A process used by companies to turn raw data into useful information. By using software to look for patterns in large batches of data, businesses can learn more about their customers and develop more effective marketing strategies as well as increase sales and decrease costs. Data mining depends on effective data collection and warehousing as well as computer processing.
A major goal of data mining is to discover previously unknown relationships among the data, especially when the data come from different databases. Businesses can use these new relationships to develop new advertising campaigns or make predictions about how well a product will sell. Governments also use these techniques to discern illegal or embargoed activities by individuals, associations, and other governments.
Data mining is primarily used today by companies with a strong consumer focus - retail, financial, communication, and marketing organizations. It enables these companies to determine relationships among "internal" factors such as price, product positioning, or staff skills, and "external" factors such as economic indicators, competition, and customer demographics. And, it enables them to determine the impact on sales, customer satisfaction, and corporate profits. Finally, it enables them to "drill down" into summary information to view detail transactional data.
With data mining, a retailer could use point-of-sale records of customer purchases to send targeted promotions based on an individual's purchase history. By mining demographic data from comment or warranty cards, the retailer could develop products and promotions to appeal to specific customer segments.
Data management is the development and execution of architectures, policies, practices and procedures in order to manage the information lifecycle needs of an enterprise in an effective manner. Data life cycle management is a policy-based approach to managing the flow of an information system's data throughout its life cycle: from creation and initial storage to the time when it becomes obsolete and is deleted. Several vendors offer DLM products but effective data management involves well-thought-out procedures and adherence to best practices as well as applications.
There are various approaches to data management. Master data management for example, is a comprehensive method of enabling an enterprise to link all of its critical data to one file, called a master file, that provides a common point of reference.The effective management of corporate data has grown in importance as businesses are subject to an increasing number of compliance regulations. Furthermore, the sheer volume of data that must be managed by organizations has increased so markedly that it is sometimes referred to as big data.