Main Differences Between Big Data, Data Science, and Data Analytics

With a variety of terms for similar subjects, it's hard to know what each one really means, right?

To make your life easier, we have prepared a material with the main differences between the three concepts.

Let's define what they are, explain what the purpose of each one is and what are the main activities of professionals in these areas.

Want to know more? Enjoy this content!

Big Data, Data Science and Data Analytics

First of all, it is important to highlight that the three areas work with the same product: data.

However, the goals and responsibilities of each are totally different and, at the same time, complementary within an organization.

Understand now the differences between Big Data, Data Science and Data Analytics.

Big Data

Every day, 2.5 quintillion bytes of data are generated, bringing new possibilities to discover and predict market opportunities and create new products.

This data set is what we call Big Data.

That is, it is a high volume of data, generated at high speed and great variety, and that cannot be processed by databases or traditional processing applications.

The main objective of the Big Data analyst is to identify data within this large set to use it in a given context relevant to organizations.

This professional works primarily on building large-scale data processing systems and architecting scalable data distribution systems.

Not an easy task, don't you agree?

• It requires multidisciplinary knowledge and well-developed analytical thinking.
• It is necessary to know about business to understand the context in which the company is inserted.
• And it is very important to have statistical skills and programming languages, such as Java and Scala.
• In addition to knowing platforms, such as NoSQL and Mongo DB.

The key, however, to being an expert in this area of Big Data is technical skill and understanding of systems and architectures.

The challenge of Big Data is its 5Vs:

1. Volume: The size of the data (petabyte, exabyte, zettabyte)
2. Variety: the format of the data (tables, images, texts)
3. Speed: How fast data is generated (per second, per minute)
4. Veracity: the reliability of the data (sources, integrations, communication)
5. Value: The outcomes that data brings to the business (context, problem, solution)

By working with all these Vs in the Big Data analysis process, your business will have access to important data that will ensure assertive decision-making.

Data Science

If Big Data is the data set, Data Science is the science that studies them.

The ultimate goal of this area is to extract value from data.

The data scientist is one who predicts the future based on past patterns and develops new methods of data analysis and machine learning.

It asks questions like:

• What data sources do we have available?
• Which ones have the most immediate value?
• What are our options for processing this data?

Its activities are highly technical and require in-depth knowledge in statistics, Python, R, SAS, Java, Perl, C/C++ languages, as well as platforms such as Hadoop and SQL.

And, finally, data science, it is essential to understand business to see the context in which the company is inserted.

With this, he combines the insights he gets from the business with his statistical and programming knowledge.

Data Analytics

It's time to present the results. Being able to turn the data into something that can be easily visualized can be challenging.

What does the data analytics professional need to pay attention to at this point?

On properly communicating important information.

This area works with the intelligent analysis of the large volume stored by companies and its main objective is to find significant correlations between data.

The professional in the area analyzes, organizes and synthesizes the data, in addition to creating reports with the most relevant information for the company.

Communication is one of the most valued skills among data analysts, in addition to, of course, mastery over business.

For them, it is not essential to know programming languages in depth, but it is essential to have ease with numbers and affinity with statistics.

You can find these services here at Indicium

One thing is certain: the importance that these services have within a data-driven organization.

So, if your company wants to develop and utilize the full power of data, we have good news!

Here at Indicium, we have professionals who are experts in all areas involving data. And they can help you.

Get in touch today here.