Using data is constantly rising. Organizations are more and more dependent on how they acquire and analyze data to extract accurate information for their business areas. This is where three interconnected terms come into play: Data Science, Big Data, and Data Analytics.

What is Data Science?

Widely spread in the market, Data Science refers to an entire process of data collection, transformation, and analysis. Information is creatively extracted through a set of methods and tools by a dedicated professional who aims to:

1. Understand their company’s business and;

2. Identify patterns that are beneficial for the company’s decision-making processes.

However, Data Science is not possible without Big Data.

So, what is Big Data?

Big Data, something that has been steadily growing since 2012, can be defined as a set of techniques capable of analyzing large quantities of data to generate results that would be difficult to achieve with smaller volumes (we talk more about it in this article).

For a better understanding of Big Data, we can define the three pillars that comprise it:

• Volume: Big Data is a massive amount of data, not just Terabytes but Petabytes and Exabytes, which are millions of Gigabytes. In 2020, the forecast was that 40 Exabytes of data would be generated annually;
• Velocity: Depending on a company’s business, one minute can be too long, whether it’s for detecting fraud, analyzing medical data, or dealing with time-sensitive information;
• Variety: Big Data encompasses all kinds of data, whether it comes from text, sensors, web navigation, social media, online stores, your smartphone, and many other data sources.

And what about Data Analytics?

To conclude the concepts discussed in this article, we have Data Analytics. It refers to the systematic use and analysis of data for efficient decision-making. It is widely applied in areas such as Marketing, Retail, Finances, and so on.

All this analysis is done using methods such as:

• Statistical Modeling;
• Forecasting;
• Text Mining;
• Experiment Design, among others.

Use cases

Digital Advertising – From banners displayed on websites to digital screens at airports, all content is determined by Data Science algorithms. This is how digital ads leverage the necessary data to target ads to specific users based on their behavior. For example, an ad that is shown to you on a website may be different from the one that appears for another user on the same site.

Recommendation Systems – Amazon’s website provides a clear example of the use of Business Intelligence (BI), Data Analytics, and Data Science. Through data collection and Data Science algorithms, it enhances users’ experiences, helping them to find relevant products.

In addition to Amazon, companies like Netflix, Twitter, LinkedIn, and so many others have been using Data Science algorithms to improve user experiences with more accurate and relevant content.