Data Mining and Value-Added Analysis

By McCue, Colleen; Stone, Emily S. et al. | The FBI Law Enforcement Bulletin, November 2003 | Go to article overview
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Data Mining and Value-Added Analysis


McCue, Colleen, Stone, Emily S., Gooch, Teresa P., The FBI Law Enforcement Bulletin


We are drowning in information but starved for knowledge.

--John Naisbitt

Law enforcement agencies, particularly in view of the current emphasis on terrorism, increasingly face the challenge of sorting through large amounts of information needed to help them make informed decisions and successfully fulfill their missions. At the same time, resources, particularly personnel, often dwindle. Described by one agency as the "volume challenge," (1) local, state, and federal agencies alike all struggle with an ever-increasing amount of information that far exceeds their ability to effectively analyze it in a timely fashion.

However, while these issues have surfaced, an extremely powerful tool has emerged from the business community. This tool, used by mortgage brokers to determine credit risk, local supermarkets to ascertain how to strategically stock their shelves, and Internet retailers to facilitate sales, also can benefit law enforcement personnel. Commonly known as data mining, this powerful tool can help investigators to effectively and efficiently perform such tasks as the analysis of crime and intelligence data. (2) Fortunately, because of recent developments in data mining, they do not have to possess technical proficiency to use this tool, only expertise in their respective subject matter.

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WHAT IS DATA MINING?

Data mining serves as an automated tool that uses multiple advanced computational techniques, including artificial intelligence (the use of computers to perform logical functions), to fully explore and characterize large data sets involving one or more data sources, identifying significant, recognizable patterns, trends, and relationships not easily detected through traditional analytical techniques alone. (3) This information then may help with various purposes, such as the prediction of future events or behaviors.

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Domain experts, or those with expertise in their respective fields, must determine if information obtained through data mining holds value. For example, a strong relationship between the time of day and a series of robberies would prove valuable to a law enforcement officer with expertise in the investigation pertaining to this information. On the other hand, if investigators, while reviewing historical homicide data, noticed that victims normally possessed lip balm, they would not, of course, associate lip balm or chapped lips with an increased risk for death.

WHY USE DATA MINING IN LAW ENFORCEMENT?

The staggering increase in the volume of information now flooding into the law enforcement community requires the use of more advanced analytical methods. Because data-mining software now proves userfriendly, personal-computer based, and, thus, affordable, law enforcement agencies at all levels can use it to help effectively handle this increased flow of data.

The law enforcement community can use data mining to effectively analyze information contained in many large data sets, even those involving written narratives (which represent a great deal of valuable law enforcement information). These may include calls for service data, crime or incident reports, witness statements, suspect interviews, tip information, telephone toll analysis, or Internet activity--almost any information that law enforcement professionals encounter in the course of their work. (4)

Not only can these data sets differ by type but they can originate from different sources, (5) potentially giving law enforcement agencies both a more complete informational base from which to draw conclusions and the ability to identify related information in separate databases or investigations. For example, this may prove valuable in the area of illegal narcotics enforcement. The law enforcement community frequently gathers information regarding markets, trends, and patterns, while medical and social services personnel store information concerning substance use and abuse on the individual level.

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