Topic 61

Data mining/data matching by Xiao Xiao Li

Presentation of the Issue

This fast growing Age of Information Technology provided the stimulus for the development of data mining and data matching, which helped firms and government agencies tremendously. However, this technology is slowly growing out of control. Firms have been data mining and data matching for years, but improvements in technology and techniques have taken the field to a new plateau where even its advocates are unsure where the road may lead. For example, in a privacy breach at the data collection giant ChoicePoint, con artists gained access to the Social Security numbers, addresses and other personal data of nearly 145,000 people.

This event clearly exposed the shortcomings of the laws governing the data mining/matching industry and consumer privacy. However, others claim that each Social Security Number is unique to every respondent and is therefore useless to the data mining industries. They also proclaim that data mining and matching is crucial to a market economy because it drives competition among the firms. This leads to a controversial debate between the subject of technology’s positive contributions to society and the possible issues it’s likely to cause. The invasion of privacy and identity theft will be perfect examples of the invoking issues created by this new technology. Instead of getting better, such social issues are becoming more severe each day with the ever-increasing technological progress.

IT Background

Data mining/matching is currently an issue to many people. But just couple of years back, few even heard of the term data mining/matching. Its development traces back to three central concepts, which are classical statistics, artificial intelligence, and machine learning. Classical statistics is definitely the oldest of the three. With no statistics, there would simply be no data mining/matching because statistics are the foundation of most technologies on which data mining/matching is built.

As the second stage of data mining development, Artificial intelligence attempts to apply human-thought-like processing to statistical problems via a ‘magical’ machine called computer. Although it’s a significant development, but data mining requires intense processing power which only made it practical years later when computers finally started to offer more power at lower prices. The last member of the family line of data mining is machine learning, which allows individuals to take advantage of the ever-improving performance offered by computers.

Machine learning enable computer programmers to learn about the data they study, to make different decisions base on the qualities of the studied data, and to use statistics for fundamental concepts. Therefore, data mining is finding increasing acceptance in science and business areas which need to analyze large amounts of data to discover trends and patterns. But such actions also results in problems such as identity theft and the invasion of privacy, thus creating a major controversy.


The Impact of the Issue

Data mining/matching can be extremely useful in many cases, but the lack of a uniform set of rules is believed to be the central problem. The consumer data protection requirements are often conflicting, they differ from state to state, country to country, and are too industry-specific.

The Government Accounting Office (GAO) recently studied the use of personal information in government data mining projects. And their report concludes that none of the five agencies they analyzed: the Agriculture Department, the FBI, the Internal Revenue Service, the Small Business Administration and the State Department had fully complied with the Office of Management and Budget (OMB) guidance on privacy measures.

These agencies did not take the adequate steps to ensure information security, and this might lead to the possibility of this information ending up in the hands of unwanted individuals. Such events have huge economical, legal and political impacts on the society as a whole since data mining/matching is closely related and broadly used in all of those areas.

The recent ChoicePoint situation for example, is a huge indication of the vulnerability of the legal system and the lack of protection of personal data. Existing laws merely nibble at various corners of the data trade and are no longer sufficient when thieves can steal data not just from a few victims at a time, but from thousands of people with vast, digitized efficiency. Although modern technology of data mining/matching possesses unlimited marketing values, but the social impacts it creates must be addressed quickly and efficiently.

Solutions to Problems Arising from the Issue

As a result of the recent signs of vulnerability of the Privacy Laws, Federal regulators and lawmakers have started calling for an updating of the rules. These laws shall, and will not abolish data mining/matching altogether due to its recognized contributions to the market. Instead, they will be modified to ensure the safe-keeping and flow of data during various transactions. All firms should hire quality data managers who will devour themselves sorely to the area of data mining.

Under such attention and care, any major economical or political chaos will be prevented. Governments around the world have to sign an international pact in order to have a uniform set of laws regarding cross-border data mining. This is crucial due to the global ness of today’s world, and yet hard to achieve as the world is never in perfect harmony. Besides government and firm’s actions, consumers themselves should also be fully alert at all times. More efficient security devices have to be invented and put into practical use, with a secret code that will be constantly changed by its user. If the governments, firms and individuals can cooperate in harmony and practice all the above suggestions, our world will continue to flourish with the help of data mining without any concerns over social issues.