Data science, or data science, increasingly affects every sector of our society; it is no coincidence that many have now defined it as data-driven. Organizations are increasingly using data science to transform data into a competitive advantage, redefining products and services and making targeted decisions.
What Is Data Science?
Science data is an evolutionary extension of the combined statistic with scientific methods and data analysis techniques through computer technology to extract value from the data.
The discourse on data science inevitably leads to talk about its specific and predominant sector: that of Big Data analytics.
Since modern technology has allowed the creation and storage of increasing amounts of information, the volumes of data have increased rapidly. Their growth is unstoppable: for example, it is estimated that 90% of data worldwide was created in the past two years. In 2020 every person on earth generated 1.7 megabytes of data every second.
The large number of data collected and stored can offer, as mentioned, competitive advantages in terms of business, but only if, precisely through data science techniques, trends and insights are detected to support decisions and effective development of products and services.
Know About Data Science, Artificial Intelligence and Machine Learning
The term data science is often used as a synonym for artificial intelligence (AI). However, these are two distinct disciplines, even if they are interconnected.
AI is a part of computer science that deals with the study and development of algorithms designed to make a machine understand how to perform one or more tasks autonomously. In particular, the branch of artificial intelligence that deals with automated learning is called machine learning.
It is the set of mechanisms that allow an intelligent system to improve its capabilities and performance over time: they will learn to perform specific tasks by improving, through experience, their skills and their responses and functions…
At the basis of machine learning, there are several different algorithms that, starting from primitive notions, become able to make a specific decision instead of another or carry out actions learned over time.
Instead, the goal of data science is, properly speaking, to develop strategies and models for data analysis to obtain new information. Still, it is also true that data science and AI are in a certain sense “complementary.”
For example, data scientists often use the deep learning methods that underpin the neural networks used to perform data cleansing, classification, and forecasting. Artificial intelligence-based applications can then leverage this clean and optimized data to learn how to perform their tasks more efficiently.
Finally, artificial intelligence enables data science and experts to perform classification and analysis operations much faster than a human being and optimize and speed up extracting information from data.
Big Data Analytics: The great challenge of Data Science
As early as 2001, the so-called “big data” was defined by analyst Doug Laney as data characterized by at least one of these three Vs.: volume, speed, or variety. Therefore, these are vast volumes of heterogeneous data by source and format, often analyzed in real-time.
The project Big Data Analytics can be classified into four types, based on the level of maturity of the methods used, and therefore the information that you can extract:
- Descriptive – that is the methodologies that describe the past and current situation of the business processes;
- Predictive – these are techniques that analyze data to answer questions about future events. In this context, we find techniques such as regression, forecasting, predictive models. It is in this context that machine learning can also come into play;
- Prescriptive – these are models that can hypothesize a series of future scenarios. Some application examples are in supply chain optimization and predictive maintenance;
- Automated – all these tools can perform an action based on the data analysis performed autonomously. Examples are dynamic pricing on a website or the automatic sorting of banking or insurance practices to identify fraud.
Advanced Analytics, finally, includes the categories of Predictive and Prescriptive Automated Analytics. The ultimate purpose of these methodologies is to provide broader support to business decision-makers, in some cases by automating specific actions.
The concrete benefits that Data Science offers to Companies
The use cases of data science are among the most varied. By way of non-exhaustive example, some of them can be mentioned, such as:
- In logistics: improvement of delivery efficiency by analyzing traffic patterns, weather conditions, and other combinatorial factors;
- In retail marketing: the determination of the customer abandonment rate by analyzing the data collected by the call centers, so that we can act to try to retain them;
- In Industry 4.0: the preparation of preventive maintenance plans to reduce unscheduled downtime, but also the optimization of the supply chain and of course, the improvement of the product;
- In medicine: the improvement and more excellent timeliness of patient diagnoses by analyzing the data of clinical examinations and the symptoms reported, but also the investigation, through the study of data from social media, of any public health needs, to finish the optimization of pharmaceutical and vaccine research;
- In finance: the detection of fraud, recognizing suspicious behavior and weird actions;
- In sales: the possibility of generating recommendations for the sale of products with a view to up and cross-selling
