Making sense of the mountain

Nearly every process and tool around us is creating data… massive amounts of it. We now think of this data in terms of petabytes and zettabytes of captured information.  The challenge we face is to formulate the “right” questions to ask of that information. Every set of data is different, but we can look at what others are doing to help find our own north star.  Here are some examples of how others are using sensor data today:

  • Predictive Analytics and Proactive Maintenance
    The ability to predict equipment failure (and respond proactively) is extraordinarily valuable because it is far less expensive to do preventative maintenance than it is to pay for emergency repair or replacement equipment under duress. If a restaurant’s refrigerator fails, the franchise loses thousands of dollars in spoiled food and a day’s revenue. Fixed assets such as cellular transmission towers are difficult and expensive to replace, yet they exist to transmit data, so sensors can transmit diagnostic data that helps prolong the life of those assets. Algorithms can process massive amounts of sensor signals to identify previously invisible, subtle patterns indicating when an inexpensive repair is likely to prevent a costly replacement.
  • Improving Research and Health
    Since 2007, Children’s Hospital Los Angeles has collected sensor data from its pediatric intensive care units, sampled from each patient every 30 seconds. This dataset includes more than one billion individual measurements. Doctors plan to use this data to diagnose and predict medical episodes with greater precision. According to one of the researchers, the difficulty is to find medically useful patterns because “there are an infinite number of trivial patterns, such as people who tend to have babies are female and people over six-feet tall are over five-feet tall.”

There are hundreds, maybe thousands of uses of sensor data.  My favorite example is the rail operator who has equipped rails with sensors that collect the sound frequencies as a train travels over a section of rail, looking for discrepancies to identify any potential issues within the system. Awesome.

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