Video Info & Transcript

Video Transcript: 

Imagine standing at an intersection. Every time the lights change, you count and record the number of cars that drive through. Over a day, you collect some basic data.  Over a week, your data gets more useful and over a month, it becomes a potential resource for discovering ways to make the intersection more efficient. 

It’s this idea, collecting data on a huge scale and analyzing them, that may help us see new opportunities to solve big problems.

Let’s say that instead of you recording cars at one intersection, we put sensors at every intersection in the U.S. and they all report data. Over a day, the sensors report millions of pieces of data, over a week, billions and in a month, over 100 billion pieces - and growing every day.

Hidden in this big pile of growing data are important clues that could help commuters save time.

That’s the goal of big data – turning all these data into useful information. But it’s not that easy.

Big Data is really huge and complex data.  To make it useful information we need a few things in place. First, we need a place to put it - our normal computers and programs are too small and slow. Big Data needs serious computing power.  

Second, we have to handle lots of incoming traffic data, which may come from multiple sources and in different formats. To be useful, it needs to be in a format that makes it similar and comparable.

But we also need to handle data about relevant and changing conditions, like weather or gas prices. With these systems in place, we can collect and analyze huge amounts of complex data in real time - data that can provide clues to solving traffic problems and a lot more.

Consider the enormous amount of data produced by the healthcare industry, weather organizations and social networks. These data could help with healthier lives, safer families and more successful businesses.

Today scientists, technologists, entrepreneurs and organizations of all types are focused on putting big data to work.

By understanding the challenges of working with Big Data, we can make it work for us.

While big data may not make the light change when we want, it may help us find ways to get around a little more efficiently.

What it teaches: 

Incredible amounts of data are produced every day, so much that we can’t use it all.  Big data is a term used to describe the tools and processes that seek to make this data useful and productive.  This video uses the example of traffic data to teach:

  • Where big data comes from and how it’s collected
  • Why special tools are required to use it
  • The three big challenges: Volume, Variability and Velocity
  • The potential of big data across multiple industries

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