Published On: Thu, Aug 8th, 2019

What is big data and how important is it to deploy in the enterprise?

Data analysis is already part of the business routine of various areas of companies. This is a strategy that helps you streamline processes and understand customer and industry behavior patterns to make services and products more profitable. But the amount of information available has never been greater, and it becomes more and more impossible to do this manually every day. That’s where Big Data technology comes in.

photo/ Gerd Altmann

In general, we can say that Big Data is a technology that enables the processing of information with high performance and availability. They are digital tools that make collecting, processing and visualizing data simpler, standardized and effective. This enables managers to better understand trends and patterns for organizing their business strategy.

The term Big Data is used to define a large set of IT tools that enable the capture, analysis and cataloging of records in real time. Information can come from different internal and external sources, such as customer base, market analysis, social networks, electronic devices, internal processes or even offline surveys.

The advantage of these tools is that they centralize the collection and analysis of this large set of records in one place. From this, statistics and processing techniques are left to the machines, allowing analysts to quickly identify patterns and predict trends more accurately.

As a result, it will be possible to create more effective routines and prepare for market changes in advance (these are called predictive analytics). Thus, the business can remain continuously with a high degree of competitiveness.


A big data tool must be able to handle a large amount of data. Thanks to social networks, smartphones, mobile internet and devices connected through the Internet of Things (IoT), the amount of information circulating in digital media grows steadily. By 2020, the volume is expected to reach 44 trillion gigabytes or 44 zettabytes, including posts on Twitter, Facebook and Instagram, email messages, chat apps, among other types of files that circulate in the cloud (from worldwide servers).

That is why we are and are increasingly dependent on Big Data tools, which through Artificial Intelligence and machine learning have led us to a new pattern of data analysis. These technologies enable analysts to be able to work with a large data stream with high performance – information is often created and collected in real time. Therefore, big data systems should be able to handle such information streams without causing performance loss or high computational cost.

photo/ Gerd Altmann


Another aspect is the ability of a big data solution to work with varied data streams. As we said, information can come from a variety of devices, social networks, mobile devices, and even offline media such as market research and financial transaction data tables.


One more scenario: the continuous flow of data in large quantities. In this case, the tool must have high analysis performance so that patterns can be found quickly. As a result, companies are now using ancillary technologies to ensure the highest performance of their big data solutions.


To ensure that data analysis is able to meet business needs, it is crucial that the company be able to work with reliable data sets. The records used are often unstructured, which can lead to scenarios where the number of noises is high, impacting the quality of the analyst’s work.


Finally, to understand what Big Data is, we have the value aspect. That is, the solution must be able to add value to processes and make services more competitive.

Author: Alicia Haven

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