Careful Application of Big Data Analytics to Prevent and Solve Crimes
Nov 05, 2015
The dark satirical Orwellian 1984-evoking movie Brazil graphically, if black-humorously, illustrates the consequences of bad data in law enforcement. There is a horrific, but truth-tinged scene, where a swatted fly landing in a printer caused it to change the name of an innocent cobbler Harry Buttle to a suspected terrorist, Harry Tuttle. This data entry error led to the brutal arrest and the subsequent unintentional death of the unfortunate Mr. Buttle.
The lesson from this grim incident has become relevant with the growing use of increasingly powerful Big Data and Analytics (BDA) tools to solve and more importantly, to prevent crimes. The Wall Street Journal recently published an excellent column “Big Data and Mass Shootings” that discusses Hitachi’s new Visualization Predictive Crime Analytics application. It also mentions West-owned Intrado’s commercial, public records and social data-scanning application, in partnership with LexisNexis and Motorola. Such applications are typically powered by artificial intelligence that use inference and pattern recognition, as outlined in a recent Frost &Sullivan| Stratecast report: “Artificial Intelligence: a Practical Assessment”.
These solutions tap into the fact that criminal acts rarely occur without notice. There is almost always a data trail of individuals, their actions, behaviors, including their uttered and written comments, as well as objects that are out of place that act as tell-tales. That trail is becoming an expressway what with the exponential rise of information governments and organizations collect on individuals, including their voice, text conversations and video, in addition to an individual’s’ social media comments.
Detecting and analyzing this information appropriately, and in a timely manner, can save innocent lives and prevent untold destruction. But so does having the right information and uncorrupted information i.e. Brazil. Bottom line: authorities can ill-afford the consequences of getting it wrong.
The same matters and the criticality of preventing them also apply to healthcare, for example misdiagnoses and wrong prescriptions. Yet the impacts of data errors by customer service organizations are also painful to customers, like in billing, bank and credit card balances, loan and mortgage information, along with lost orders and deliveries. These unfortunate events are often costly and nightmarish to correct and ameliorate.
However, sourcing and maintaining correct data is difficult, as customer contact organizations are realizing. Data often rests in departmental and product line silos that must be connected, along with their database and data handling packages, which can vary across organizations. Data also must be standardized and scrubbed, in order to be useful and valid for all users. The data itself must be kept and accessed securely to prevent access by sophisticated criminals. Finally, there then needs to be systemized data validation, cross-checks, and cross references.
The data issues are summed up in another Frost & Sullivan|Stratecast report: “Big Data & Analytics: Risk, Reward and Return on Investment”. The analysis points out that “BDA requires clean, complete and secure data. This is a scarce commodity in most organizations of any size, where system, network, and database administrators struggle to keep multiple legacy systems and databases up-to-date.”
Equally critical, there are legal hurdles to cross with BDA. There are fine lines on which information to use, what to seek with warrants, and when authorities can act on them to prevent civil liberties infringements, or worse, to avoid others from having similar fates that befell Brazil’s Mr. Buttle.
Finally, and most importantly, organizations must have individuals who can properly analyze and interpret the information, including the ability to detect and the authority to act when they see data and other analyses that are not quite right. While AI-driven BDA solutions have become much more powerful and accurate, automating routine tasks, Stratecast points out, they cannot know whether their analyses “match reality or desired results”.
Therefore, these systems require truly human intelligence to draw the right conclusions from them, aggregate these inputs with other knowledge sources and analyses, and to ultimately decide on the next best course of actions.
Brendan Read is Senior Industry Analyst with over 25 years’ experience covering business, communications, staffing, and technology. He has worked in, prepared reports, and blogged on a wide range of topics including customer contact, CX, CRM, IoT, social media, supply chain, and BC/DR. He also has backgrounds in construction, manufacturing, materials, resource extraction, site selection, and transportation. He examines the broad economic, environmental, innovation, political, and social mega trends, and their impacts on businesses, markets, and society.