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Master data management tips: Whether to make an investment into master data management

Many companies are not aware of that but today there is a to get over, which is master data management.

The definition of master data

Master data came from such simple analog systems as library card catalogs, which provided support for managing all the books or documents which are in the repository. There is a lot of attributes for every book, document as well as file folder which was already documented to be approachable. The identifiers made use are anything from the date of creation or the person as well as access location.

The term metadata originated in the late 60s together with the early databases in order to make a description for the same types of data in a digital form. Early master data management tended to be tactical, whose focus is only on major domains of data such as customers, products, facilities and so on. As usual, only a small number of domains were regarded as departmental initiatives. Due to the increased recognition for the value of data and the fast development of organizational data, the result would be an organizational master data management.

For instance, in large school districts, such data as students, classes or tests and scores will be integrated in from 20 to 50 different systems. Some systems even just have one copy of the data and others adding their own attributes. That’s too much. And this is the reason why master data management applications become important to deal with all the data efficiently.

Styles of master data management

There have been various thoughts about how master data management should be used and at this point, practitioners would advocate any of them according to the needs and demands of the company. Actually, they will regard them as an evolutionary phase. The following information is about the existing industry accepted approaches.

Registry

This is the conventional though of master data management, in which the solution gathers data from the source systems and carries out the work of cataloging, merging, cleansing and showing the data for all other downstream systems such as a data warehouse. The source apps will keep the same system of record. This is the model for master data management which is the most to see and may be the most efficient regarding cost and effort.

Consolidation

This approach is set up on the Registry idea. When all of the merging and cleansing is done, business owners for every piece of data could revise and accept or augment the master data. in spite of the fact that much of master data is controlled in the source applications, the master data management platform turns out to be the system of record for all downstream apps.

Co-Existence

This approach is established on the Registration and Consolidation approaches by supporting a feedback loop to the source apps so that any updates carried out by the data owners in the master data management platform could be equipped back into the source systems. Downstream apps will still be delivered from the master data management platform.

Centralized

Centralization is the goal and maybe the source for much of the general pessimism with master data management. With this style, the master data management platform will turn out to be the system of record. Master data such as the staff, clients, students or products will be controlled in the solution before being synced with all other systems that need it, such as the transactional systems, also the originating one. If a school wants to handle the admission of one new student, the master data management system should be involved in that task. To make this process seamless, the Student Management System will capture the data from user, check the background and use the master data management platform to decide if a record is here, and if not, create it in both of the above systems. In this model, the master data is considered to be a golden record.

Master data management software

A comprehensive master data management solution should have the abilities to deal with these applications, including extract, transform and load, data quality and cleansing, metadata management, enterprise application integration and data repository. This needs a multifunctional solution. As a consequent, the conventional suppliers for Extract, Transform and Load as well as Data Cleansing are some of the leading sectors in master data management. There are a lot of tools provided by over 40 suppliers including all or several of those functions.

This is also the reason why understanding the approaches or models of master data management that your company should take advantage is so important. Master data management software choices can be classified as BI Embedded master data management and Enterprise Master Data Management.

Most commercial master data management tools tend to be a part of business intelligence platforms and specially Extract, Transform and Load tools. You can consider such software vendors as Informatica, Talend and SAP, which is how IBM and Oracle’s solutions came from. Such tools can do different functions like capturing, defining rules, cleansing and augmented the application data with semantic one.

Enterprise master data management means platforms which may have originated as ETL and BI and other relevant apps, which have been integrated but provide the full master data management technology stack.

Whether to make an investment into master data management

Nowadays, it may be impossible for you to see a company regardless of its size which does not recognize the value of the data and the costs of integration between the apps as well as the need for data governance. Senior leadership often understands that data quality and governance is what their company should make an investment into. Master data management is less prevalent as decision makers are not aware of the options and often have negative thoughts of the pure centralized model of master data management and the costs as well as complexity of the enterprise solutions.

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