A recent report on Toward Freedom points out that the National Security Agency (NSA) is using data mining techniques to predict events such as political protests, pandemics, and economic crises. Experts are analyzing the US government-collected online data to establish connections between people and generate investigative leads. They are employing automated analytics programs using open source information such as Facebook, Tweets, Google searches, and other publicly-accessible data to predict events and be prepared to prevent disasters.
Data mining plays an important role in helping companies predict business trends and its importance continues to increase. Companies are using data collected from different sources to gain business insight that will increase their Return on Investment (ROI).
Data mining is proving vital in areas such as healthcare, education, engineering, and customer relations management. Similarly for identifying customer behavior, customer tastes, market trends, customer feedback, etc should be thoroughly analyzed.
Mining valuable information from huge volumes of data is like exploring a mountain for a vein of valuable ore. It involves sifting through immense amount of data and probing it to extract valuable ideas. The specific uses of data mining in business include:
- Segmentation of the market – Identifying the common characteristics of customers that buy the same products from a company.
- Customer turnover – Predicting which customers may leave a company and buy a competitor’s product or service.
- Fraud detection – Identify transactions which have a high chance of being fraudulent.
- Direct marketing – Discovering which prospects should be feature in a mailing list to get the highest response rate.
- Interactive marketing – Forecast what kind of information visitors to a website are most interested in seeing.
- Buying behavior – Find out what products or services are commonly purchased together.
- Trend analysis – Look at the differences between this month’s and last month’s typical customer.
Steps in the Data Mining Process
- Document conversion: Data collected for decision making can take up the form of documents, graphical formats, video, records, and so on. Document conversion allows the data to be converted to the appropriate format is essential for analysis. The data can be easily retrieved from the database for decision making.
- Data tagging: Data tagging is the process that enables algorithms to identify connections between data. It is closely associated with metadata, the data about data — the names and sizes of files on your computer. Searching and processing is easy in case of tagged data for data analysts to complete their task with flexibility.
- Data Cleansing: The final step in data mining process is cleansing and formatting of data. Inaccurate and irrelevant information available in raw data is identified and removed through this process. As the final process, experts store the cleansed data in to database tables or into other applications such as spreadsheets.
Businesses that need to mine huge volumes of data can benefit from the data mining services offered by professional outsourcing companies. Using advanced data mining software, these companies help their clients leverage the information at their disposal to maximize ROI.