An overview about Big Data Analytics and how does it work

To succeed in your business, it is essential that your product or service meets all or a large part of the customers' wishes.

For this, you need to understand these cravings very well. In addition, it is necessary to be within the market trends and know well which strategies to use correctly.

With this, another concept emerges in the market, Big Data Analytics. Is it a new term? Does it generate useful information?

Come with us and learn everything you need to know and what results you can expect.

In this post you will read about:

• What is Big Data Analytics
• The 5 pillars of Big Data Analytics
• How does Big Data Analytics work?
• Where is the problem?
• How does the market use Big Data Analytics?
  

What is Big Data Analytics?

Big Data Analytics can be understood as a concept of structured and unstructured data clusters that are generated at all times.

The term Big Data is old, but only gained relevance in today's generation of digital data, where people are more immersedin IoT. All this thanks to the methods of sharing and integration between the media.

With the help of statistical programming, like R or Python, companies learn more about their users and explore more their datasets, receiving a lot of information about new market trends.

These procedures help companies explore their data and use them to learn in order to create new opportunities. All these data are possible to be analyzed with almost real-time responses.

Are you tired of missing out on incredible career opportunities because you don't know about the main Excel tools?

So you are in the right place! In the Excel for Beginners course, you will learn to work with data in an agile way.

In addition, the course is available for FREE, just click on the banner button below and embark on this journey of knowledge!

The 5 pillars of Big Data Analytics

To complement the concept, it is critical to understand the features behind Big Data Analytics that will help you in this competitive environment.

• Volume: the volume is already large and the trend is only increasing. It's a lot of data generated every second and stand in the zettabytes and brontobytes range
• Speed: The speed follows in the same proportion as the volume, i.e. large and fast. With this, the data is analyzed the instant they are created thus, the purpose is to achieve real-time information processing
• Variety: with increasing speed and volume, comes increasing diversity. The data come from several sources and interpretations
• Veracity: The large volumes of data analyzed are intended to serve as a guide to planning more safely. With this, it is necessary to establish which data are true and current
• Value: In order to add value to your business, you need to have a good investment in the infrastructure so you can get useful information.

How does Big Data Analytics work?

When we talk about Big Data Analytics, we can divide this analysis of data into 3 categories:

• Social Data are data that contains profiles and behaviors of the people, we have as examples the social networks
• Enterprise Data are data generated especially by companies, data such as operations and financial
• Personal Data is new, based on the integration between devices through the internet, the main example are smartphones.

The data analysis process is able to explore the data in order to create principles to optimize the understanding of scenarios and patterns. This is all thanks to careful and accurate analysis.

All of these standards are designed to filter and bring you all the useful information. Behind this, there is a whole cycle that begins with data extraction, organization, treatment, and understanding.

But is it worth implementing in my business? What improvement points will I perceive?

Where is the problem?

Now that you have understood better about Big Data Analytics and you already have all the useful data at hand, it's time to move forward with greater security and assertive decision making.

To do this, we've listed the key benefits that Big Data Analytics deployment brings you. Come on!

Accurate Pattern Identification: Custom segmentation of your business audience is occurring. It's necessary to study the behavior of this public, whether by age, behavior and / or region. Therefore, it is possible to deliver to the customer a product according to his profile

Cost Reduction: Cloud data storage is an opportunity for businesses to work more efficiently. With cloud computing, Big Data made it possible for many companies to no longer need to have their own servers, and that was a huge advantage.

Faster and better decision making: With the speed and intelligence of data analysis, it is possible for companies to have all the information they need in real time, enabling managers to make their decisions more accurately.

Using Machine Learning Algorithms, all data is processed and the manager can make the best decision based on the mathematical results, thus guaranteeing success in the analysis.

Development of products and services: done with the collection of concrete data of the consumers, and later the accomplishment of the Data mining, where an algorithm is used within a large database to recognize patterns and rules that can aid in making a decision. Thus, it is possible to establish the needs and desires of the customers, resulting in the success of your product.

Competitive analysis: Getting ahead of the customer's needs and wants is a way to get ahead in the competitive environment. In addition, it is possible to identify different and better paths than those followed by competitors.

How does the market use it?

Many companies now have data that is part of Big Data Analytics. However, few make the use strategically, that is, taking advantage of everything the database offers.

Big Data Analytics can be implemented regardless of the size of the company, because the implementation is not complex, but arduous. In Brazil, few companies make use of it, so it's time for you to get out in front of them and get good positions in the market.

Generally, the data scientists are responsible for the "data cleaning" and preparation of the data, which can also be understood as Data Science, which is the term referring to the set of techniques and / or tools which are used to extract data or insights.

Analysts can also take on these roles, often being linked to marketing, technology and innovation. This market is on the rise and full of opportunities.

Which professional is employed in this area?

As we see in other markets that involve technology, to work in all the employments in this area you must need, at least, a bachelor’s degree. This is due to the intense use of computational mathematics and statistics. Let's see some positions:

Data Scientist: The priority for this employment is to work on new ways of capturing and filtering data. The large volume of data, without many treatments, will be managed by the data scientist

Data Analyst: After the raw data collection is done by the data scientist, the work is passed on to the data analyst. This, in turn, is in charge of working with the data that has already been filtered, drawing valuable insights and feedback on new market trends.

Systems Architect: The systems architect is the IT professional responsible for the conception, design and development of computer systems architecture. He will be responsible for coordinating the actions of the data scientist and data analyst, in addition to being responsible for all structuring of the cloud system.

If you made it this far, I have a question for you...

Today one of the most demanded skills in selection processes is the use of Excel. And to give you a good idea of the first steps and first functions, we created the Excel eBook for Beginners.

By downloading our eBook, you will have a step-by-step and practical guide to be able to create your first spreadsheets, and use your most basic and essential functions.

Download it now!