August 11, 2022

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Data Science: What is Big Data Analytics?

3 min read
Data Science: What is Big Data Analytics?

Chances are you’ve heard about big data. Even people with no interest in data science have likely read or heard the term in news stories, brand marketing, and even popular culture. Unfortunately, the use of the term sometimes leads to confusing or inaccurate interpretations. Thus, people who are interested in a career in big data analytics are often starting from a less-than-clear point. This article breaks down the definitions and can help develop a better understanding of this career path as you work to master the key skills.

What Makes Data Big?

The buzzword status of “big” data means it gets thrown around a lot, so it can be difficult to understand how it’s any different from regular data.

According to Oracle, Big Data is defined as “data that contains greater variety, arriving in increasing volumes and with more velocity.” The three Vs are the keys to understanding the concept:

  • Volume: The amount of data being collected by the system.
  • Velocity: The rate at which data is being collected.
  • Variety: The number of different types of data in the system.

There isn’t a clear cutoff point at which data becomes big. However, the term is best applied to data so massive in scope that traditional data processing methods wouldn’t be able to manage it.

What Is Big Data Analytics?

Once you understand the factors that make data big, understanding big data analytics is simple. It’s all the work done to process and analyze those big datasets. More specifically, it’s the technologies, techniques, and work used to organize, manage, and transform big data so it can be used to uncover new information and insights.

Although the concept is quite simple, the process of analyzing this data can be very complicated. Furthermore, as the three Vs keep progressing, the techniques and tools needed for analysis must also get more advanced and complex.

Why Is It Important?

In the simplest terms, big data analytics is important because big data requires special analysis to be useful. For each organization, specific solutions may vary. Some teams use these processes to more effectively target their marketing efforts to their audiences. Other teams use big data to identify new revenue opportunities or to predict the likely future of their current revenue streams. In short, big data analytics helps organizations use larger, faster, and more complex data to achieve their strategic goals.

How Does Big Data Analytics Work?

There are five key steps to data analytics. These steps are the same regardless of whether the data is big or not. However, the techniques used, complexity, and significance of each step are a little different.

  • Collection: First and foremost, the data must be collected. For big data, this is typically achieved with many different streams such as web service logs, mobile app analytics, social media engagement, machine data, and more. Data streams are chosen based on organization needs.
  • Processing: Next, the data must be processed so it can be retrieved in an organized fashion. With simple data sets, this may be done manually. However, automation is required for big data to handle the complexity and scale. When a data system is set up, this is one of the most important considerations.
  • Cleaning: Sometimes data sets have mistakes in them. They may have inconsistencies, duplications, or formatting errors. This is even more likely with big data due to the three Vs. Thus, having excellent cleaning tools is a must for any organization working with this kind of data.
  • Analysis: With processed and cleaned data, you can begin your analysis. This can include tools such as data mining, machine learning, artificial intelligence, and predictive analysis. Sometimes big data is used to drive a product’s functions automatically. Other times, it may be analyzed and visualized to help with decision-making.
  • Implementation: Finally, the analyzed data can be used. Again, this step depends significantly on the organization and the purpose of the data. Examples of implementation include marketing analytics, predicting revenue flow, and strategizing for more accurate business decisions. 

Explore the World of Big Data

Working in big data analytics can be an exciting opportunity to engage with cutting edge technology. Improvements in big data have helped push artificial intelligence and machine learning forward. In turn, this has led to improvements in transportation, user experiences, and much more. INE can help you learn more through data science and analytics courses

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