Creating value for businesses through Artificial Intelligence (AI) has a fundamental basis that cannot be overlooked: what fuels AI. The revolution of this technology brought unimaginable benefits and completely transformed the way companies view data in their strategies. However, there is still an important path to travel for this truly transformative innovation to become genuinely relevant for companies. Many Artificial Intelligences are still fed with incorrect or very low-quality information. And, as a consequence, they only deliver results at the same level. The well-known concept ofgarbage in, garbage out"Enter trash, exit trash" has never been so true.
With advances in Generative AI and increased computational power, we are witnessing the generation of information and contexts on an extraordinary scale. To harness all this potential, using accurate and reliable data to underpin AI is key. After all, they are the fuel that feeds AI algorithms, and therefore, companies and organizations that do not invest in a solid data foundation may take longer to implement these solutions. Or worse. They can adopt the technology incorrectly and turn this initiative into a major problem.
For AI to produce accurate and useful results, the data supporting it must reflect the reality of the market and the company without errors or distortions. This requires them to be diverse, collected from different sources, to reduce biases and ensure that applications are less likely to make unfair decisions. Furthermore, it is necessary to consider the constant updating of information and its accuracy, because when they are outdated or incorrect, they produce imprecise answers, compromising their reliability. Updated data allows AI models to follow trends, adapt to multiple scenarios, and deliver the best possible results.
In the financial market, for example, incorrect data can lead to inadequate credit risk analyses and forecasts, resulting in the approval of loans for delinquent clients or the denial for good payers. In the logistics sector, outdated and poor-quality information causes distribution problems with out-of-stock product sales, leading to delivery delays. And, consequently, loss of clients.
Data security is also paramount. Leaving them vulnerable in AI applications is like leaving a vault door open, exposing them to theft of sensitive information or manipulation of systems to generate biases. Only through security is it possible to protect privacy, maintain the integrity of the model, and ensure its responsible development.
Data ready for AI also need to be identifiable and accessible within the system, or they will be equivalent to a library full of locked books. Knowledge exists, but it cannot be used. But, it is important to highlight here the importance of granting access to the correct people and areas. The same data can be accessed in its entirety by an area, that is, complete and detailed. In another, only access to the totalization of the data may be granted, in a summarized form. A certain data will not always be accessible to everyone in the same way. Identifiable information, possible through the use of business and technical metadata, reveals the true potential of machine learning and Generative AI, allowing these tools to learn, adapt, and generate innovative insights.
Finally, the data needs to be in the correct format for machine learning experiments or Large Language Model (LLM) applications. Facilitating the consumption of information helps unlock the potential of these AI systems, enabling them to ingest and process it smoothly and transform it into intelligent and creative actions.
The path to maximizing the potential of Artificial Intelligence in business inevitably depends on the quality of the data that feeds it. Companies and organizations that understand the importance of a robust, secure, and up-to-date database gain a competitive edge, turning AI into a strategic ally and a market differentiator. This new era of innovation we are experiencing requires companies to invest in the right ingredient — their data — to steer the AI engine in the right direction, bringing a new perspective to business.
