How Bigdata can do wonders in businesses?

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Data analysis is already a part of the business routine for various companies. This strategy helped the business leaders to streamline the process, understand customer and industry behavior patterns to make services and products more profitable. But, today we swim in an ocean of information and it is impossible to work on it manually every day. This is where Big Data technology comes in.  

Undoubtedly, big data has been the major economic trend for the past few years. 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. Thus, managers can more clearly understand bigdata trends and patterns for organizing their business strategy.

 If you want to know more about this topic hold on for a moment and learn what big data is and how it can impact your business.

What is big data?

The term Big Data is used to define a large set of IT tool that enables the capture, analysis, and cataloging of records in real-time. Information may come from various 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 is possible to create more effective routines and prepare for market changes in advance and this is known as predictive analytics.

Bigdata and its 5V’s

The complete concept of big data is based on five principles and each of them has a direct influence on the performance of solutions available in the market. These principles are known as the 5 V’s of Big Data. There are contents that point up to 10 V’s, but we believe these are the top five and originate the others.

  • Volume – The big data tool is able to handle a large amount of data. Thanks to social networks, smartphones, mobile internet and devices connected through the Internet of Things (IoT) as the amount of information circulating in digital media grows steadily. According to the forecast, by 2020 the volume is to reach 44 trillion gigabytes or 44 zettabytes, including Twitter, Facebook, Instagram posts, and email messages, chat apps, and other cloud-type files from worldwide servers. 

As we are fully dependent on Big Data tools, Artificial Intelligence and machine learning have led us to a new pattern of data analysis. These technologies enable analysts to work with a large data stream with a high performance where information is often created and collected in real-time. Therefore, big data systems are able to handle such information flows without generating performance loss or high computational cost.

  • Variety – Another aspect is the ability of a big data solution to work with varied data streams. As we have discussed earlier, 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. Thus each data has a characteristic type and is therefore classified into two types – unstructured data and structured data.
  • Speed – Let’s consider this scenario: the continuous flow of data in large quantities. In this case, the tools should 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. 

For example, Cloud computing is one of Big Data’s main “allies”. By running such systems in the cloud, analysts gain greater operational scalability at a lower cost. Thus, if the flow of information increases, it is possible to scale resources, preventing the new demand to impact the speed of execution of analysis routines.

  • Veracity – To ensure that data analysis is able to meet the business needs, it is important for the companies to work with reliable data sets. As we have mentioned earlier, 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. Given this, Big Data solutions should search for data from trusted sources and should be able to filter out which content is relevant to the business, as well as eliminating unreliable or unrelated content. This creates a more accurate analysis routine that is more likely to succeed.
  • Value – Lastly, to understand what Big Data is, we have the value aspect. The bigdata solution should add value to processes and make the services more competitive. How? By identifying trends and patterns that enable managers to make decisions with confidence and better target the strategy to win customers or more markets. Otherwise, from an operational perspective, you can also evaluate internal routines and use corporate tools to track bottlenecks and make process management more efficient.

How big data can impact businesses?

Big data can be employed in various areas of a business. However, for its use to be improved, it is not necessary to have knowledge about the technology, but also to identify which points of the company will be impacted by its implementation. With this knowledge, you will be able to target resources and increase the return on investment more efficiently in these data analysis solutions. 

Besides, big data technology can also help in the following ways:

  • Value Creation – All investments and internal processes can be directed to add real value to the business and its services. By implementing metrics across the entire operating chain, major improvements can be made to bring quality to the business, from management routines to creating strategies with greater potential for profit generation. Together, such factors will have a major impact on the business. The return on investments will be higher as service development strategies and projects will create a more competitive business portfolio with greater income generation potential. This will allow the companies to more easily differentiate itself from its competitors.
  • Cost reduction – Companies with complex operating chains need to be careful of their budget management. Poorly rated investments can have a negative impact on business resource management. Given this, it is critical for the company to be able to identify what can be optimized and which processes should be eliminated to create more effective internal dynamics, without operational bottlenecks and more efficiently. In this sense, Big Data plays a prominent role. Managers can use data analysis to identify bottlenecks and areas of low productivity. With this, the business can create processes with lower costs and waste.

Looking at this second aspect of data analysis, you can collect and evaluate information from the market and competitive research, social networking, after-sales processes, user support, and other offline data. All of this is to help marketing and sales teams to identify key market trends and anticipate consumer needs or better avenues for business expansion. Experts can also work on engaging consumers and create more relevant messages: Campaigns can be created to increase service returns and spontaneously spread merchandise.

  • Enterprise Risk Optimization – A company must continually assess and minimize the risks involved in its market strategies. In an environment based on a data analytics culture facilitated by Big Data, managers are able to predict scenarios more efficiently and thus identify which threats are involved in a project or in a particular product or offered services. As we have seen throughout the text, a good big data solution needs to analyze a large data stream with high performance, low computational cost and high scalability. Data analysis should be done according to the business profile, using resources that are aligned with the company’s needs and with high security. Thus, the investment will be able to generate a high return.

Conclusion

By being able to process and understand all these vast amounts of data, Big Data has revolutionized the business world for virtually every industry. Companies around the world are now getting ahead of the competition by being able to make more accurate predictions and better business decisions.

