Big Data applied to business


Gamco Team

The market for Big Data is in full expansion. Although the need to transform data into information for decision making is not new, the implementation of Big Data in many areas of large companies has made visible the importance of data as a strategic resource.

Therefore, for companies that are digitally transforming their organizations, Big Data is already a crucial resource to meet the challenges of the future. Because, without a doubt, the future belongs to companies that understand how to collect, store and use their data effectively. 

The key to Big DataThe key to success, therefore, lies in the useful information that companies are able to extract from the data collected. Because having a lot of information and not knowing how to use it is of no value. That is why companies need new tools and technologies to manage these huge amounts of data in order to get the maximum potential out of the data they have. This translates into the need to hire personnel to facilitate the integration of Artificial Intelligence solutions for the management and automation of Big Data.

Although the implementation of Big Data, due to the high economic investment it requires, is being carried out mainly in large multinationals, more and more SMEs are incorporating Big Data analysis and management because of the competitive benefits it brings them. In these cases, the work is usually outsourced through cloud services, which are the most demanded. Cloud-based data analysis helps to reduce the costs of storing large amounts of data.

► You may be interested in: Digital Transformation in SMEs 

But what is Big Data?

Any device that is capable of storing and processing information is a source of data. So the most important thing is that from the raw data, it can be organized so that it can be converted into useful information for businesses.

In this sense, Big Data makes it possible to collect information from website visits, social networks, call logs and other data sources.

Big Data is a term that refers to large volumes of data or big data that require computer applications, based on Artificial Intelligence, for their proper processing and that allows transforming the information generated into a company's asset.

In this way, decisions can be made from a more realistic perspective (about what is happening) and not just based on intuition. This allows, for example, a company to launch products and services aligned with the needs and desires of its customers in a given time period.

In this sense, understanding customer trends and decisions helps guide a company in the direction it should take.

How does Big Data work?

The 3 classic magnitudes that define Big Data are known as the 3 V's: volume, variety and velocity.

  1. Volume. It refers to the amount of data that is originated and stored for the purpose of processing and transforming it into actions. These can come from various virtual sources: social networks, e-mails, electronic devices, etc.
  2. Variety. Refers to the sources in which data can be recorded and extracted: text documents, emails, audios, videos or images residing on a mobile device, social media profiles, etc.
  3. Speed. It refers to the speed with which data is created, stored and processed in real time. In this aspect, it is essential that the company has the necessary infrastructure and processes in place to convert data into useful information in the shortest possible time.

Likewise, Big Data can also be defined based on the measurement of other complementary magnitudes that we list below:

  1. Truthfulness. It refers to the quality of the data or the degree of reliability of the information received. By means of machine learning, AI makes it possible to exclude from the analysis all data that are false or lack value for the proposed objective.
  2. Value. It refers to the ability to generate valuable insights that allow converting the information received into knowledge that helps to make a decision or perform a specific action.
  3. Feasibility. It refers to a company's ability to efficiently transform data into useful information.
  4. Visualization. It refers to the way in which data are displayed in order to proceed appropriately from the patterns and interpretations (variability) of consumer behavior.

What is Big Data for?

Every time a person visits a website or accesses a social network from their smartphone or laptop, they are leaving a trail, providing a series of data about their online activity and how they interact with products or services.

By using technology to manage Big Data, a company will thus be able to get to know the user (new, potential or established customer) in depth and delve into their needs, frustrations and desires in order to generate satisfactory user experiences. It also offers the opportunity to segment customers according to their preferences.

In its beginnings, Big Data was developed only in communication and marketing companies, but thanks to the evolution of technologies and the market itself, there are many sectors where it is being successfully applied, such as sports, commerce, security, medicine or transportation, with the aim of improving the interaction experience, as well as optimizing the value offered to the customer in order to build customer loyalty. 

Big Data can be used to manage and optimize countless tasks in companies, among which we can highlight:

  • Improve marketing strategies (customized) and identify new business opportunities.
  • Discover consumer buying habits to identify behavioral patterns that improve the shopping experience.
  • Prevent possible financial fraud.
  • Identify processes whose costs can be reduced and increase sales volume.
  • Analyze the competition to learn from their successes and mistakes.
  • Optimize fuel use in the transportation industry by setting the most suitable routes according to weather, traffic conditions, etc. 
  • Personalized health plans by monitoring the client's health conditions in real time.
  • Manage merchandise stock through predictive inventories.
  • Generation of cybersecurity protocols and real-time data monitoring.

Main contents analyzed by Big Data

The information analyzed by Big Data can come from a multitude of sources. The most frequent sources due to the quantity and quality of the data they offer are the following: 

  • The content provided by the user's navigation through the web pages.
  • Content sourced from social networks.
  • Machine-to-machine or M2M content, i.e. any technology that allows two devices to exchange information with each other and send data. Human intervention is not necessary for this communication to take place, as it takes place between machines or devices autonomously.
  • Invoice records, e-mails, voice memos and phone calls.
  • Biometric information, such as fingerprints or facial recognition.
  • In risk or financial portfolio tracking applications. Where hundreds of millions of monthly transactions corresponding to tens of millions of contracts and millions of customers are handled.
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