Data Monetization: What Every Company Should Know

The process of data monetization, a concept that until recently was present only in conversations between technology experts, is now one of the recurring topics in strategic meetings at the management level in companies.

Tuesday, August 24, 2021

What is Data Monetization?
The concept of data monetization refers to the process of extracting, cleaning and analyzing the millions of data generated within a company, with the purpose of obtaining a benefit or economic value. This value can range from using the information to create performance indicators of the company's own business and use them to optimize processes and make better strategic decisions, use it as input in the creation of other products or services, market it to third parties, share it with business partners, among others.

Why is it so relevant for companies?

Thanks to the accelerated advance of technology and the development of data science, there are more and more tools and methodologies available to obtain data from company information systems, such as servers, transactional platforms, operating systems, software programs, among others. Now any company can extract its own data from its systems, clean them, validate them, analyze them and extract value from them.  
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Codemotion.com reports that "... research shows that Big Data is responsible for generating about US$190 billion in revenue for companies, and this figure is expected to reach US$274 billion by 2022. Companies that adopt Big Data practices can increase their sales by up to 4%."

What benefits does data monetization generate for companies?

From data-driven business models to creating tools to extract only certain types of information at a specific time in a production line, the uses of data monetization techniques are manifold.
 

  • Optimization of data use
  • Improved decision making and strategic planning processes
  • Increased operational efficiency
  • Improved customer understanding and buying experience
  • Reduced operational costs
  • Improved risk identification and prevention models
  • Identification and exploitation of new business opportunities.


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