Data analysis is already a part of the business routine for various companies. This strategy helped the business leaders to streamline the process, understand customer and industry behavior patterns to make services and products more profitable. But, today we swim in an ocean of information and it is impossible to work on it manually every day. This is where Big Data technology comes in.  

Undoubtedly, big data has been the major economic trend for the past few years. 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. Thus, managers can more clearly understand bigdata trends and patterns for organizing their business strategy.

 If you want to know more about this topic hold on for a moment and learn what big data is and how it can impact your business.

What is big data?

The term Big Data is used to define a large set of IT tool that enables the capture, analysis, and cataloging of records in real-time. Information may come from various 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 is possible to create more effective routines and prepare for market changes in advance and this is known as predictive analytics.

Bigdata and its 5V’s

The complete concept of big data is based on five principles and each of them has a direct influence on the performance of solutions available in the market. These principles are known as the 5 V’s of Big Data. There are contents that point up to 10 V’s, but we believe these are the top five and originate the others.

  • Volume – The big data tool is able to handle a large amount of data. Thanks to social networks, smartphones, mobile internet and devices connected through the Internet of Things (IoT) as the amount of information circulating in digital media grows steadily. According to the forecast, by 2020 the volume is to reach 44 trillion gigabytes or 44 zettabytes, including Twitter, Facebook, Instagram posts, and email messages, chat apps, and other cloud-type files from worldwide servers. 

As we are fully dependent on Big Data tools, Artificial Intelligence and machine learning have led us to a new pattern of data analysis. These technologies enable analysts to work with a large data stream with a high performance where information is often created and collected in real-time. Therefore, big data systems are able to handle such information flows without generating performance loss or high computational cost.

  • Variety – Another aspect is the ability of a big data solution to work with varied data streams. As we have discussed earlier, 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. Thus each data has a characteristic type and is therefore classified into two types – unstructured data and structured data.
  • Speed – Let’s consider this scenario: the continuous flow of data in large quantities. In this case, the tools should 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. 

For example, Cloud computing is one of Big Data’s main “allies”. By running such systems in the cloud, analysts gain greater operational scalability at a lower cost. Thus, if the flow of information increases, it is possible to scale resources, preventing the new demand to impact the speed of execution of analysis routines.

  • Veracity – To ensure that data analysis is able to meet the business needs, it is important for the companies to work with reliable data sets. As we have mentioned earlier, 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. Given this, Big Data solutions should search for data from trusted sources and should be able to filter out which content is relevant to the business, as well as eliminating unreliable or unrelated content. This creates a more accurate analysis routine that is more likely to succeed.
  • Value – Lastly, to understand what Big Data is, we have the value aspect. The bigdata solution should add value to processes and make the services more competitive. How? By identifying trends and patterns that enable managers to make decisions with confidence and better target the strategy to win customers or more markets. Otherwise, from an operational perspective, you can also evaluate internal routines and use corporate tools to track bottlenecks and make process management more efficient.

How big data can impact businesses?

Big data can be employed in various areas of a business. However, for its use to be improved, it is not necessary to have knowledge about the technology, but also to identify which points of the company will be impacted by its implementation. With this knowledge, you will be able to target resources and increase the return on investment more efficiently in these data analysis solutions. 

Besides, big data technology can also help in the following ways:

  • Value Creation – All investments and internal processes can be directed to add real value to the business and its services. By implementing metrics across the entire operating chain, major improvements can be made to bring quality to the business, from management routines to creating strategies with greater potential for profit generation. Together, such factors will have a major impact on the business. The return on investments will be higher as service development strategies and projects will create a more competitive business portfolio with greater income generation potential. This will allow the companies to more easily differentiate itself from its competitors.
  • Cost reduction – Companies with complex operating chains need to be careful of their budget management. Poorly rated investments can have a negative impact on business resource management. Given this, it is critical for the company to be able to identify what can be optimized and which processes should be eliminated to create more effective internal dynamics, without operational bottlenecks and more efficiently. In this sense, Big Data plays a prominent role. Managers can use data analysis to identify bottlenecks and areas of low productivity. With this, the business can create processes with lower costs and waste.

Looking at this second aspect of data analysis, you can collect and evaluate information from the market and competitive research, social networking, after-sales processes, user support, and other offline data. All of this is to help marketing and sales teams to identify key market trends and anticipate consumer needs or better avenues for business expansion. Experts can also work on engaging consumers and create more relevant messages: Campaigns can be created to increase service returns and spontaneously spread merchandise.

  • Enterprise Risk Optimization – A company must continually assess and minimize the risks involved in its market strategies. In an environment based on a data analytics culture facilitated by Big Data, managers are able to predict scenarios more efficiently and thus identify which threats are involved in a project or in a particular product or offered services. As we have seen throughout the text, a good big data solution needs to analyze a large data stream with high performance, low computational cost and high scalability. Data analysis should be done according to the business profile, using resources that are aligned with the company’s needs and with high security. Thus, the investment will be able to generate a high return.

Conclusion

By being able to process and understand all these vast amounts of data, Big Data has revolutionized the business world for virtually every industry. Companies around the world are now getting ahead of the competition by being able to make more accurate predictions and better business decisions.

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