Guide to master data management for beginners

  1. The importance of data quality when it comes to master data management

It is rather true to say that master data management is a part of an information quality strategy as it is able to deal with different problems ruining an information quality framework such as lack of real-time data, copies and so on.

Master data management connects different data items together which are related to the same logical object. This may cause some problems in terms of consolidation for the system.

Until now, there is still no agreement on how common data items ought to be saved. Therefore, as you make effort to connect disparate records for your companies, you will need to make decisions on which source you should choose to be the most reliable and accurate one.

Nevertheless, the issues associated with master data management is more and more serious as it may take you several months to resolve the data problem when working on a data migration project.

Master data management depends much on real-time consolidation so that these complicated regulations often need to be connected the infrastructure which indicates how sophisticated to implement master data management.

  1. The importance of data governance for master data management

As you may not know, if there were not data governance, it would be more difficult for master data management to become successful. In other words, if you want to set up a master data management strategy, you had better get a well constructed data governance framework first, which is designed with all the business subject areas you focus.

  1. The technologies needed for master data management

The first technology is master data management hub. This one comes with a persistent hub delivering all the important data of the company into the hub from the source system, a registry hub in which only identifying information and key record identifiers are duplicated to the hub ad finally a hybrid hub, which combines both the above two hubs and is used to control what happens in the hub.

Secondly, it is data integration or middleware, which is required to synchronize data throughout the disparate system landscape. As you may know, you always need to sync any data quality advancements occurring so that the advantages can be maintained while improving the quality regularly.

In terms of data quality tools, there are five types including data quality auditing, data quality cleansing, data quality standardization and hybrids.

Among them, the hybrid tool comes with all the elements of the other data quality functions while incorporating ETL abilities.

  1. The reasons proving the popularity of master data management

As you can witness, master data management has been so popular these days. Here are the reasons why it is adopted widely all over the world.

First and foremost, master data management issues have influences on the entrepreneur. The precious key of a business success is its clients, its services, its products and its staff. When it comes to this point, master data is one of the most vital data that a company has and they have to fix the problems in the past even if they are the smallest one causing problems. Many companies have recognized the benefits brought by master data management that can generate more profit for them.

Secondly, master data management deals with higher complexity and globalization. Nowadays, master data management is truly a driver for an Information Development approach. Organizations are now possessing an increasing amount of both information and data. Reducing that complexity is really important to succeed. Also, globalization has resulted in a lot of issues and complications to manage data. Some of the problems can be listed as multi-character set issues, data availability demands and so on. Companies also receive more information and have to offer data to different channels on time, which is truly in need of master data management to handle.

Thirdly, all sides realize a big chance. Master data management is a complicated and important issue. Therefore, this is a big chance for both product providers and system integrators. New master data management technologies have been developed increasingly every day. In spite of the fact that the data hubs look similar to their predecessors Operational Data Stores, modern data hub technologies are being take advantage to leverage a wide variety of modern technologies which were not integrated in the old tools. What is more, because of the fact that the problem is referred to information management, each information management provider should have a solution. Vendors prioritizing application also recognize this as a big chance to extend their integration and application area. On the other hand, companies with master data management issues are carrying out different approaches to frame the problem more effectively. They have to solve many challenges in the information management space.

Last but not least, what should be concentrated is also compliance initiatives. Currently, these initiatives have put extra pressure on the companies. If there were not a master data management solution, companies have to cope with more and more difficult issues in order to support increasing regulatory demands.

  1. The challenges brought by master data management

Last but not least, let’s discuss the challenges from master data management. The difficulties to get over the process of delivering a master data management are really similar to data migration tasks.

The first challenge is complexity, in which companies face with complicated data quality issues with master data, especially when it comes to customer and address data from legacy systems.

Secondly, there is a high degree of overlap associated with master data. For instance, large-size companies are saving customer data within different systems in their business. Moreover, companies often lack a data mastering model to define main masters, less important ones and so on. As a result, the problem of master data complex will take place. Finally, we can not ignore standards. To be more specific, it is often not easy to reach an agreement on domain values stored throughout various systems.




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