There are many forms of DBMS (database management systems) which include such company areas as accounting, human resources and customer support systems. As large organizations generally only needed these types of DBMSs to handle the large amounts of data exchanged and stored, it is now a commonality in many companies, both large and small, and is a fairly standard part of any company's back office system.
Data management is an important aspect of the enterprise server management structure. Through proper and structured management of all corporate data, a company can engage in secure and through using both proper data management tools and IT system tools.
Data management is comprised of a variety of disciplines and the official definition provided by the DAMA (Data Management Association) is that 'data resource management is the development and execution of architectures, policies, practices and procedures that properly manage the full data lifecycle needs of an enterprise". As this is an extremely broad definition it generally focuses on a server side data management and data mining, but within this paper there is a need to have a broader focus of the end-user data management which will encourage employees to have a central repository for their files.
Many times a company will focus on just ensuring the data is secure but fail to engage their employees in training on these data management and data architecture systems. There are two formats for training that will be discussed in Chapter 11 that include an advanced training curriculum for the DBA's (database administrator) to maintain the servers using the necessary IT tools. The second format of training is for the average end user in how the architecture works and the new tools that will be used as a central repository for files by using such type of peer-to-peer network tools much like Microsoft SharePoint Services.
This paper will detail in full the topics that comprise the data management spectrum as well as training and end user tools that are instrumental in ensuring that all employees within a company that are getting ready to implement data management disciplines. The paper will also provide a UML diagram of the data management architecture to be set up for the fictitional company along with questionnaires and training modules for understanding where the data will be situated.