Big data is a trendy topic, but do you know what it really means, and why it became so important?
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.
What are other applications of Big Data that you could think about?
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Hello, everyone, I am Riccardo Osti and on a daily basis I help the world’s best brands become more profitable by investing in the consumer experience
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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” data? It is easy to answer this question if you continue saying “Big compared to what”? In fact, the term Big data usually refers to data sets that are too big compared to what humans or common software could analyze.
Big data, for example, is the sum of the data captured by a lot of weather sensors. Big data is the sum of all healthcare information aggregated by a set of wearable devices. Big data 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:
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 insightVariety: where the type and nature of the data are diverse. Usually, Big data comes from text, images, audio, video, and other forms often mixed together
Velocity: where the speed at which the data is generated and processed is incredibly high. Big data 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. In fact, big data applications usually run on cloud-based infrastructures, that potentially can reach infinite computational power.
These types of algorithms, which are created to analyze big data, generally have the goal to identify patterns and trends that describe the dynamics of a certain group of items. This type of analysis creates a fertile playground for the application of other technologies and processes, such as machine learning, deep learning, and neural networks.
All of these together have the goal to brilliantly 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 big data…how? Well, subscribe and watch my next videos.
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See you next time,