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.