Generative AI and Data quality: Can they be good friends?
Some may disagree, but Generative AI and data quality can work well together, and be good friends. Quick Look: Even though some may think differently, Generative AI and data quality can definitely get along. Dr. Abrar Abdulnabi, Head of Artificial Intelligence at Saal AI, draws an analogy between data and fuel for AI engines. Just […] The post Generative AI and Data quality: Can they be good friends? appeared first on SAAL.
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Some may disagree, but Generative AI and data quality can work well together, and be good friends.
Quick Look:
- Organizations should implement firm processes and regulations to prepare for using generative AI.
- As Generative AI improves, people will expect it to provide personalized results that fit their needs or adhere to ethical standards.
- Because there are strict rules about how data can be used, it’s important to follow them carefully.
Even though some may think differently, Generative AI and data quality can definitely get along.
Dr. Abrar Abdulnabi, Head of Artificial Intelligence at Saal AI, draws an analogy between data and fuel for AI engines. Just as the quality of fuel affects a car’s performance, the quality of data significantly impacts how well AI systems function. Neglecting data quality or lacking a clear data management strategy can limit the effectiveness of Generative AI. However, with proper organization and data management practices, Generative AI can become a powerful tool, providing a competitive advantage in various industries.
On the other hand, Mr. Kiran, a data scientist at Saal AI, emphasizes the critical role of data quality in AI system performance. He explains that high-quality data leads to more accurate and reliable results from AI, including Generative AI.
Potential Challenges:
Wessam AbuOrabi, Business Development Manager at Saal AI, emphasizes the risks associated with seeking quick fixes when addressing complex issues requiring data analysis. He cautions that decisions made without a comprehensive understanding of the data can have significant consequences, impacting both business planning and strategic decision-making.
For example, consider a scenario where a company hastily launches a new product based on incomplete or inaccurate market data. This impulsive decision could lead to financial losses and harm the company’s reputation if the product fails to meet consumer expectations. Therefore, prioritizing thorough data analysis and understanding is essential before making critical decisions.
Looking Ahead:
In the future, businesses will need to be ready to use both Generative AI and quality data. Vikram Poduval, CEO of Saal, believes that companies will start to bring together their IT, risk, and data teams to ensure they’re collecting, managing, and using data safely and properly. By following strict privacy rules and managing data well, companies can keep data safe while still using it to train AI.
As Generative AI improves, people will expect it to provide results that fit their needs or adhere to ethical standards. This means we need high-quality data that respects people’s privacy and ethical standards. With regulations like GDPR and the AI EU Act, Generative AI developers must use data carefully and comply with the law. They need to ensure that the data they use is of good quality and that they’re using it in a way that’s legal and ethical.
In short, Generative AI and quality data aren’t just good for convenience – they’re essential for ensuring AI works well and remains ethical in the future.
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The post Generative AI and Data quality: Can they be good friends? appeared first on SAAL.
