Data Warehousing and data mining Research Paper

Data Warehousing and data mining - Research Paper Example Moreover, the increased data redundancy is further exacerbating the situation and the conversion of data into information, information into knowledge and knowledge in to power is very slow. This redundant and dubious information resource is of no good for managers who have to take quick decisions. Managers require precise information that represents and accounts for every aspect of a business. It is the responsibility of a decision support system to answer any query related to information stored in the system and to generate some nontrivial information patterns. These patterns can impart the required business intelligence and can leverage certain decisions. Data Warehouse There is no consensus on the definition of a data warehouse. In simplest terms, a data warehouse is a set of multiple applications, concepts, methodologies, tool and techniques to gain some knowledge based on historical data that may come from multiple systems and sources to assist managers in decision-making proces s. Vercellis (2009) defines â€Å"A data warehouse is the foremost repository for the data available for developing business intelligence architectures and decision support systems.† However, it is not a comprehensive definition and Vercellis (2009) himself admits, â€Å"The term data warehousing indicates the whole set of interrelated activities involved in designing, implementing and using a data warehouse.† Characteristics of a Data Warehouse There are few important characteristics of a data warehouse. These characteristics define the efficiency and effectiveness of the system and determine its qualification being a data warehouse. Most important characteristic of a data warehouse is the strength of its repository, which depends on the availability of sufficient historical and current data. The exact amount of historical and current data is determined by the domain where the data warehouse is being deployed. Secondly, a data warehouse has to provide ad-hoc access to information sources. This means there are only fewer fixed SQL queries and most of the inferences and intelligence is gathered through dynamic, on-the-fly queries. A data warehouse employs several tools like data modeling, star schema, data mining etc. to ensure ad-hoc access to its resources. Thirdly, a data warehouse is designed for decision makers and knowledge workers. However, these people are not bond to be information technology experts. Because strategic decisions are more concerned with customer trends, behaviors and market forces knowledge workers are not interested in individual records of a customer, product or service rather these users require an all inclusive big picture that may help to make long term strategic decisions and short term operational decisions. How it is different? A data warehouse is essentially different from Online Transaction Process (OLTP) and Enterprise Resource Planning (ERP), Customer Resource Management (CRM) systems. Because these systems are not designed and engineered for decision-making and knowledge discovery, they do not have huge historical data. Secondly, they record live transactions of the business and keep records of customers, products and services updated. On the contrary, a data warehouse does not record live trans