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Enterprise Data Management Terms Paper

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Enterprise Data Management Terms Paper


University of Phoenix

Sean Kern, Facilitator

There are various terms that are associated with Enterprise Data Management. Some of these terms are UML, OLAP, OLTP, Data Warehouse, Data Mart and Multi-Tier Architecture. Subsequently, these terms were covered during the five week course of DMB405 and will be explained in further detail throughout the course of the paper. Although the paper will not be all inclusive to the detail of each term, it will touch upon the definition, their use and their place in Enterprise Data Management. The first term that will be discussed is UML and how it relates to the subject at hand.

UML is the industry acronym for the Universal Modeling Language. The Universal Modeling Language is used for software, business, hardware and organizational object structuring. In 1997, with the help of three men and their teams, UML was created and adopted as an industry standard by the Object Management Group (OMG). The Universal Modeling Language is a collaboration of many different people and numerous modeling approaches put together as one unified standard for the purpose of enterprise data management. Not only is it now an industry standard in the United States, but also internationally. UML was created so there were not so many different modeling approaches to arrive at similar conclusions and solutions. Too many approaches can be confusing because things are not uniform. With the creation of a universal standard for modeling, diagramming and conceptual illustration, all stakeholders can in effect, be part of the process and understand the project more thoroughly. The standard creates a common theme amongst the vast array of data that makes up a complicated project and brings it to a level that all can partake in during the analysis and developmental stages.

Online Analytical Processing (OLAP) " an approach to quickly provide the answer to analytical queries that are dimensional in nature." OLAP is part of the database system in that it allows for relational reporting and data mining. The capabilities of OLAP in the enterprise data management are great. It allows for effective business reporting in sales, marketing, performance management, budgeting, financial management and forecasting. Online Analytical Processing is a variant of the more traditional Online Transaction Processing (OLTP) that was used prior to the introduction of OLAP. Although both OLAP and OLTP both are used to produce data warehouses, OLTP is considered the "old mindset" that focused far too much on data structure with tightly controlled planning, schedules and milestones. OLAP, on the other hand, takes a far less structured approach to data processing. OLAP takes a more experimental approach to the methodology and is considered far more effective in completing the project. This type of newer approach can benefit the organization in the long run and leaves more room for more creative design.

The next term, as it relates to enterprise data management, is Data Warehouse. A data warehouse is a large collection of logical data that has been stored from many other operational databases, for the purpose of data analysis and collection. The information in the data warehouse allows the organization to go back and collect data in order to help them make better quality decisions based upon the findings. In addition, the use of a data warehouse can be for archival purposes and because of its nature, allows for more flexible and efficient data storage and research. It also allows the user to get a more unique look at the overall data instead of looking at the information from a day-to-day perspective. This gives the corporation the "big picture" verses a small cross-section of daily activities and operations. A data warehouse is very beneficial, overall, because it allows for an organization to store their large volumes of data centrally and have the ability to strategically look at it when the reporting need arises. Because the data can become quite large, even for the warehouse, the ability to break it all down farther into data marts is essential.

Since data warehouses can become large, there is a need to break the information down even farther into smaller logical units. These smaller logical units are called Data Marts. Data Marts allow for greater efficiency and reporting by categorizing and storing data together that is more similar in relevance. Like the data warehouse, the data mart is designed to



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