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Garbage in, garbage out: The essential role of data quality in Artificial Intelligence

04/01/2024

Our contemporary world is saturated with reflections and debates about Artificial Intelligence. From self-driving cars to user customizations, the capabilities promised by AI seem limitless. However, behind these amazing technological applications lies a less dazzling but vitally important issue: high-quality training data. If this is lacking, even the Artificial Intelligence more advanced They can fail due to their critical dependency on power data.

The fundamental value of data with optimal quality

Rectified and error-free data is the foundation for any successful AI application. The algorithms that constitute the AI feeds on data; They are able to identify patterns, make decisions and generate predictions based on the information absorbed. As a result, the quality of this data is of utmost importance for training AI.

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La poor data quality It can take multiple forms, from missing or incomplete data and inconsistent data with incompatible formats, to data that is not relevant and does not meet the company's requirements and goals. When this data enters the system of the Artificial Intelligence, the implications can range from minor inaccuracies to disasters operaserious tives. Wrong predictions can lead to bad decisions, and biased algorithms can damage reputations and even lead to legal disputes. Therefore, data cleaning strategies must be prioritized to take advantage of the wide range of potentials that the AI technology offers.

The contribution of AI to Data Quality Improvement

Although the problem of data quality may seem like a daunting task, we have reason to be optimistic. Paradoxically, the technology that is affected by data quality – AI – can play a cardinal role in its improvement. AI-powered data cleansing tools can detect and correct data anomalies. These tools are capable of identifying missing data, detecting inconsistencies, and eliminating superfluous entries, providing a uniquely measured and accurate representation of each data point. In addition, they excel in data unification, integrating and reconciling data from different sources in a compact and easy-to-assimilate format. In this way, AI transforms data cleaning from a daunting task to a simplified and automated process.

human analysis of the data generated by advanced AI algorithms is crucial to generating quality training data. Human intelligence effectively guides AI in selecting data to obtain an optimal result. The collaboration between AI and human knowledge ensures that the training data fed into AI models is of the highest quality, resulting in more robust and accurate AI systems. By adopting AI with human feedback into their data management strategy, organizations can maintain high-quality data, substantially improving the performance of their AI systems.

Data Products: Ensuring Data Quality from its Inception

The most effective way to avoid the risks associated with bad data is to ensure its quality from the beginning. This is where the data products. However, there is often confusion with the term “data product”, leading to different interpretations of its definition. To bring clarity to this debate, a data product is a ready-to-use, high-quality, reliable, and accessible set of data that individuals in a organization can use to solve business challenges. Organized by business entities and regulated by domains, data products are the premium version of data. These are complete, clean, selected and continuously updated data sets that are aligned with key entities such as customers, suppliers or patients, and that both humans and machines can consume in a secure and widespread manner within a company. Data products, powered by the efficiency of AI and with human supervision to provide feedback, play a key role in the data collection and management process, ensuring its quality and reliability.

At the epicenter of the AI ​​revolution, data quality becomes the master key that unlocks the full potential of AI. In the pursuit of data quality, AI-based data products emerge as the solution, ensuring accuracy and reliability. Investing in data quality is not an optional business choice – it is an essential commitment to the future of AI-based innovation. The key to avoiding the “poor input, poor output” cliché lies not in the sophistication of your AI, but in the quality of your data.

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