In recent years, data has become increasingly significant, especially considering the growing use of Artificial Intelligence (AI) across industries.
This is because every advancement in AI must be accompanied by quality data to support the model. In this sense, data is an indispensable asset for such projects and can ultimately drive—or hinder—their effectiveness and relevance.
In fact, according to the Latin American AI Index, “data is the fundamental component of AI systems, so greater availability, usability, and proper governance of data are closely related to a country’s potential to generate a healthy AI ecosystem.”
Examining the data subdimension of the same study, it was found that the countries excelling in this enabling factor were Brazil, Colombia, and Uruguay, the only ones scoring over 50 points.
On the other hand, Argentina, Chile, and Mexico scored 10 points above the regional average, which was 39.8 points. Ultimately, there is still much progress to be made at the Latin American level, but the good news is that advancements are being made.
Today, data is everything. AI algorithms are powerful tools capable of processing large volumes of information, learning patterns, and making autonomous decisions. However, their performance is strictly related to the information they receive: without data, AI would have nothing to learn from or extract knowledge.
Similarly, quality is far more important than quantity. It is useless to have 100 pages of incomplete and biased data versus one page of clean, precise, and representative figures.
Therefore, data cleanliness and integrity are crucial to ensure the AI project’s results are reliable and objective, making preprocessing a fundamental step.
I am referring to tasks such as data cleaning, outlier removal, normalization, and selection of relevant features. This way, the effectiveness and efficiency of AI algorithms can be maximized.
Data plays an essential role in ensuring any AI implementation, especially considering we are in a highly competitive digital era. It is crucial to recognize its importance and adopt practices that guarantee its quality and ethical use.
It is not just about investing in data acquisition. In many companies, for example, they are unaware of how to manage data, resulting in a project without a clear objective. According to the aforementioned study, using data for specific purposes is a cross-cutting challenge in all evaluated countries, highlighting the need for a method that allows consistent progress.
Here is where AI plays a fundamental role. With data and a clear objective of the information needed, the only thing missing is the tool and, of course, a professional team capable of developing or, in some cases, selecting the appropriate model.
This way, organizations will be able to identify how to create value more efficiently by practicing data-driven strategies. For example, in retail, companies can collect information from their customers to provide a much more personalized service: by simply entering their user account, the administrator can know what a particular consumer bought and when.
This helps to understand different segments and profiles, subsequently enhancing their loyalty. Based on preferences, AI can infer which other products might interest them and offer relevant offers and promotions.
In the tech industry, experts point out that data is the new oil, due to the relevance it is acquiring and will continue to have. The use of Artificial Intelligence will not stop, and we must be prepared to keep advancing with it.