Data Mining: From Moore’s Law to One Sale a Day

Today the internet is more customized than it ever has been before. This is largely because of data mining, which involves using patterns and records of how you use the internet, to anticipate how you will continue to use the internet. This is an application of data mining, however; more broadly, the term refers to how to analyze data to cut costs or increase revenue.

While the term data mining is new, the practice is not. Due to Moore’s Law, which states that processing power and data storage double every 18 months, over the past five years, it has become significantly easier to access vast stores of data. People are also continuing to use the internet and explore the web at an exponential rate so that the effect of data mining by 2020 will mean that roughly five billion of the world’s seven and a half billion people will be affected. After about 2020, integrate circuits will be so advanced and tiny, that many predict that computers will be nanorioned, with billboard-size pieces of information directly inserted into our brains. The science fiction stories that Hollywood used to root us into the plotlines of our favorite films no longer hold up reality, as the possibilities of computing power are so advanced that scientists and researchers are struggling to keep up with it.

Data mining has more practical examples, too. The products you’ve bought off Amazon, for example, are analyzed by data miners at the company’s warehouses and retail locations around the world. This type of data mining is addressed in the broader area of e-commerce. Products can be identified by category and retailers can be fed back to the data mining program that generates an estimated shipping total. In the financial industry, data mining can identify complex financial indicators, such as country purchases versus total GDP. This ability to generate information from enormous data sources comes in handy when making risk decisions for buying or leasing a credit card, for example.

The definition of data mining is a natural resource that can be cheaply and incrementally mined for data. Data mining can address many non-volatile memory requirements, which are more flexible and variable than strict memory devices. Flexible storage and memory access rules are an important part of the new age computers, not to mention the sheer tons of data that the Internet generates on a daily basis. Data mining makes real sense for the situations where strict reliability is essential.

Data mining brings to the forefront the importance of accurate and precise data analysis. The computer may be powerful, but it is also malfunctions if the data is incorrect. Data mining brings to the forefront the consideration of data quality in large databases.

Correct calculation of data, that is, retrieving data with the correct standards of accuracy and reliability, is an important part of the complex equation of data mining. In particular, data mining can extract data that have been lost because of poor estimation.

Advanced data retrieval systems address these concerns in different ways. Sometimes the non-availability of data due to bad data recording or retrieval techniques can be eliminated. These systems record each stage of the data-taking process, including data collection, quality control, data refinement, data loading and data verification. The ability to repeat the steps usually comes with evaluation of the data to obtain an understanding of how the data was resolved.

Data mining programs also account for differences in the way people work. These may be differences in language accessibility, time availability, practices and underlying modes of operation. While this last example is specific to the subset of data mining analysis that pertains to Web search, data mining programs can be applied to other areas such as insurance data processing, instant insurance marketplaces, etc. Data mining programs are increasingly being used in business, law and even social science research. The future of data mining is wide open.

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