Last week our CEO explained the meaning of Big Data, a very trending topic, in this video. Today, we will talk about Small Data. You might not have heard about this concept yet, but it is everywhere in our daily lives. Watch the video to understand what this concept means, its relationship with Big Data, and why it is so important.
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First of all, why do we call it “Small” data? We know that “Big” data is something too big to be analyzed by humans. Well, on the contrary, Small data is data that is ‘small’ enough for human comprehension. It is data that comes in a volume and format that makes it accessible, informative and actionable for us.
In fact, the term “big data” is about machines, while “small data” is about people. The only way to comprehend Big data is to reduce the data into small, visually-appealing objects representing various aspects of large data sets. A simple example? Sensors gather weather data all over the country, computers process this big amount of data and transform it into a chart, that is shown by a tv presenter and easily understood by everyone.
We could continue with many other examples, but generally, we can describe data as “small” where the dataset is:
Small Data is: Accessible
Big data is very difficult to manage, and consequently, very few people within an organization are capable to make sense of it. Small Data comes in smaller, lighter, packages that are much easier to use
where even without powerful computers you can derive conclusions. That’s why we use charts…they summarise big data into something smaller and easier to understand
small data is generally about users, customers, and their behaviors. It highlights the reason why behind the trends of big data, and therefore can be very insightful and actionable
In short: small data is big data which has been connected, organized and packaged by complex algorithms in order to appear easy and actionable for humans
This term will be much more popular than big data in the near future. In fact, the technology industry is moving away from big centralized analysis software to smaller, detailed and intelligent, connected datasets. If today the secret for success stays in computational power, tomorrow it will be in the capability to mix and merge datasets together, extracting value.
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See you next time.