Data warehouses research
Inmon, data warehouse may take physical or logical form to capture information from diverse sources for the core purpose of business intelligence. Unlike databases data warehouse maintains a long-range view of data over time (Thareja, 2015). Data warehouse allows for consolidation, analysis and reporting of data at different levels. Data warehousing reduces data complexity and makes managing of data to be easy. With a wide range of uses in business, healthcare, finance, and industries, data warehousing has continued popularity in retrieval of information for decision making. Manufacturing organizations use data warehouses to analyze current business trends, detect warning conditions, and view market developments and to predict market changes. Over and above these uses, data warehouse serves an important purpose of identifying weak points within the organization through analysis of customer feedback and product portfolios.
One of the most crucial sectors that utilizes data warehouse is the healthcare sector. Data collected by electronic medical records (EMR) undergoes integration and stored in data warehouses allowing for access and analysis of data by an organization with numerous inpatient and outpatient services (Berndt, 2016). Detailed clinical, financial and employee information stored in a data warehouse enables health organizations to strategize, predict outcomes, generate patient reports, share data with insurance companies, and track their service feedback. Procedure of analyzing eases because instead of analyzing data from their separate locations, all data analysis occurs from a centralized location. As a result of its historical intelligence, data warehouse allows for advance reporting and analysis of several time-periods unlike transactional systems which lack historical data.
Customer interaction has also undergone enhancement with speedy data retrieving. Once information is in the data warehouse, a quick search finds whatever statistic one may require and can analyze it to assist with decisions regarding customers (West, 2016). Along with its advantages, data warehouse is limiting due to the fact that it can be costly in setting up and managing it. Data warehousing has come a long way since the concept began in the late 1980s and continues to progress filling loopholes that eases the versatility of analysts and decision makers. Challenges experienced while using a data warehouse can be solved using these steps; first, data modelling is essential in the integration and presentation repositories since it involves the design and implementation of data models.
Enhancing the skill of developing data models on a system-by-system basis is crucial in ensuring that same analysis has no repetition in overlapping areas and further analysis can done in the interlaces between the analyses. Dimensional modelling expertise is essential compared to relational data models (West, 2016). The distinction between logical data model and physical data model is crucial in enhancement of current skill sets. Companies can analyse detailed information and make strategic decisions related to its operations. Completely designed for analytical reporting, ad-hoc queries, decision making, and data mining. A data warehouse has numerous applications in the medical, finance, banking, education, marketing, and healthcare industries. Many industries are trying to upgrade and adopt the data warehouse technology to enjoy benefits such as consistent information, easy decision making, collection of organised information and integration of multiple systems in an organisation.
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