If you are in the world of big data analysis, you have surely heard about machine learning term. Beyond that this term is fashionable, it is undeniable that it is a technology that is impacting all organizations and will continue to do so in the coming years; So we wanted to give a brief introduction to the subject.
Machine learning is a technology that is in the field of artificial intelligence, where computer science and statistics are closely related, thanks to this, we will have the possibility that computer systems learn automatically, this has been possible thanks to the large volume of data We are currently generating. Approximately 90% of all existing data has been produced in the last 2 years. With this large amount of data it has become a challenge of great proportions to be able to extract knowledge from the data, it is there that Machine Learning becomes important since it has the ability to identify complex patterns in millions of data and behaviors that perhaps for the human beings would be impossible to find, by means of algorithms that review the data.
Why is Machine Learning Important?
As we said in the previous section, the amount of data generated by companies is growing exponentially, so being able to extract relevant and valuable information becomes a competitive advantage that we cannot fail to take advantage of, since it will allow us to anticipate situations that They still do not happen, find behaviors that for a human it would be impossible to try to predict them, and last but most importantly, to be able to make better decisions and develop better business strategies based on data.
Today the challenge of extracting knowledge from data has been simplified, thanks to the evolution of technology and adequate analysis, we will be able to create models to analyze high volume and complex data, providing reliable and agile results without human intervention.
It is clear that you should not only pay your attention to the volume of the data, it is not a requirement to have the amount of data that Google or Amazon have in order to generate relevant knowledge from them, it is better to have reliable and quality data than having millions of data from which no value can be extracted.
Machine Learning Applications
Basically, Machine Learning applications are infinite, they depend on the industry and the data you have available, here are some practical examples:
Predict machine failure in factories.
Recover portfolio according to the payment behavior of customers.
Select potential clients according to their behavior in networks.
Identification of diseases in the analysis of diagnostic images.
This technology is drastically changing the way companies act and make decisions, the data is there and the technology is also, now all that remains is to start applying it and obtaining great results from this new form of data-based decision-making.