Big Data Helps to Fight California Wildfires
Mary K. Pratt, 2/23/15
Summary by Jiayu Peng
Crews responding to California wildfires can now access aerial photos, stats on ground moisture and wind, and other data to help them plan how best to attack the fire.
Fires shift character as they grow and recede, and the data helps firefighters and their supporting logistics teams respond as effectively as possible. Recently, data scientists in California Natural Resources Agency have made the data available via a private cloud. With the help of this new system, firefighters can now use smartphones and laptops to access the data in the field. The cloud, along with the apps and portals to access it, allows them to have logistical support on the ground in real time.
Other state agencies and citizens can also get current and historical data from the agency's private cloud. For example, decades of data on water flows, soil erosion and climate change help manage water during droughts. A farmer can gather rainfall and groundwater statistics to help make planting decisions.
Storage technology is critical for the new cloud. Prior to this system, data storage capacity was limited, and therefore only current information was available and historical data was kept on tapes. But now, all information is no longer locked away.
Bringing Big Data to the Fight Against Benefits Fraud
Natasha Singer, 2/20/15
Summary by Jiayu Peng
Financial companies have long been using computer-modeling and data-mining software to inhibit fraud. Nowadays, state and local government agencies are turning to these services.
For example, the New York City Human Resources Administration once tried a new way to root out fraud among people receiving government benefits. Data detectives began running benefit recipients through a computerized pattern-recognition system. They discovered that the behavior of a small percentage of people stood out.
One of those outliers was Ms. Raghunandan. Data scientists pointed out that her case was unusual, compared with families of similar size and income, which typically received multiple benefits like health coverage, food stamps and cash assistance — but Ms. Raghunandan had applied only for Medicaid for herself and her children.
Later investigations confirmed the suspicion by data scientists. According to agency officials, her family had underreported many assets, including a business owned by her husband, three residential properties and joint bank accounts with more than $100,000, etc.
Officials say that this kind of multisource data analysis has helped them uncover more benefit abuse with less effort. To keep up with the increasingly complicated nature of benefits fraud, more and more State and local government agencies are all arming themselves with data science techniques.
The Inside Man: NBA Analytics
Summary by Jiayu Peng
For seven years, Ben Alamar has been working with NBA teams, as a consultant on how to utilize data in their decision making. Recently he published an article in ESPN The magazine, in which he reviewed how data analytics have grown in the league, and here is a summary of his main points.
Seven years ago when Ben started his consulting career, NBA teams reached a crucial turning point. They had been overwhelmed by the avalanche of data, produced by devices that record the whole process of every game. As a result, the number of analysts employed by NBA teams grew significantly.
In the beginning NBA data scientists were doing relatively simple analyses, for example, anyone with advanced spreadsheet skills could probably add value to a front office. But that was not enough. Modern devices, such as SportVU cameras, capture the positions and moves in the court. Consequently, the amount as well as the complexity of data grew tremendously. Therefore, it requires a much higher level of data-analysis skill. Nowadays, deep statistical programming skills, along with advanced computer science knowledge, are needed to create value. These are skills for which companies such as Google and Facebook pay quite handsomely.
For all the challenges, data analysis becomes a big part of the NBA's future. More and more teams are starting to figure it out, and some are even raising salaries for data scientists.
Not Always Big Data -- Small Data Is Driving The Internet Of Things
Summary by Jiayu Peng
In a recent article by Mike Kavis, he argues that we shall not overuse big data in the Internet of Things (IoT), and instead, "small data" are of more practical use.
Here is a summary of that article:
When people talk about the Internet of Things (IoT) they tend to think about big data technologies like Hadoop where petabyte size datasets are stored and analyzed. But in fact, many IoT use cases only require "small data".
Small data is a dataset that contains very specific attributes; it is used to determine current states and conditions. According to the author, small data is important, since they can trigger events based on what is happening now. On the other hand, big data tells us about patterns, mechanisms or trends.
A simple example is the use of smart labels on medicine bottles. Small data can be used to determine where the medicine is located, its remaining shelf life, if the seal of the bottle has been broken, and the current temperature conditions in an effort to prevent spoilage. Big data can be used to look at this information over time to examine root cause analysis of why drugs are expiring or spoiling.
In conclusion, big data is not a requirement for all IoT use cases. In many instances, knowing the current state of a handful of attributes is all that is required to trigger a desired event; small data knows what a tracked object is doing.
Should Big Data be Applied to Managing Human Resources?
Dianne Buckner, 2/26/15
Summary by Jiayu Peng
A growing number of human resources executives are starting to dig deep into computerized statistical data on employees, to make decisions regarding salaries, promotions, and even benefits programs. It's a trend that excites some and worries others.
Andrew Martin, who oversees human resources at the Joey chain of restaurants, believes that data analytics is great for companies. His HR departments track data such as absenteeism, salaries, sales figures, and measure the performance reviews from colleagues.
Joey restaurant chain has used data to design a more fair way to pay its chefs and general managers. The chain measures a variety of indicators for its chefs and general managers. Restaurant cleanliness, wait times, and revenue growth, along with assessments given by other employees, are used to determine a ranking.
However, Business ethics professor Chris MacDonald is on the other side of the issue, questioning the use of statistics for decisions about people's place and success in the workplace. In his opinion, although statistics can help, we cannot rely on a number to sum up the whole employee-employer relationship.
Some argue that quantitative measurements for people are often unfair, since analytics present a raft of limitations. For example, the measure doesn't exist for some characteristics such as initiative or enthusiasm. Also, it is hard to remove bias any time we get a person interpreting the data.
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