Data Mining attempts to discover patterns in huge data sets that help companies

Data Mining is a field at the intersection of statistics and computer science. Moreover, it is the process which attempts to discover patterns in huge sets of data. It uses methodologies as the intersection of artificial intelligence, statistics, machine learning and database systems. The data mining's overall goal is to extract information from a set of data and turn it into a comprehensible structure for further use. Besides the raw analysis step, it also involves data management and database aspects, model and inference considerations, processing of data, complexity considerations, interesting metrics and a whole lot more.

The process derives its name from the sameness between searching for valuable business information in a huge database. For the most part, the process tells about very big and complex data sets, the types of information that will be readily obvious regarding small and simple things. Discovering information from data takes a couple of main forms, which are the description and prediction. Data mining is a new powerful technology with great potential to help organizations focus on the most important data in their warehouses. The tools predict future behaviors and trends, enabling businesses to make knowledge-driven, proactive decisions. The prospective, automated analyses offered by the method move beyond the analyses of events in the past provided by retrospective tools that are typical of decision support systems. Furthermore, the data mining tools could answer business queries that traditionally were time-consuming to solve.

Sometimes called data or knowledge discovery, the process is that of analyzing information from various perspectives and then summarizing into useful information that could be used to boost revenue, cut costs and even both. The data mining software is one of several analytical tools to analyze information. Technically, it is the procedure of finding patterns or correlations among several fields in big relational databases.

Although the terms are relatively new, the technology however is not. Organizations and businesses used powerful computers for sifting around volumes of supermarket scanner data and then analyzing market research reports that have been around for years. Nonetheless, continuous innovations in disk storage, computer processing power and statistical software considerably boosts the accuracy of analysis and at the same time driving the cost down.

Data mining primarily is used today by organizations with a strong focus on consumers, such as financial, retail, communication and marketing companies. It enables these firms to determine relationships among internal factors like price, staff skills, and product positioning and external factors like economic competition and indicators and customer demographics. Furthermore, it enables them to find out the impact on sales, corporate profits and of course customer satisfaction. It allows organizations to drill down into summary information to check out detail transactional data. With data mining, a retailer can use point-of-sale records of the client purchase to send to targeted promotions based on the person's purchase history. Through mining demographic data from warranty cards or comment, the retailer can develop promotions and products to attract to specific client segments. While large-scale IT has been evolving separate transaction and analytical systems, mining of data provides the link between the two.

Ritesh Mehta is the Sales Director at TatvaSoft Australia, a Software & mobile app development company. For Over 15 years, he has been professionally active in financial management, software development.