Big data is a trendy topic, but do you know what it really means, and why it became so important? It is in all the posts on social media, all the reviews in an e-commerce page, or even a large amount of NPS data.
In today’s video, you’ll understand the meaning of this concept, and how companies are analyzing this data in order to get insights to take meaningful decisions.
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Today’s video is about Big Data. We have heard this term so often, but do we really know what it means and how to make sense of it? Let’s figure it out!
First of all, why do we call it “Big”? It is easy to answer this question if you continue saying “Big compared to what”? The term “Big data” usually refers to data sets that are too big compared to what humans or common software could analyze.
It is, for example, the sum of the data captured by a lot of weather sensors. Or the sum of all healthcare information aggregated by a set of wearable devices. It could also be the sum of all the news published by the users on Facebook.
We could continue with many other examples, but generally, we can describe data as “big” where the dataset has the following characteristics:
Big Data has: Volume
Where the quantity of generated and stored data is enormously large. Usually, the size of the data also determines the value and its potential insight.
Where the type and nature of the data are diverse. Usually, this data comes from text, images, audio, video, and other forms often mixed together.
Where the speed at which the data is generated and processed is incredibly high. It is often available in real-time. It is generated frequently and, as frequently, is handled and processed.
In short: big data is a very large amount of data, which is only insightful if analyzed with sophisticated algorithms ran by powerful computers. Its applications usually run on cloud-based infrastructures, which potentially can reach infinite computational power.
These types of algorithms, which are created to analyze this kind of data, have the goal to identify patterns that describe the dynamics of groups of items. This type of analysis creates a fertile playground for the application of other technologies and processes. Machine learning, deep learning, and neural networks are a few examples of it.
All of these together have the goal to describe the past and the present. So that we become capable of predicting the future with good precision.
The alternative to Big data is Small data. Surprisingly Small data can be even more insightful than the big version…how? Read about it here.