and streamlining all your business data.
Master Data Edits – When one record is edited in MDM the update is pushed to all locations company wide, eliminating data discrepancies.
Effective Data Analysis – MDM provides you with smart, reliable data that empowers you to make business decisions with confidence.
Authoritative & Consistent Data – MDM provides accurate data that can be relied upon.
Increased Efficiency – Better data leads to better efficiency, which leads to happier customers and an improved bottom line.
Increased Machine Learning Potential – An MDM program applies hierarchies and taxonomies to data, priming it for machine learning.
Better Data Privacy – As new data privacy regulations are rolled out, organizations need to be able to track and account for all data and ensure it’s being used properly. Master Data Management makes this task easier and more efficient.
Master Data Management for Enterprise and Mid-Level Organizations
It is now easier than ever for enterprise and mid-level organizations to consolidate and standardize business processes and technologies using master data management. The benefits of MDM are maximized and provide the most ROI when the program is spread across an enterprise and multiple lines of business. If enterprise MDM is coupled with comprehensive data governance, it can facilitate a seamless flow of information from department to department, acting as a powerful tool for overall digital and business transformation.
A successful enterprise MDM implementation requires a lot of groundwork and clearly defined steps to overcome obstacles. There are a few “must-do” steps to ensure that an enterprise MDM solution is a success:
- Set the MDM Vision: Focus on aligning your MDM vision with your organization’s business objectives. Clarify that MDM is not only an IT initiative.
- Gain High-Level Buy-In: Focus on business-oriented scenarios, set clear ROI goals, and obtain sponsorship from across the organization.
- Carefully Choose First Project: The success of your MDM program largely hinges on the success of your first project, so choose it carefully to maximize your chance of a positive outcome.
- Deliver Value Early & Often: start implementing your MDM program in small phases and with each phase, demonstrate the value and request more funding. It’s especially important to highlight how your MDM program can provide cross-departmental benefits.
- Maximize Benefits & Share Costs: If you institutionalize MDM across your organization you can split the cost of implementation while maximizing benefits. This will also ensure program longevity.
Laying a strong foundation for data management early on at your organization helps you expand and diversify with confidence. If your vital business data is kept up-to-date you can easily add new departments, employees & roles, and new applications without having to re-invent the wheel with each change. When contemplating a merger or IPO, having one access point to all of your critical data is priceless for both you and your investors or partners.
How MDM enables Digital Transformation
Reliable and consistent customer data is one of a modern organization’s most valuable assets, and Master Data Management can help uncover the hidden relationships between people, products, and places to create a holistic customer profile.
Customer 360 with Master Data Management
Customer 360 has existed for years in various iterations, and it mostly means aggregating customer data from different sources and cleansing and analyzing it in order to get an idea of a customer’s behaviors and preferences. The biggest impact on customer 360 today is the vast amount of data that an organization has at its fingertips at any given time. Harnessing said data to positively impact the customer experience is where MDM comes in.
Using MDM, organizations can aggregate data from internal and external sources, including social media and third-party applications. MDM helps you match, merge, and enrich data to complete customer profiles and correlate them to their omnichannel interactions and transactions. When paired with analytics and machine learning, MDM delivers better data quality and enables actionable recommendations that allow you to craft a personalized customer experience, identify new upsell opportunities, and devise a plan to increase the customer’s lifetime value.