Summary
The quality of the data is crucial for the success of artificial intelligence projects, since the inconsistent data of compromises increase costs and compromise the efficiency, which require strategic and rigorous information of the information.
Imagine trying to maintain a high -performance team by giving it by nourishing it every day only with fast food. Initially, energy seems sufficient, but over time the results fall, health deteriorates and failures appear in the most critical moments. This is exactly what happens to artificial intelligence projects fueled by low quality data. Advanced like the algorithm, without consistent, reliable and reliable information, it is unlikely that the project will provide the expected performance.
According to Gartner, by 2026, 60% of artificial intelligence projects risk fail with precision due to the lack of adequate data that effectively support their applications. That is: the problem is almost never in technology, but on the basis that nourishes it. Investing in cutting -edge solutions without taking care of the origin, structure and relevance of data is like installing a cutting -edge engine and feeding it with adulterated fuel.
Today we live the age of information excess, where the volume does not mean value. Many companies have transformed their data pipes into redundant, disorganized and doubtful information deposits. The result is the models that at the beginning seem to work well, but they fail when necessary – in strategic decisions, adverse scenarios or sensitive situations.
The impact of this reality goes beyond technical issues. Negative data directly compromise the financial efficiency of the projects. Without quality, artificial intelligence becomes an expensive, fragile and expensive solution, consuming time and resources to correct the analysis, review the results and evade the faults. In practice, it is like maintaining an operation that seems agile, but generates silent damage and compromises the company’s competitiveness.
The challenge is not only in gross data, but in the absence of a culture of curator and responsibility for the quality of the information. The data must not only be collected, they must be processed, validated and contextualized to achieve strategic objectives. It is the role of leaders to ensure that the information used is relevant, updated and without distortion. Without this rigor, companies continue to feed their systems with distorted content and low value.
And is it worth it: how many times your team uses easy and accessible database, leaving aside hygiene and the qualification process, just to accelerate deliveries? In the short term, this seems to solve. In the medium and long term, performance decreases, the risks increase and maintenance costs – such as those who use delivery every day, until the account and consequences arrive. The next great revolution in IA will not only feel from new models, but from maturity in data management and quality. It is time to put it at the center of the strategy.
Anderson Paulucci is CDO and co-founder of Triggo.ai, Data Analytics and startups.
Source: Terra
Rose James is a Gossipify movie and series reviewer known for her in-depth analysis and unique perspective on the latest releases. With a background in film studies, she provides engaging and informative reviews, and keeps readers up to date with industry trends and emerging talents.
