Master data management involves several steps that need to be executed. Some people have underestimated this process as just a simple check and balance for operations to run smoothly. What we do not realize is that it involves a lot of research, application and even a touch of marketing to ensure that everything is completed. A good master data management consultant will not be satisfied by a general, one-size-fits-all package; they should be able to conduct extensive research and investigation within your company to ensure that everything they recommend would be exactly what your business needs.

Tailor-fit and customized are two words that should accurately describe master data management. It is not just about the accuracy of information that goes in and out of your company. Everything should be right fitted to the unique processes and policies that exist in your organization. Aside from that, the right master data management company will ensure that the accountability does not fall on one person alone. Everyone should be able to play a part in keeping all your records up to date and well maintained. Being able to instill such a discipline may be challenging at first, but with the right strategy, you can truly enjoy consistency and success in the competitive world of business.

1. Identify master data and its sources – Identifying what data needs to be managed and where it is stored allows for appropriate methods to be implemented for its management, and can also reveal some problems at the very basic levels of data collection.
2. Identify who produces and uses master data – By identifying what generates the master data and the applications in which it is used, targeted approaches to managing this data in a productive way for both ends can be made.
3. Analyze the details of the data – The type of data, its dependencies, what constraints exist for the current system used to store data are all important variables to consider when implementing a data management system.
4. Develop a model – A prototype of the master data—what the data should look like, what should be stored in it, how large it is, standards that should be used when recording data, etc.—is necessary to ensure that all data meets the criteria and assumptions under which the MDM system operates.
5. Choose the method and tools to be used – After the details about the data are known, a set of tools that best fits the needs of the organization with regards to its master data can be selected, modified or created.
6. Developing infrastructure and maintenance standards – With the systems in place to manage the data, how data is stored and maintained becomes the next concern. Without proper standards for updating and destroying data, the systems implemented for managing it will fail to remain consistent.

Data networks provide a method for the transmission of data within an enterprise, allowing for the transfer and storage of information. Data can be held on a common server, accessed by various departments or subsidiaries of an enterprise, and used in business operations. Utilizing a data network, it becomes possible to efficiently collect important master data from different sources, allowing for its consolidation and proper management. Data networks also create an easy method for sharing critical business information to be used in daily transactions and business functions, as data can be accessed from many different locations in an enterprise, allowing for corporate data to be quickly shared between departments or even companies.

Data warehouses serve as storage location for the master data, providing an electronic repository where data can be analyzed and reported. In addition to the storage of data, data warehouses involve processes for the retrieval, analysis and management of data and information needed to properly access it. Data warehouses are generally optimized for data retrieval, and lack data normalization to preserve data integrity, which is instead handled by the operational systems that feed information to the warehouse. Some of the advantages of data warehousing are the use of a common data model to increase the ease of analysis and reporting, independent yet cooperative work with operational systems to allow for data to be accessed without slowing down operational systems while still enhancing their ability, and long-term storage for master data that may be destroyed on the source systems and still remain intact in the data warehouse.

A subset of the data warehouse is the data mart. Data marts contain data relating to a more specific purpose or subject, and are used by groups within an enterprise to access relevant information to their business needs. Data marts can exist independently of a data warehouse, but generally data marts are either consolidated to form a data warehouse, or subsets of data are extracted to form data marts. Due to the more limited scope of data marts, they provide easier access to frequently-used, relevant data for specific entities in an enterprise as well as creating a data access point with a more defined set of end-users to facilitate operations.

In contrast to the more long-term storage intended for data warehouses, operational data stores are responsible for the more volatile “real-time” storage and use of data. Relevant and detailed data is gathered from multiple sources and stored to allow for access to current operational information needed for daily transactions and corporate functions. Data is cleaned, any redundancy is resolved, the remaining data is checked against enterprise rules for data integrity, and then this enriched and refined data is stored, with data being frequently rotated as more recent data history takes the place of older data that is then relegated to a data warehouse.

IBM’s solution for master data management is a multiform approach which is intended to target what they consider the root cause of master data complexity – how the data is created, analyzed, managed and accessed. Their Multiform MDM aims to centralize all these processes, identifying commonalities and using them to streamline operations. At the same time, the Multiform MDM allows for a number of domains and styles of data management, allowing for the centralization of not only the data itself, but the processes that manage it. By homogenizing the applications that control data, consistency is maintained throughout the enterprise, and data can quickly and effectively be utilized. IBM offers different master data management servers to accomplish this task, for things such as product information as well as a central master data server.

Oracle has a number of MDM applications that work together to provide a complete solution. Oracle aims to consolidate accurate master data, maintain this data and distribute it to different applications using a shared service. As such, Oracle offers a MDM suite which includes their Oracle Customer Hub, Product Hub, and Site Hub as well as their Hyperion Data Relationship management application. Hyperion provides a solution for financial and analytical master data management, allowing for the creation of cross-functional perspectives of data while retaining the ability to create separate departmental views of the data to provide for consistent access to requisite data among different entities. The Oracle MDM Suite uses integrated applications to consolidate, rationalize cleanse, govern, and share master data throughout an enterprise.

