The mass surveillance programs of the National Security Agency (NSA) and its access of the servers of top technology companies is drawing much flak. A recent reportpoints out that the NSA is using US government-collected online data to predict events such as political protests, pandemics, and economic crises. The basic concept behind NSA's data mining operations is that the collected information can be analyzed to establish connections between people, which in turn, can generate investigative leads.
Benefits of Business Intelligence
Data Mining involves digging through and analyzing enormous sets of digital databases to extract useful information. Data collected can take up the form of photos, videos, word-processing files, financial records, social-media posts, phone-call records, results of science experiments, and even Skype chats. Mining information with great precision allows us to see patterns and model outcomes and to take actions that result in greater socioeconomic growth and progress.
When it comes to business, data tracking systems provide crucial business intelligence. Gathering business intelligence via data mining techniques can help keep companies informed about customer tastes, market trends, areas with potential for revenue generation and insights into competition. Web data mining of trade journals and newspapers can provide information that is crucial to the growth of your business. Such knowledge is important to retain existing customers, find new ones and prevent business setbacks. Entering a new market is less daunting with predictive analysis of its growth potential for defined criteria.
Similarly, banks use data mining to create profiles of customers that have the most profitable types of accounts. By finding other customers who match that profile, they can create a marketing campaign to target them. Banks also mine customer data to determine those with a proven record as poor credit risks and use these features to screen new customers and predict if they represent good or bad risks.
Data Mining Procedure
Data tagging is the very first step in data mining, as it enables algorithms to identify connections between data. This is related to metadata, i.e., the data about data -- the names and sizes of files on your computer. Tagged data helps data analysts in classifying and organizing information so that it can be searched and processed with ease.
The collected data is cleansed and formatted. Data cleansing helps identify and remove inaccurate and irrelevant information. Average values are created and the data model is built. Data mining experts evaluate the model and its parameters are tweaked until optimal values are achieved. The final model is then used extract business explanations. In the final stage, the data mining experts use the data mining results by exporting the results into data database tables or into other applications such as spreadsheets.
Identifying relevant information from big chunks of data can be like looking for a needle in a haystack. Different tools such as traditional data mining tools, dashboards, and text-mining tool are used to identify information in given data.
Using data mining techniques to understand and extrapolate data and information can reduce the chances of breach, cause proactive responses to potential business changes, and ensure that risks are managed in a more timely fashion. Once the data is mined, it must be stored systematically in databases for fast referral.
The best option for businesses that want to gain valuable insights from big data is to partner with a data entry company that uses advanced data mining software to uncover in-depth business intelligence that can improve the quality and reliability of business decision making.
This post first appeared on Business Process Outsourcing Company - Managed Out, please read the originial post: here
