Big Data vs Data Science

Among various directions related to digital technologies, two trends stand out as the most popular and promising: big data and data science. While these concepts are clear to experts, many laypersons misinterpret and confuse them. Let us outline the definitions and compare big data and data science.

Any data science consulting company will easily point out the key difference between these two concepts to their clients. Some consider data science as a complex approach, while others refer to it as a scientific area that encompasses several disciplines and aims to recover valuable information from raw data. The true meaning lies in the middle. 

Data science lies on top of several conventional sciences and borrows many principles and methods from them. Here are just a few illustrative examples of disciplines participating in data science: math, statistics, cybernetics, programming, UI design, and many others. 

Big Data is a large set of digital technologies that gave a common purpose: handling large amounts of information. Typical tasks performed in this regard are: 

  •  gathering/extraction of data, 
  •  data storage using local or cloud capacities, 
  •  performing a search in one or several databases simultaneously,
  •  sharing information among authorized users, 
  •  performing analysis of collected data for business, scientific and other purposes,
  •  providing visualization of the selected information, and so on.

The main similarity between Big Data and Data Science is that they both collect data and transform it into useful information that has practical value. Their goals are also very similar and may be briefly described as follows: collecting information, processing it into applicable knowledge, and storing it, preferably in a structured way. Ultimately, this accumulated information serves as a source for future analyses and insights to promote the development on various levels, from enterprise and industry levels to the global scale.

Despite the obvious similarity, Data Science differs from Big Data in some fundamental ideas as well as many small nuances. Here is a short list of the most important differences:

  •  Data Science is more focused on scientific approaches to data, while Big Data focuses on the technological aspects of data handling.
  •  Big Data focuses on extremely large volumes of information, while Data Science is more versatile and can be equally applied to any data regardless of its size.
  •  Data Science has a wider application. Its goal is to develop effective data-driven solutions and integrate them in all relevant areas of life. Big Data mostly aims at data mining, building structured databases, and analyzing collected information.
  •  Currently, Data Science is mainly used in scientific scenarios, while Big Data is more appreciated in business areas.
  •  Data Science tends to remain a more theoretic area as it involves scientific approaches, strategies, concepts, etc. Big Data prioritizes practical use, which narrows down its implementation. It puts theory to use with the help of hardware and software solutions.
  •  Data science is closer to statistics, and Big Data is closer to analytics.

Big Data provides information that is subsequently used by Data Science for finding patterns, building prediction models, creating machine learning algorithms. For example, thanks to this cooperation, Data Science can develop and improve working techniques to recognize speech and images.

Another characteristic feature of Big Data is its business focus and immense potential for finance and marketing fields. Of course, it is beneficial for many other areas, including healthcare or law enforcement, for example. But it really shines when applied to commerce. Big Data creates possibilities to analyze markets, evaluate and optimize performance, develop business strategies, and offers other advantages for enterprises.

Data Science also has numerous business applications, among other areas and industries. It is vital for understanding customer needs and desires in order to improve client reach and retention. The creation of behavior patterns is based on the data sets obtained using data mining and presents another connection between Data Science and Big Data. Moreover, the use of AI and ML technologies allow developers to build various customer support bots that help businesses and clients.

As you can see, Data Science and Big Data are very close. They have numerous similarities and common application areas. The main reason why people keep mixing them up is their tight connection with each other. Each of these areas complements and enhances the other one. The synergy between them is so large that Data Science cannot be effectively used without Big Data and vice versa.

If you own a business enterprise and are fond of digital transformation, you must implement both Big Data and Data Science to achieve maximum benefit. Do not disregard one in favor of the other; you won’t succeed or save significant money by doing this. Use Big Data to scrape and hoard as much information from your customers and rivals as possible and then use Data Science to find out how to promote your business based on the acquired data.

Find an experienced company that understands the importance of a well-balanced approach and has skills to implement it. The seamless integration of both Big Data and Data Science will bring your company a competitive advantage on the market and promote its further development. The implementation may be pricey but think of it as an investment that will pay off in the near future.