With its acquisition of Stratature, Microsoft has moved into data management solutions. Microsoft offers the SharePoint server to integrate with their pre-existing Office suite, with the aim to provide a data management solution that also allows for other vital corporate functions to be performed from a single, centralized location. SharePoint provides features such as:

Social computing and collaboration – These allow for teams and employees to work together and share information, streamlining data creation and storage as well as minimizing data mismatch.

Enterprise Content Management – Proper content management allows for the storage and management of corporate records, documents and Web data. In addition, ECM allows for the streamlining of content-driven business applications as well as the protection of important data.

Business Process and Form Management – Standardized business forms can be created and managed, as well as integrated with a database to streamline the accurate creation and collection of important data.

Business Intelligence and Enterprise Search – Allow critical information to be located and quickly accessed, data to be analyzed and viewed, and reports to be generated to expedite the implementation and usage of data. Enterprise search also integrates with every facet of the SharePoint server and Office suite to allow information to be easily located, utilized and shared to increase corporate productivity.

In a transaction/repository-style hub, all relevant data is stored and accessed from a single database, and the database must contain all of the information needed by the different applications which access it. All data is consolidated and centralized, and published to the individual data sources after it has been linked and matched. This style of hub allows for a single source of data to be created, minimizing duplication by making it easier to detect as data is collected and cleaned. However, the transaction/repository style has drawbacks as well. Existing applications may have to be modified to use the master data, and in some cases this is not possible. Different applications and services which serve as an interim interface between the MDM software and the data-dependent applications may be needed and this can add to costs. Also, data models need to be complex enough to include all relevant information for the applications that utilize them, but not so large that they become overly large.

Registry style hubs, in contrast, do not store master data in the hub, but rather master data is maintained within native application databases. The hub instead stores lists of keys with which to access all relevant attributes for a specific master data entity, linking these attributes between application databases. The registry style hub allows for applications to remain fairly intact as all data is managed within native databases. However, when requests are made to access master data, data must be located, a query must be distributed between numerous databases, then a list of the requested data must be formed all in real time, and as the number of source databases grows, this can become increasingly inefficient. In addition, duplicate data entities can reside on different databases, or even within the same database, and while consolidation and cleaning of individual databases would be ideal, it is not always practical. Another disadvantage is that when new databases are to be included in the hub registry, new keys must be added to the existing tables, which may also require altering how queries are generated.

Hybrid style hubs utilize methods from both transaction/repository and registry style hubs, and try to address some of the issues present in each. Since it may not be practical to update existing applications or to send inefficient, massive queries across several databases, the hybrid system combines some of the advantages present in the other models by leaving master data on the native databases, generating keys and IDs to access this data, but replicating some of its important attributes to the hub. When queries are made, the hub can service the more common requests, and queries only need to be distributed for the less-used attributes, which results in a more efficient process. While the hybrid style combines advantages of both of its parent models, it has its own disadvantages. Since it stores replicated data from outlying databases, it may run into updating issues, and, like the transaction/repository style, deciding which attributes to store, naming to be used and format to store them in can create problems.

In an instant, you will have access to vital information that you may need to make an very important decision. Without the proper information and data, it is not possible to predict trends, or create new services, contact suppliers and customers, as well handle personnel.

Part of a good MDM software is being able to synchronize the supply chain, sales and after sales, products and merchandising, as well as marketing, accounting and risk management, government regulatory reporting and trade reconciliation.

As they say, information is gold, and so without it, your company officers will not be able to function effectively. It would be impossible to expect them to remember everything and retain the data in their laptops or even memory banks.

A good MDM software must also address the issues to do with inaccurate or inconsistent terms and data. Simple concerns such as how to type in figures could spell disaster for the company.

The three basic areas of MDM are people, location, and assets. These are the master files. The MDM software you should get is one that can be very detailed and accurate, at the same time handle the right process and data flow. The main objective of a good MDM software is to always have uncorrupted data. This is the only way a company can move forward. It should avoid bundling of different applications that could conflict with each other, especially if the company has networking and sharing.

One way to analyze a good and effective MDM software is by checking your organizational chart. Here you will see what are the major areas of the company and how they work together to produce a desired result.

From your organizational chart and working with a MDM software, you can plug loopholes and create a more accurate data base. The other benefits of a effective MDM software is basically that the company will be able to function more effectively,  and in the process, this will translate into better performances and higher sales.

For example, officers and supervisors can make better and quicker decisions. Your customers will be attended to properly. Their needs, as well as their feedback will be part of your database. It will take you less time constructing a marketing plan for new products because you have a better data flow.

As far as purchasing and production is concerned, you can accurately trace all transactions in and out without batting an eye. As long as you have implemented the “proper rules” in data entry, you will keep your MDM software up to date and consistent.

All these groundwork, if done properly, will ensure that employees as well as officers and owners will be able to function more effectively and there will be less time wasted and back jobs due to inconsistencies and misinformation.

As a result, you will have a well oiled machine, and that’s what every company needs today if they want to keep up with the competition, and surpass them.