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Data science is a field of study that uses various tools of Big Data and Machine Learning (ML) for processing huge volumes of data to extract patterns, derive meaningful information, and make reasoned business decisions.
Machine Learning is one of the core technologies underlying data science. It is a specific branch of AI that enables a program to learn based on a vast amount of processed data.
Big Data is a technology used to collect, store, process, and analyze information. A large collection of data is called the data set. The person who manipulates data sets in order to get insights or draw conclusions is called a data scientist.
In this article, we’ll look into how data science works, what tasks it can solve, and how it can benefit your business.

Data science usually deals with such tasks as data exploration, data analysis, reporting, and model creation. It is now used in many industries, including finance, e-commerce, entertainment, healthcare, logistics, etc. – and is constantly spreading to new areas as new solutions keep appearing.
Common data science applications include various recommendation systems (such as Netflix or Amazon), bank scoring and fraud detection solutions, detecting objects on images, medical diagnosing systems, speech recognition, planning and optimizing routes, and many more.
How it works – a practical example Here is an example that explains the process of working with data. Suppose we need to create a work schedule for our technical support staff. We know that the contact center works 24 hours a day, 7 days a week, and receives more calls during the daytime than at night. So we can conclude that we need to assign fewer employees to the night shift.
Still, this data is not sufficient to build a comprehensive model. We will also need to take into account such information as:

• the average number of incoming calls daily, weekly, monthly, and for each of the seasons;
• average distribution of calls within 24 hours;
• the number of employees;
• the weekends and holidays dates;
• location of the calling customers (difference in time zones can affect the result);
• additional information, such as employee salaries, lunch breaks, etc.

Based on all this data, an AI algorithm will be able to devise an optimal work schedule for the contact center. What’s more, it will also monitor all data changes and keep optimizing the schedule according to these changes.

It often takes a significant amount of time and effort for businesses to accept and start using new technologies. Sometimes a company’s management simply does not believe in the possibilities of AI-powered approaches. It can be psychologically challenging to shift to something new when the old ways of doing things are still working. However, the more businesses adopt data-driven approaches, the easier it is for the rest to follow in their footsteps.
One of the concerns regarding data science is that it’s quite expensive. For example, the costs to hire highly qualified experts, rent powerful servers, and other hardware, etc. However, in the long run, a data-driven approach will significantly reduce other expenses, such as the cost of business analytics. Usually, the costs of implementing such a system can be recouped in three to four months, depending on the project.
Another concern is “What if there is no or little data to process?”. If the company does not collect any information about customers or processes, then this can really be a problem since data is required to train algorithms. However, this issue can be fixed by integrating with a CRM system, especially if the company has used such a system for at least 2-3 years.

One of the most valuable benefits data science can bring to a company is empowering its management to make better decisions. Human decision-making can often be hampered by the lack of information or not being able to consider multiple factors. Data-driven approaches allow managers to see the big picture and make decisions based on a large amount of data.
Another way how data science can bring value is by personalizing customer experiences. Armed with AI algorithms, sales and marketing managers can better understand their audience and offer highly personalized experiences to each segment or even each individual customer. This helps to drive customer loyalty and makes them spend more, which results in increased profits.
Data science can also help identify growth opportunities through comprehensive data analysis. Data scientists can analyze the existing processes and make various assumptions to develop additional or alternative methods and then check their hypotheses. This is a continuous work at improving operational processes that is based on the organization’s data, which leads to a more optimized and streamlined business model.