Analyzing big data is the process of examining large data sets in order to uncover hidden patterns, show changes over time, and confirm or challenge theories.

Most often associated with corporate decision-making, data analytics actually has applications well beyond the for-profit world of business. In fact, big data is making significant impacts throughout many industries: leading cancer patients to full recovery, increasing the reach of disaster relief efforts, and much more.

Here are four exciting examples of big data and analytics in action across a spectrum of industries.

 

Big Data in Health Care

Predictive analysis of big data within the health care industry can improve lives. This analysis can be used to update health care protocols, often improving outcomes across whole populations. Dr. John Halamka, one of the foremost health care CIOs in the world, shares a personal situation demonstrating how data and analytics can benefit patients and catalyze positive changes in health care delivery.

Halamka’s wife, of Asian descent, was diagnosed with stage IIIA breast cancer in December 2011. He and his team queried data from all the Harvard hospitals on treatment and outcomes for the last 10,000 Asian females with a tumor similar to his wife’s. Data revealed a medication that would be most effective for her specific case. Halamka credits his wife’s full recovery to the findings that he was able to draw from this analysis.

 

Big Data in Disasters

The April 2015 earthquake in Nepal killed nearly 9,000 people and injured 22,000. Analytics technology developed at North Carolina-based SAS had a huge impact in disaster relief efforts in Nepal. SAS CEO Jim Goodnight shares how two of the company’s global development projects intersected with the relief efforts.

First, SAS analyzed shelter data provided by the International Organization for Migration (IOM)   in order to help better allocate resources. The IOM used SAS® Visual Analytics to identify high-risk shelters based on several factors such as solid waste disposal problems, high population of infants and children, and overcrowding. SAS based their analytics research on work they did with IOM following Typhoon Haiyan in the Philippines.

A second project, SAS Visual Analytics for UN Comtrade, which makes decades worth of international trade data available using SAS software, also helped Nepal relief efforts by allowing IOM to quickly locate the top exporters of materials needed in the recovery efforts.

 

Big Data in Child Welfare

In the same way a 19th-century English physician collected and used data to stem the tide of a massive cholera outbreak in a neighborhood in London, modern day analysts can use big data to similarly impact and improve communities and the lives of people in them.

Dr. John Snow was skeptical of the theory that foul air was the cause of a significant cholera outbreak. He collected interview data on those infected and identified patterns from the data he collected, concluding that the water pump on the street was the source of the outbreak. The pump was turned off and the outbreak stopped.

Joshua Verville, of Hewlett Packard Enterprise – US Public Sector, uses this example to demonstrate how big data analytics should be used to collect, store, manage, and analyze child welfare data. Human service organizations can uncover patterns in homes that lead to neglect, abuse, and fatalities among children; reduce instances of child abuse and neglect, and understand what services work best to strengthen families and prevent future abuse. Big data analytics can provide a significant opportunity for human services to discover and analyze insights from across the child welfare system.

 

Big Data in Online and Physical Security

Data analytics is also reducing fraud in banking, government, and corporations, bolstering cybersecurity initiatives across industries and enhancing law enforcement and national security efforts. Data analytics can often both increase data security concerns in the financial sector and also provide organizations with information to better protect their data by:

  • Identifying suspicious activity or behavior.
  • Taking steps to prevent attacks before they happen.
  • Reducing fraud with real-time monitoring of user behavior and network activity.
  • Triggering alarms when analytics picks up on irregular activity.

In terms of physical security, cities, mayors, police chiefs, and other local leaders around the country are using data analytics to help them understand and address gun violence. For example, Chicago’s mayor analyzed the sources of illegal guns recovered from Chicago’s streets. With this data, he was able to work with gun dealers and local law enforcement to develop a local ordinance for responsible gun sales that requires local shops to comply with several safety measures, including background checks for employees and regular inspections.

 

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