DATA WAREHOUSE AND DECISION SUPPORT SYSTEM. Management Essay

DATA WAREHOUSE AND DECISION SUPPORT SYSTEM
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Globally, most organizations heavily rely on information technology in operational and decision-making activities. Hart D. & Gregor S (2010) argue that it is nearly impossible to miss the major role that the systems are playing in making work easier by automating services and processes. According to Bahner (1999), information systems are technological devices that are used in communication, processing and automating processes within an organization. On the other hand, people are the users, system developers and the controllers who are responsible for ensuring the system works efficiently
In the modern business world, organizations have faced stiff competition due to growth and development of the economic sector. This marks a big problem for organizations, thereby making them to actively react by making strong strategic decisions to remain relevant in the industry. As a result, there is a need to manage and store data for future use. Most of the organizations all over the world, work toward achieving the strategic goal of making a profit by gaining a competitive advantage and venturing into a global business (Kyriazoglou, 2012). In order to overcome this pressure, making critical strategic and technical decisions, storage and continuous use of the data are highly recommended as it is a basis for future outcome. Therefore, this paper seeks to explore on theories related to the data warehouse (DW) and decision support system (DSS) and their wholesome development and functionality in decision making as well as incorporating them in current systems to find the area of improvement.
Since the invention of the new technology data warehousing has been used in many sectors of the economy, such as production, banking, marketing, health, legal and education sectors simply because of arising demand on the support when making decisions. A data warehouse is an electronic storage, that is, a database of collected data from various related sources in an organization, which helps to make managerial decisions. According to art D. & Gregor S, (2010), the data warehouse includes subjects such as customers, employees, financial management and products. Then data integrated into a warehouse is cohesively set into useful information from other information systems. This data is accurate to some date and time variability, and it is not prone to change once it enters in storage. However, a data warehouse is the basis for data analysis and reporting in a business intelligence system
In a wide business world, a data warehouse can integrate its consumer’s information into its database from the point of sale, customers’ mailing list and feedback to forecast on production regarding amount quality type of product and at what time are the products required. More so, employee information collected from the human resource department is used to prepare end month payroll. It is, therefore, easier for a company analyzing the available data it has to make holistic decisions that could lead to its profitability, (Kyriazoglou, 2012) Operational sources, provide the warehouse with raw information, for example, cash registers, office computers, stock ledger, then, the data is transformed by removing its redundancy and aggregating to meet granularity level, (Alamar, 2013). Afterward, data that are not prone to change is loaded into the warehouse for the user's availability.
Data flow in Data Warehouse
Data martDepartment 1Outflow
Data warehouseUp flow
Primary sourcesAccess tools by the user
Inflow
External storageData martDepartment 2Up flow
Down flowOutflow
Subsequently, data warehouse technology is an area that has not been fully utilized in many organizations. Before making an inclusive decision in a company, there are some factors that management has to consider. Of which they are complex using human intelligence only. For that reason, they require to integrate a data warehouse and a decision support system which helps them gain different useful information from databases to make explicit decisions. DSS hence is a collection of the data software tools and techniques designed in such a way that management can actively interact to analyze problems evaluation and make the appropriate decision (Bahner, 1999).
In most cases, a database in DSS is extracted from the Management Information System (MIS) and Transactional Processing System (TPS), but they accommodate data from other sources. For example competitive prices. This system answers ‘what if question.’ Among the most preferred types of DSS are; communication-driven, data-driven, document-driven, knowledge-driven and model-driven DSS. Commonly, the rising difference in the above systems is how the data is stored and processed. Nowadays, due to demand for efficient database and technology, the point of interest counts on data-driven DSS.
Generally, the current trends like stiff competition, continuous improvement, and change in technology, new and improved products, and services, facing global and local business are the main reasons why managers are highly investing in data-driven DSS to provide business data to make relevant decisions. The key towards gaining super revenue and higher profit in business is developing a data warehouse. It is therefore advisable to involve all levels of management to ensure that all relevant data to develop a decision support system is captured. Fitzgerald & Adam (2016), found that the environment in which data is going to be used is defined, primary sources are identified to extract data, cleaning, and transformation of the same are the activities that help in structuring a data-driven DSS.
Also, necessary data is collected and loaded into a database where cleaning is done to remove errors and repetition, add the missing information and analyze data to ensure its integrity has not been interfered with. More so, some other information like date and time may be added. Once the data reaches the warehouse, it is made available to the users through one user identification, a policy made to protect and maintain data integrity. Due to large volumes of data in a warehouse, metadata is used to identify the location of a particular data descript...