Did you know that data produced doubles in volume every two years? Unstructured data(data without any format) makes up about 90% of this data. Such large amounts of data could hold key information for development of businesses, science and research, but since it's unorganized, it is unsearchable/unusable. This is where data mining is used. Data mining is the process of extracting useful information from a much larger set of raw data. The process involves discovering patterns and correlations within the large data sets. Data mining has its base in three interlinked fields: Statistics, Artificial Intelligence and Machine learning. Data mining requires data collection and warehousing and uses advanced mathematical algorithms to analyze data. Availability of superior processing power and Cloud computing has led to fast and efficient automated data analysis techniques. Some techniques used for data mining are clustering, decision trees, sequential pattern analysis.

Data mining is used in multiple businesses such as banks, manufacturers, retailers and insurance companies, to discover how best they can improve customer satisfaction, engagement and loyalty in order to maximize their revenue. Even research institutions use it to decide the line of research to pursue or strategies to be used for the analysis of their data. An example of usage of data mining is: retailers use their rewards card program data, to gain insight into the purchasing decisions of customers and about usefulness of particular sales. This data can then be used to deliver personalized coupons to customers. It can also be used by retailers to make informed decisions about everything from placement of product within the store to price optimization.

As we can see, data mining helps businesses with decision making, profitability and risk reduction. The data mining process would start with evaluation of the business requirements and available data and creation of data models. Later machine learning and mining techniques are used to extract usable information to make predictions/decisions.

Uses of Data Mining:

-- Insurance companies use data mining for fraud detection, competitive pricing, risk evaluation etc.
-- Biotechnology firms use data mining to analyze complex data and make research findings.
-- Telecom and technology industries create data models to determine pricing, profitability of products and for targeted advertising.
-- Retailers use data mining to predict customer demand, price products competitively and invest in advertising.
-- Manufacturing industries use data models to increase/decrease product supply based on forecasted demands to maximize profits.
-- In Medicine, data mining can be used to prescribe most effective treatments, preventative health screening for patients based on their age, gender and medical records etc.