For many businesses, corralling the insight buried deep in big data can mean retaining customers, adding new customers or boosting profits.
However, data analysis is also being tapped to ward off fatal fires and peek into a person’s blood stream – big data applications that have the potential to save lives.
We’ve written about humanitarian agencies using big data and data analysis for the greater good in international disasters, but the City of New York is applying analytics at the local level to boost housing inspector efficiency and reduce the potential for fatal fires.
At a recent data science conference in New York, Michael Flowers, director of New York City’s Policy and Strategic Planning Analytics Team, describes how his “skunkworks” team of analysts handles one of the challenge facing the city’s housing inspectors.
The problem is the housing office receives 20,000 complaints a year about buildings that are overcrowded but there are only 200 inspectors to investigate the complaints.
So Flowers’ team analyzes data from previous sites to identify the buildings that are likely to have “catastrophic outcomes” like serious fires.
“In examining data, they discovered things like age of the building and financial condition of the structure had a strong correlation with likelihood of serious problems,” according to Computerworld. “For example, a tax lien on a building made it [nine] times more likely to sustain a serious fire.”
Ranking complaints by the danger probability has boosted housing inspector efficiency. The inspectors now find problems at 80% of their site visits to houses occupied by more people than allowed by law. Before the days of data analysis, inspectors only uncovered problems at 13% of these site visits.
This is significant because the fire department is 15 times more likely to have an injury or death in some of the illegally converted buildings compared to the buildings that follow codes, Flowers adds.
While the city of New York is aiming to reduce the risks of fires in its neighborhoods, companies are tapping into the power of big data and data analysis to try to marry data sets obtained by sensors worn on or in the body – or data that patients report themselves – to better tailor healthcare, the New York Times reports.
We’ve mentioned that several companies are developing sensors – products that can be attached to clothing or shoes, worn as tattoos or installed in the body – to capture a wide variety of data about the human body including vital signs, electrolyte levels and brain activity. This data can then be integrated with other data, like demographic and age data to uncover larger health patterns and trends in the population.
Linda Avey, who co-founded the personal genetics company 23andMe is now working on a start-up called Curious that she hopes will help spur the notion of an open source model of medicine where companies can share data to boost medical research.
Curious will allow people with conditions that are hard to diagnose or treat – like lupus or fibromyalgia – to share data about themselves that’s derived from devices but also self-reported data like what the person ate for dinner or how often he was in pain. Collectively, this data could lead to evidence about how behavior and biology contribute to these conditions.
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