Data Storage

• Preserving data is an important part of data management. Simple data storage computer software ensures that critical administrative, financial and research data is protected from data-corruption risks. Automated back-up is part of data life-cycle management systems, which generally occurs at sequential time intervals.
  
  

Data Integration

• Integrating duplicate data files and disparate legacy system databases can reduce business operating costs. Performing regular data integration ensures closer control over obsolete data. The integrity of data systems is of particular importance in industries such as telecommunications, insurance and utilities. Here, customer databases are the central means of generating scores of business-to-customer accounting reports and communications that must be accurate.
  
  

Data Mining

• Data mining is the process of extracting data from large databases with the aim of finding data patterns that can be useful to business goals. Data mining information can identify loyal customers, and this information can be used in customer loyalty marketing strategies. It also may reveal sales patterns that signal market trends.
  
  Dormant computer data generally is the source of most data mining, which may exist for a number of reasons, such as a company's decentralized database management. Data mining, also called knowledge or data discovery, can become the basis for multichannel distribution strategies and customer relationship management initiatives.
  
  

Data Warehousing

• Data warehousing is a centralized reporting database. It is a business intelligence tool developed by extracting data from various operational systems, removing data redundancies and formatting missing data fields in order to develop a central data reporting database. Its capability to retrieve and manage data from multiple sources is used to perform activities such as data mining, market research, online analytical processing and statistical modeling.