Data processes. To ensure quality of data

Data warehouse is a collection of data that helps in decision making. It is one of the components of business intelligence. It is a type of database which is generally used in processing queries and analysis and contains details of previous transaction or processes. To ensure quality of data it usually goes through various steps of data cleaning. Relational database is used for online transactions in which data is stored in previously defined categories which includes insertion, update and deletion. Data warehouse is used for analytical processing where as relational database is used for transactional processing. When it comes to query analysis, data warehouse shows high performance and relational database has a low performance.  In data warehouse, online Analytical processing (OLAP) is used to which is used to improve response time and helps in analyzing better, which is done by denormalization of the data. Where in online data transactional processing (OLTP) the data is highly normalized which helps in quick response time and provides a lot of storage.  Both operational data and decision support data have different functions from each other. Operational data is used to store the data in RDBMS and supports the transactions which takes place in the business. Operational data is more real time whereas decision support data shows processes or transaction which have already occurred.  Operational data is usually used in places to keep up with the simple operations such as keeping track of every item sold in a store. Decision support data require denormalization of data and are useful for place where data there are not a lot of data changes done on daily basis. Since decision support data requires high speed it does not include all the details of every transaction but at the same time it contains huge amount of data. It is highly likely that it contains many duplications and the data is in denormalization form.  Operational data usually has a short time span whereas decision support has a longer time span. In decision support data, data can be analyzed at different levels staring from an overall summary to details of each transaction whereas operational data tends to focus on individual transaction.  One example where database could be used to support decision making could be company such as amazon which requires large database for its inventory and for its various products. They also must keep up with the large amount of their customer’s information which includes billing details, shipping date, payment method etc. Another example could be it can also be useful in companies where decision making is required to take further actions or future decisions for the company or using queries to analyze the data. This can be based on several details. Last example of this could be that, it is helpful for organizations who need to keep track of their things which include keeping track of assets, liabilities, sales and profits.  Data mining is a subset of business intelligence used by various companies these days. Data warehouses and data mining these days are used by companies along with sophisticated software to predict the market. A good example could be data mining used by large organization such as a credit company to predict as to what type of advertisement a client would be interested in or which areas to focus more from where we can a potential client. Another example is that is also helpful in keeping up with the market and latest trends which help companies to outgrow positively and plan for future. Last example of data mining could be that it is used by companies to analyze the change whether there is an overall increase or decline in their market value or a product.  

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