*By Cesar Ripari
The creation of business value from Artificial Intelligence (AI) has a fundamental basis that cannot be overlooked: what feeds the AI. The revolution in this technology has brought unimaginable benefits and completely transformed the way companies view data in their strategies. However, there is still a long way to go before this absolutely transformative innovation is truly relevant to companies. Many Artificial Intelligences are still fed by incorrect or very low-quality information. And, as a consequence, they only deliver results of the same level. The well-known concept of “garbage in, garbage out“(Garbage in, garbage out) has never been truer.
With advances in Generative AI and the increase in computing power, we are witnessing the generation of information and context at an extraordinary volume. To harness this potential, using accurate and reliable data to support AI is key. After all, data is the fuel that powers AI algorithms, and so companies and organizations that do not invest in a solid data foundation may be slow to implement these solutions. Or worse, they may adopt the technology incorrectly and turn this initiative into a major problem.
For AI to produce accurate and useful results, the data that supports it must reflect the reality of the market and the company without errors or distortions. This requires that the data be diverse, collected from different sources, to reduce bias and ensure that applications are less likely to make unfair decisions. In addition, it is necessary to think about constantly updating the information and its accuracy, because when it is outdated or incorrect, it produces inaccurate responses, compromising its reliability. Up-to-date 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 result in inadequate credit risk analyses and forecasts, leading to loans being approved for defaulting customers or denied for good payers. In the logistics sector, outdated and poor quality information can lead to distribution problems with sales of out-of-stock products, causing delays in deliveries and, consequently, loss of customers.
Data security is also paramount. Leaving data vulnerable in AI applications is like leaving a safe door open, exposing it 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.
AI-ready data also needs to be identifiable and accessible in the system, or it will be the equivalent of a locked library full of books. The knowledge exists, but it cannot be used. However, it is important to emphasize 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, access may only be granted to the totality of the data, in summary form. A given piece of data will not always be accessible to everyone in the same way. Identifiable information, made possible by the use of business and technical metadata, reveals the true potential of machine learning and generative AI, so that these tools can learn, adapt and produce innovative insights.
Finally, data needs to be in the right format for machine learning experiments or Large Language Models (LLM) applications. Making information easy to consume helps unlock the potential of these AI systems, so they can ingest and process it smoothly and turn it into intelligent and creative actions.
The path to maximizing the potential of Artificial Intelligence in business inevitably involves the quality of the data that feeds it. Companies and organizations that understand the importance of a robust, secure, and up-to-date database will get ahead of the competition, transforming AI into a strategic ally and a market differentiator. This new era of innovation that we are living in requires that companies invest in the right ingredient — their data — to move the AI machine in the right direction, bringing a new perspective to business.
*Cesar Ripari, Pre-Sales Director for Latin America at Qlik
Notice: The opinion presented in this article is the responsibility of its author and not of ABES - Brazilian Association of Software Companies
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