Experience with real data

Álvaro Muñoz


You already have everything you need to get down to work and start working with the company's data. After overcoming the first hurdles of dealing with real data, you set out to apply a algorithm of artificial intelligence with the most modern programming language and libraries, and after several hours waiting for the result you realize that you are far from the expected solution and you don't know how to improve it. 

Do not forget that solving real problems in the industry requires much more effort and experience. In this article you will find out the main difficulties of working with actual data and the key points you need to make the most of them, from arrival to departure.

Experience with real data

experience with real data. Artificial intelligence


The field of research is constantly developing new methods for handling data and extracting its information. During the training stage algorithms are taught at universities and tested on simple data sets apparently obtaining very good results. 

It is only necessary to deal with real industry problems to realize that running an algorithm is not enough and that a series of additional difficulties arise where experience plays a fundamental role in arriving at good solutions. 

The first difficulties begin with the arrival of the data. Initially, the available sets may not be ideal due, among many other things, to the format The data available either because they are incomplete, inconsistent, contain few records or variables, or otherwise have to deal with huge amounts of data. 

It is important to have the ability to receive data from different sources and to process it so that its format is optimal for the artificial intelligence. GAMCO has extensive experience in working with all kinds of data available to companies, from excel sheets to databases with more sophisticated structures with few or hundreds of millions of records. 

It should not be forgotten that in real problems, suppliers have interlocutors from the client company with whom communication is maintained throughout the process, initially to establish the data sources and carry out their automation. 

The experience with different sectors has allowed GAMCO to get to know and work with many types of real problems that are different from those encountered in the teaching stage, not only in terms of objectives and available data, but also in terms of the complexity of the different sectors and businesses. The effort to understand this complexity is crucial to bring new approaches and ideas, being able to enrich the knowledge through external search for information or requesting new data from the client. 

In addition, domain knowledge helps to squeeze the data and provide continuous improvement of the solutions based on Artificial Intelligence.


The most common is that the arrival of information is recurrent and through different files. fundamental to follow the traceability. This allows for greater control of the process and to know at all times the path followed.

This handling of information normally causes fear in companies because of share confidential information. Thus, work in a transparent and secure manner, providing data encryption that allows the use of Artificial Intelligence algorithms. seeing only coded data. Many are the companies that trust GAMCOThe confidentiality of the data is emphasized by working with data from the Ministry of the Interior, banks (Bankia, Santander, ...) and multinationals.

Not only security, but also quality and speed in the deployment of the solution must be achieved, requiring an established and validated process such as the one established by GAMCO in the large company, and now puts available to SMEs. In this way, efficient methods are used that are reviewed, researched and updated over time. 

A key feature of these methods is their robustness, which allows the same results to be reproduced throughout the development and deployment of the artificial intelligence-based solution. This, together with traceability, allows total control of the process.


The objective of the problems should not be to ask for data to extract only a predictive value, but to be able to provide a real solution that improves over time and that the customer can understand and access easily and safely.

This solution must be simple to the end user, allowing an understanding of the information without the need for the client to have statistical or advanced knowledge on the subject. This prediction is what the customer will use to take actions. 

Lord Kevin's quote: <<What is not defined cannot be measured. What is not measured, cannot be improved. What is not improved, is always degraded.>> clearly expresses the importance of measuring for improvement. It is essential to provide a visualization of the performance achieved and the impact of the solution. 

Finally, with the tools deployed and automated, the only thing left to do is to benefit from the gains obtained from exploiting the company's data, and to continue communicating with the customer in search of continuous improvement.

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