Abstract: As an essential partner for Modern Data Integration, Master Data Management continues to amplify its focus on transforming data into the crucial game-changing business asset that it should be. MDM has been evolving from older approaches that take too long to make an impact on the organization, to an agile platform that responds to what organizations need and want from master data: reliable data that reflects how organizations need to work to achieve successful outcomes for themselves and their customers. 

This is Part 5 of a multi-part series on the essential interwoven nature of Business and Modern Data Integration. Click here to read previous blogs on this topic.

Master Data  Management and Modern Data Integration

Master data is very much a valuable business asset that has a growing role in many areas that should matter to organizations: strong brand presence, multichannel customer interactions, right-fit content and information, and highly variable buying journeys. To more deftly keep pace with business needs, master data management has been evolving to better mirror how businesses use data for numerous purposes. In this sense, MDM plays a significant part in the evolution of modern data integration. The recognition of the now more overt relationship between business and data management is a defining aspect of modern data integration and MDM alike. While it is tempting for organizations to think of MDM as a technical or IT function, MDM is far more about business objectives and the problems to be solved to achieve them.

Behind the scenes, MDM works to eliminate the data and process silos that threaten the ability of organizations to quickly and accurately put data to work in a myriad of processes and initiatives. The most up-to-date and useful data now comes from many sources, some internal to the organization and others from sites all over the digital landscape. Master data is critical to providing context to sources like big data, to connect big data fragments to customers, transactions and other organizational entities.

Data quality and data governance processes and practices are important parts of MDM. To succeed with accurate analytics and customer programs, centralized high quality data is a must-have. Hard work has to be done to make sure data is relevant, particularly if it originates from multiple sources of varying reliability. Many data-driven processes need to take on adaptive qualities to handle the increasing dynamic nature of business activities today. But this only works well if data is continuously trustworthy and timely.

The “New Age” of  Master Data Management

Offerings from the newer master data management solution vendors, and new approaches by established MDM vendors, are responding to what organizations need and want from master data: reliable data that reflects how organizations need to work to achieve successful outcomes for themselves and their customers. Many MDM solutions are moving towards: faster and more streamlined implementations; broader capabilities in a single extensible platform; and the ability to work with any kind of data. Organizations need multi-dimensional knowledge, context and insight related to relevant entities, such as customers and products. Today, master data entities extend beyond this classic pair to include domains such as asset, location, supplier, finance, and personnel data.

The world in which businesses operate isn’t just “relational”. Relationship data grows in importance. MDM solutions must handle complex connections, hierarchies and relationships between multiple entities or domains. And companies must go beyond traditional transactional data to capture data describing behavioral aspects that amplify context. These newer kinds of master data may not be best stored in relational data repositories; as a result, graph databases are gaining importance for MDM effectiveness.

Cloud-based solutions are becoming the norm for modern data integration, especially with the growth in rich options for secure data flows from anywhere to anywhere. MDM solution vendors are following suit for cloud offerings, though at a slower pace. Organizations should already be extending data governance processes and practices to include any SaaS / cloud generated data, and most organizations pull in data from cloud sources for master data processes. So the cloud transition should be a “natural” move for MDM solutions themselves. 

Data Governance Future Direction

Data governance is the pathway for orchestrating practices, processes, policies, and the involvement of many business roles, to assure that master data is available as a trusted and vital asset for the organization. A new direction for data governance is to create policies and practices that reflect the continuously changing needs of dynamic or adaptive organizations that want to be able to respond quickly to business opportunities and challenges. This means that data governance activities have to develop faster to be effective. At least that’s the ideal goal.

Unfortunately internal politics in organizations continue to weigh down data governance efforts. Direct involvement of corporate leadership is essential to ensure that cross-enterprise collaboration and cooperation are kept as a top priority to empower data governance efforts so necessary for optimal use of data assets. Tasking upper management to recognize that data and the solutions that manage it are strategic functions for organizational success is a game-changing move.

Data Priorities Run on Business Processes

An imperative of master data management is to understand how the business operates: how the business uses key data and processes, how processes interact with data, and which business processes matter to strategic focal points. From an understanding of business processes, the best approach or approaches can be determined for implementing a master data ‘plan of attack’ that not only creates trustworthy, consistent master data but is responsive to changes in the business and to new data that continues to be collected / generated by business processes. The convergence of data and application integration and the increasing importance of orchestration hubs that manage various data-related processes, pave the way for master data management to have a natural partnership with modern data integration.

Organizations strive to determine how to extract value from data assets. Data and data-oriented solutions by themselves don’t usually map directly to business outcomes, and as such, usually can’t be measured to determine the value contributed to desired outcomes and the achievement of key corporate goals. It’s through business processes that data can be mapped to business value. The key focus should be the business processes that impact revenue, business agility, competitiveness, and overall positive performance, connecting work to desired outcomes that lend themselves to various metrics. However -- both reliable data and effective data governance are essential enablers of gaining such value from business processes.

MDM: Never Just About  the Technology

The increasing focus on master data in relation to business processes extends usage across the organization to more functions and business roles. For MDM and data governance, technology has long taken a back seat to strategy, planning and the creation of practices and processes – all of which are people-intensive activities. The involvement of business roles is critical for data governance to work, and to validate the MDM approach and keep it current to business needs.

As a reflection of the long-time participation of business roles, MDM technology solutions continue to provide more access points for a variety of business roles. This aligns with efforts by modern data integration solution vendors to recognize the importance of integration activities to be performed by both technical and less technical roles. Modern data integration solutions could benefit from taking a closer look at the trail already blazed by master data management and data governance initiatives, in terms of involving more business roles in many aspects of strategies, practices and processes.