YouTube sees more than 300 hours of videos uploaded every minute. That's 432,000 hours in 1 day or 158 million hours in 1 year. That's 18,000 years worth of videos in a year. And that's just YouTube ONLY! If we add all other videos in the public domain, we wouldn't even know where to start with the numbers.

However, the even bigger numbers are actually hidden in the private domain from sources like broadcasters, surveillance cameras, GoPros, bodycams, smart devices, etc. We are recording videos at an unprecedented pace and scale.

There is one word to describe this phenomenon - BIG!

Which brings us to Video Big Data. Or should I say the lack of it. Even the term "Video Big Data" is rarely heard of. This stems from the inability to extract video data and making sense of it. But there is so much information embedded inside videos that is waiting to be discovered.

So the real question is... how can we extract value from videos?

However, the problem with video is that it is the most difficult medium to work with. There are a few reasons why:

It is very difficult to extract various elements (speech, objects, faces, etc.) of video data.
Each video element requires a different data extraction technique.
It is very difficult to make sense of video data because of its unstructured nature

But there is hope yet. We will examine how we can tackle these problems and extract value from video big data in the next article.