Generative AI and data quality: a virtuous circle for innovation
Gold medals!
Few events are worthy of praise. Yet there are some that stand out by virtue of their notoriety, audiences and themes. These events were moments of intense exchange, where we discussed a key issue: the criticality of data quality in the success of artificial intelligence projects, and more specifically generative AI.
For us, two highlights marked the first half of the year:
Vivatech (Porte de Versailles exhibition center), where we had a stand in partnership with French Tech Grand Paris. We had the opportunity to meet a large number of companies, all focused on innovation and Artificial Intelligence in particular. Many of these companies are looking to move from POC (Proof Of Concept) to large-scale industrial projects. We were delighted to see that these companies' assessment of their POCs is that data quality is critical to the success of their AI projects.
Data Days in Deauville (Republik IT), during which we were able to discuss the many issues facing Chief Data Officers in implementing high value-added Data and AI projects. We were able to measure, year on year, the spectacular increase in the level of maturity of the companies we met: the numerous experiments they have launched in recent months have convinced them that success in the field of AI depends on controlled data governance, as well as efficient management of data quality issues.
The days when we struggled to convince people that identifying and resolving data quality issues was a key success factor are long gone, and we're delighted.
The virtuous circle between AI and data quality
To deliver results, AI, and in particulargenerative AI, needs reliable data. Interestingly, AI also enables data quality problems to be identified and corrected much more effectively. Thanks to the possibilities offered bygenerative AI and RAG (Retrieval-Augmented Generation), we can:
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Transform data via natural language instructions
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Quickly identify anomalies
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Better match similar data (deduplication, fuzzy joins)
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Automatically correct inconsistencies
This continuous improvement loop is a virtuous circle: classical and generative AI increase the power of Tale of Data's Data Quality algorithms, enabling you to produce much more reliable data and, consequently, more effective AI models. At Tale of Data, we see this every day, and our discussions at Vivatech and Data Days have only confirmed it.
An ongoing commitment to the future
At Tale of Data, we work daily to integrate the latest generative AI and classical AI algorithms into our platform, to make the identification and resolution of data quality problems ever simpler and more efficient.
This autumn promises to be particularly rich in innovation, with a host of new features to come, and we look forward to sharing them with you.
Jean-Christophe Bouramoué
CTO of Tale of Data
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