Meet the Next Great Crime-Stopping Hero: Data Integration

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    It’s easy to think of integration as an IT problem, but two recent stories exemplify how integration—or lack thereof—can be a major strategic turning point.

    The first is a story of crime and integration. In a recent Information Week commentary, Accenture Consultants Jody Weis and Wai-Ming Yu examine the very real crime solving problems created by local, state and national data silos.

    As a former cop reporter, I’m very aware that police investigations never play out like they do on TV. I shuffled through piles of paperwork every week to find copy, so I’ve long known that TV misrepresents the realities of modern police work.

    Still, most people tend to believe police can just scan in a fingerprint and match it to a criminal six states away. Victims of minor crimes are frustrated when they learn police don’t even collect fingerprints for most crimes, because it’s a pretty useless practice for most petty crimes.

    That’s just one example of the discrepancies between what we think police do and what police actually do.

    “On television, this looks like an investigator holding a mobile phone and tapping into a national database that spits out the name and location of the suspect,” write Yu and Weis. “In reality, officers often struggle to navigate a patchwork system of data and information that is inaccessible across city, county, and state lines.”

    This is no small integration problem. We’re talking about more than 17,000 state and local law enforcement agencies operating siloed IT systems, Yu and Weis point out. The very real and troubling result is that sometimes criminals go free because police can’t access information from other law enforcement bodies.

    “The issue has resulted in police forces that are challenged to identify and arrest offenders and manage cases properly, because the information they need resides in another state or county.”

    On the face of it, this problem could be solved by technology. But issues of data control (let me introduce you to a thing called data governance), security and expense stand in the way.

    Yu and Weis argue that these issues are just myth-understandings police have about technology and data integration. “When you look at it from a budgetary and public safety perspective, the costs of not embracing multi-tenancy information systems are far higher than the costs of implementation,” they write.

    I’ve talked with detectives and know the time and expense some of them go to track down evidence that should have been easily available from another agency. So, I tend to agree with them: It’s time to integrate police data. The stakes in lives and property are too high to let misconceptions about technology stand in the way of progress.

    A second example of integration as a business enabler comes from KM World Magazine, and features a watershed management program by the Southern Ontario Water Consortium (SOWC).

    Managing a watershed is no simple task, but it can be critical to environmental and health concerns. For instance, where I live, the city runs a combined sewage/water run-off system, which means water from our roads runs into the sewage and sometimes, both overflow into the rivers and streams. Hey, it made sense in 1860.

    The SOWC uses 120 sensors to monitor the 80 square kilometers of the Grand River watershed in Southern Ontario. Those sensors collect data about rain, snowfall, flow rates and water quality.

    IBM worked with the consortium to build a data integration platform for gathering the 600 data points per hour.  The system makes the data usable, allowing the SOWC to monitor and make decisions about the watershed in real time.

    Beyond showing how critical data integration can be, it’s a great mini-case study in Big Data and the Internet of Things. You can read more on the SOWC’s website.

    Loraine Lawson
    Loraine Lawson
    Loraine Lawson is a freelance writer specializing in technology and business issues, including integration, health care IT, cloud and Big Data.

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