What is data science?

Data on its own serves no one. Once collected, classified, and analysed, it becomes a powerful tool which can have a huge impact on businesses, people, and the world. This is exactly what data science is all about – deriving meaningful and important information (called intelligence) from a huge amount of data.

By combining statistics, maths, machine learning algorithms, new technologies and computer science, it allows people to draw conclusions from those quintillions of data produced daily.

Importance of data science 

The main scope of data science is discovering certain patterns within data, which allows data scientists to come up with data-driven forecasts and conclusions, and to perform other analysis, helping businesses in their decision-making processes and influencing their growth.

This is why data science is of utmost importance in all businesses across all industries.

Some of its benefits are:

• it allows getting to know customers: their habits, needs, experiences, age, interests, location, gender, preferences, shopping habits. All that information helps businesses target the right people and develop products that are in line with their expectations,
• it helps businesses make better decisions, based on solid facts, not just guesses,
• it allows making predictions for the future, including the ways each industry will develop, and how new, cutting-edge technology will influence certain processes,
• it helps to streamline manufacturing processes by identifying weaknesses and finding more effective ways of production,
• it improves research, for example in case of medical devices or drug discovery,
• it improves customers’ experience by giving information on what users expect and like,
• it helps to mitigate risks and frauds by predicting threats and allowing to create alerts that fight cybercrime,
• it allows businesses to identify opportunities and gaps in the market,
• it helps to test certain decisions and their consequences,
• it saves time and resources.

How do tech companies use data science?  

In IT industry, data science is even more important than in other sectors as it not only allows for making better decisions and developing businesses, but it constitutes an indispensable part of the entire IT world.

Data scientists, analysts and engineers have IT backgrounds, experience in working with complex programmes and devices, and need to know different strategies of analysing, extracting, and using data.

In the IT world, data science is used for:

• analysing data,
• developing new strategies,
• preparing data for analysis,
• visualisation of data,
• building models with data using different programming languages,
• deploying models into application.

Which industry will benefit from data science?

With its incredible usefulness, there is probably not a single industry that would not benefit from data science.

No matter whether it’s healthcare, transport, marketing, economic, ecommerce or finance, data science helps businesses create strategies influencing their future operations, at the same time saving their money, time, and efforts.

But while every industry can benefit from introducing data science, the benefits don’t always justify the investment linked to it. To make the best use of the possibilities it offers, it’s best to consult your data science strategy with specialists, experienced in introducing data science in the particular sector.

If you don’t know where to start, try our free Request for Proposal template for organisations looking for a data solutions partner.

Why is data science important in the future?

Our whole world is now digital, and it will be even more so in the years to come.

Data science, allowing businesses to gain more insight and intelligence from their own records, will therefore grow and become even more important than today, allowing businesses to develop and stay competitive.

Data scientists are and will be one of the most fundamental parts of every IT business, making data science one of the most promising areas of science, and allowing for further development of new technologies, such as artificial intelligence and machine learning.