Big Data vs. Analytics: what’s the difference?

Data has become one of the most important factors in business. In order to be able to process, analyze and store large amounts of information, different approaches and work methodologies have been developed. In this article, we are going to see the difference between these methodologies and, more specifically, between Big Data vs. Analysis.

First of all, we will provide some concept definitions. In order to do this, we will talk about both Big Data and Data Science as well as Data Analysis. While they may look the same, they are not, so our aim is to identify each of them and try to minimize the potential of confusion between the different types of work methodologies when it comes to information processing.

All in all, we should also highlight that all these fields provide plenty of opportunities to develop a promising professional career. This is particularly the case of Big Data, where a Big Data course that allows you to become a professional specialized in certain tools can greatly and easily improve your professional profile so that you can become an opt candidate for well-regarded and well-paid positions.

 

What is Big Data, Data Science and Data Analysis?

First of all, let’s go through the concept of Big Data. When we talk about Big Data, we refer to any large and complex collection of data. This data can be structured or unstructured but, given its usual complexity, it is most common to work with unstructured data. The scope of application of Big Data is usually found more often as part of private companies.

On the other hand, Data Science represents a multidisciplinary field of knowledge and its objective is to achieve a deeper and broader knowledge on a specific topic. When we refer to Data Science, we are talking about a scientific discipline that is usually aimed at research, not at improving economic or financial results in companies.

Last but not least, there’s Data Analysis. In this case, when we talk about data analysis, we refer to the process of extracting relevant information from all the data collected, wherever they come from.

Both Big Data, Data Analysis and Data Science all belong to the same field of knowledge, but present different applications.

To sum up, these three disciplines are:

• Big Data: large and complex data sets
• Data Science: a scientific discipline for data-driven research
• Data analysis: allows for the extraction of relevant information from data

As can be deduced from these definitions, these three disciplines are complementary, but, at the same time, can be used separately. For example, data analysis does not have to focus on extracting information from large data sets and can be focused on small data.

 

Big Data vs. Analytics: the key difference

Now that we are familiar with the definitions and possible applications of Big Data vs. Analytics, it is time to see what their key differences are:

• Data types. The fundamental difference between Big Data vx. Analysis is in the nature of the data itself. While Big Data can be described as a great library that contains all the information we need, data analysis can be compared to a book containing the solution to a specific problem.
• Structure. As regards the structure of the data, Big Data tends to be composed of unstructured data, that is, data that comes from different sources and in different formats. On the other hand, in Data Analysis, the information with which one works is well structured.
• Tools. Another of the key differences between Big Data and Data Analysis is in the work tools used in each of them. Big Data relies on sophisticated tools, capable of processing and managing large volumes of data in parallel. On the other hand, Data Analysis employs simple tools needed for data modeling or predictive analysis processes, since the data is structured and organized.
• Industry. The scope of application of both disciplines represents another key difference between Big Data vs. Analysis. Although they may intersect or overlap in certain areas, data analysis is typically mostly dedicated to IT, including certain specific sectors such as travel or private health. On the other hand, Big Data is often designed for financial and commercial sectors that seek to make strategic decisions in highly competitive markets.

Getting trained in Big Data or Data Analysis is a good way to improve your professional opportunities.

As you can see, there are some key differences between Big Data vs. Analytics, although both disciplines can complement each other. However, Big Data is becoming increasingly relevant because it allows experts to address a greater amount of information, even if the work involved is somewhat more complex and slightly more expensive.

 

Get trained as a Big Data expert!

Now you know all the differences between Big Data vs. Analysis. As we mentioned at the beginning of this article, Big Data represents an outstanding field to improve your future professional career. However, in order to achieve this, you’ll need quality training that provides you with the knowledge and skills necessary to enhance your professional profile.

With Tokyo School’s Big Data course you can do this. We are a training school specialized in new technologies and we’re passionate about data. Let our senseis train you and become a Big Data professional!

If you have any questions or want to know more about us or our Big Data training, get in touch with us! We will solve all your concerns and help you take the first steps to become a Big Data expert. We can’t wait to meet you!