What is Master Data Management?
Master Data Management refers to the processes, technology, and overall governance methods related to ensuring the quality and overall integrity of key enterprise data. With the streamlining and integration of enterprise business processes, the underlying data and meta data needs to be consistent. This also requires alignment of key master data across various enterprise systems. In a typical large enterprise data related to products, customers, suppliers, orders, employees, etc. is stored in multiple systems that includes ERP systems, legacy applications, data from social media platforms, IoT devices and sensors, new cloud applications, native digital apps, and so on. As number of data sources increase, it is all the more important to ensure consistency of information shared across various business processes.
The quality of data has ramifications on business intelligence, dashboard reporting, decisions made based on those reports and intelligence, etc.
Significance of MDM in the Digital Enterprise
With the emergence of the digital economy where more than unifying the business processes in its value chain, an enterprise also connects to external partners in a broader ecosystem, the flow of data into the core of the enterprise requires that the data in cleansed and in accordance with the policies and technical formats of the enterprise systems. So, as an example, if the thousands of suppliers that provide retail data on products that have to be loaded on enterprise systems, the data must all conform to certain guidelines and have all the required attributes before they can be uploaded in the central enterprise repositories.
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Complexity of digital business processes
As enterprises connect with partners and other stakeholders in broader and deeper business ecosystems, applications become more complex along with associated data.
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Complexity of Enterprise Architecture
As the domain of the enterprise architecture expands to include systems and data on the cloud, it further necessitates understanding the lifecycle of data. Such complex systems configurations require more data stewardship and governance processes and rules to ensure integrity, security, storage, archival, and other data management issues.
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Data Analytics and Big Data
The wider adoption of Big Data and analytics is further necessitating consistency of data across various business domains and processes to ensure analytics software and systems can do the right matching and deliver useful business insights to the enterprise.
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Compliance Pressures
With increased compliance and regulation requirements in certain industries such as health and life sciences, government, etc. data stewardship and governance issues are becoming increasingly important.
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