Master Data Management Explained

Master Data Management Explained

Master Data Management (MDM) is the management of an organisation’s official shared master data assets in order to ensure that it is accurate, consistent and uniform – to provide a single point of reference of the data for the whole organisation.

This is important because master data is often the core data that is critical for the operations of a specific business or business unit. It typically consists of the data of everyone that the organisation conducts business with, including customers, suppliers, vendors, prospects and partners, places, places, materials, products, services or assets, and even financial and organisational data such as organisation structures, chart of accounts and price lists. In other words, master data can cover relatively static reference data, transactional, unstructured, analytical, hierarchical and even metadata.

As it is often used by different units and groups within the same organisation, it may end up being distributed and stored redundantly, causing the data to be inconsistent and inaccurate. That is where master data management comes in to eliminate redundancies, ensure the quality of master data and provide one point of reference for critical information through the use of different processes such as data identification, cleansing, transformation, repair and integration. Most importantly, MDM streamlines this whole process, which traditionally used to be quite labour intensive.

To avoid relying on multiple, conflicting sources of information, which could end up can causing costly errors, data inaccuracies and misleading analytics, organisations use master data management software to accumulate and consolidate data from different sources into something called a master file, which can then be used as the primary resource for all business processes and applications.

Therefore, when executed correctly and implemented as part of a larger enterprise planning strategy, MDM is able to automate and optimise business processes, reduce operational costs, allow for easier sharing of data between departments, increase usability, ensure consistency and reduce the complexities of data management, even as data continues to grow exponentially.

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