Gas TurbineLarge scale equipment for power generation, manufacturing, mining, and similarly sized functions are structurally important to the global economy. They turn raw materials into the energy and other products that help keep the economy running.

Consider the gas-turbine electric plant. These installations can involve multiple instances of large scale gas turbines like those manufactured by General Electric and Siemens, and they can supply power for thousands of homes and many jobs. Keeping these turbines running, continuing to turn gas into electricity, requires precise manufacturing. Bearings, blades, and shafts must be perfectly balanced in order for continued power generation, but strict maintenance schedules for addressing corrosion, fatigue, and wear are also required.

As precisely as these giant machines are made, the extreme conditions of highly compressed gas and the continuous runtime requirements of power generation invariably lead to some components failing to function appropriately. Extreme temperatures, high pressure, corrosive environments, and many other factors lead to costly interruptions in power. Nevertheless, these machines are intended to run for long periods of time, and interruptions in power generation for either maintenance or repair are very expensive. Continue reading

This article is a repost from the Wired Innovation Insights blog. Click here for the article’s original source. 

wired_logoDuring Big Data Week 2014 I had the pleasure of talking to Michael Dulin of Carolina’s HealthCare System about their work using data to improve healthcare outcomes.

Q: I am sure that your healthcare system’s advanced analytics group, Dickson Advanced Analytics, did not happen overnight. What were the drivers for Carolinas HealthCare System to focus on predictive analytics?

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hadoop-logoWhat is Hadoop? It’s another wacky name for an open-source software project, but Hadoop was also a significant advancement in the way that companies, governments, and organizations can collect, store, and process data. Companies like Cloudera, Hortonworks, and others have emerged to deliver professional level, Hadoop-based data solutions for the enterprise, while many organizations have built successful Hadoop implementations on their own.
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"NFL" by Parker Anderson,

“NFL” by Parker Anderson,

The great myth of Big Data is that it’s defining characteristic is size. In spite of the warnings about Variety and Velocity, in addition to every other V-word out there, the world has been obsessively focused on the collection of bigger and bigger data. But the key to extracting valuable information from data isn’t actually the size of the database, but your ability to make the most out of the data that you have.

In the NFL, scouts don’t simply identify talent by college performance. Predicting the potential success of a recruit requires combining several different data sets: comparing characteristics of previous players, successful and unsuccessful, who have spent time in the league, metrics from the scouting combine where athletes perform athletic proficiency tests, and college metrics, to represent the pool of potential talent. The more metrics scouts can incorporate, the better understanding they have of a prospect. For NFL scouts, the limitations of any single data set are obvious. While the potential value of personnel decisions can be huge, extracting value from data becomes more difficult as the data sets become more complex.

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