The creation of value for business from Artificial Intelligence (AI) has a fundamental basis that cannot be neglected: what fuels AI. The revolution of this technology has brought unimaginable benefits and completely transformed the way companies see data in their strategies. However, there is still an important way to go so that this absolutely transformative innovation is really relevant for companies. Many Artificial Intelligences are still fed by wrong or very low quality information. And, as a consequence, they deliver only results at the same level. The well-known concept of “garbage in, garbage out” (in trash, out trash) has never been so true.
With advances in Generative AI and the increase in computational power, we are witnessing the generation of information and contexts in an extraordinary volume. To take advantage of all this potential, using accurate and reliable data to support AI is the key. After all, they are the fuel that nourishes AI algorithms and, therefore, companies and organizations that do not invest in a solid database can take time to implement these solutions. Or worse, they can adopt the technology in the wrong way and turn this initiative into a big problem.
For AI to produce accurate and useful results, the data that supports it needs to 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. In addition, it is necessary to think about the constant updating of information and its accuracy, because when they are outdated or incorrect, they produce inaccurate answers, compromising their reliability.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 bases can result in inadequate analysis and forecasts of credit risk, leading to the approval of loans to delinquent customers or denial to good payers. Already in the logistics sector, outdated and poor quality information generate distribution problems with sales of out-of-stock products, causing delays in deliveries.
Leaving them vulnerable in AI applications is like leaving the door of a vault open, exposing them to theft of sensitive information or manipulation of systems to generate bias. Only through security is it possible to protect privacy, maintain the integrity of the model and ensure its responsible development.
The data ready for AI also need to be identifiable and accessible in the system, or will be the equivalent of a library full of locked books. Knowledge exists, but it can not be used. But it is worth emphasizing here the importance of granting access to the right people and areas. The same data can be accessed in its entirety by one area, that is, complete and detailed. In another, can be released only access to the totalization of the data, in a summarized way. Not always a certain data will be accessible by everyone in the same way. The identifiable information, possible with the use of business and technical metadata, reveals the true potential of machine learning and of the Generative AI, can be adapted to produce these tools.
Finally, data needs to be in the right format for machine learning experiments or Large Language Models (LLM) applications.Easing information consumption helps unlock the potential of these AI systems so that they become able to ingest and process it smoothly and turn it into smart and creative actions.
The way to maximize the potential of Artificial Intelligence in business inevitably passes by the quality of the data that feeds it. Companies and organizations that understand the importance of a robust, secure and updated database get ahead of the competition, turning AI into a strategic ally and a market differential. This new era of innovation that we live requires companies to invest in the right ingredient 'Your data 'to move the AI machine in the right direction, bringing a new perspective to business.
