Why have we been talking so much about Data Science

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Why have we been talking so much about Data Science in recent years? Originally, the data available to companies was essentially structured and very limited in number. Today, on the contrary, the data is largely unstructured or semi-structured. As the following graph shows, it is estimated that in 2010, unstructured data will represent more than 80% of the data collected by companies.

definition data science unstructured data
Source: Erudeka
These data come from very diverse and heterogeneous sources. Basic BI tools are no longer able to process this huge volume of data and especially this considerable variety of data. This is why we now need to use more complex and advanced analysis tools and algorithms to extract “insights” from this mass of raw data. But this is not the only reason why Data Science has become in a sense a “popular” term (even if few people really understand what it is!). The other reason for the rise of Data Science is the explosion of use cases. Here are three of them:

What if you could understand your customers' needs precisely using the data you already have on them (such as their browsing history, their purchase history, their age, their income, etc.)? You already have this data in your information system. But now, with the increase in the volume of data available, you can run models much more efficiently than in the past and make recommendations with much more relevance. With the key, more turnover and more profits for your company. Marketing recommendation paraguay whatsapp list is one of the major fields of application of Data Science.
Let’s take another use case of Data Science, another example of the use of this “science” in decision-making. Imagine a car that can drive you home by itself? Self-driving cars collect data from sensitive sensors (radars, cameras, lasers) to create a map of the environment. Based on the data collected in real time, the car can make decisions on its own. For example, to accelerate, slow down, overtake, negotiate a turn, etc. All based on machine learning algorithms. The rise of self-driving cars has a role in the popularization of Data Science.
Let's take one last example. That of predictive analytics. And more specifically, predictive analytics applied to weather forecasting. Data from planes, boats, radars and satellites can be collected and analyzed to build models. With models, not only could we predict the weather in the coming days, but also the recurrence of natural disasters. This application of Data Science could potentially save dozens of lives.
The fields of application of Data Analysis are very numerous. This infographic allows you to become aware of them:

definition data science use cases

More and more fields need Data Science to develop. We clearly haven't heard the last of this discipline. Now that we know why Data Science has "established itself", let's try to understand how it works!