Data Quality Remains Biggest Detriment To AI Success - RTInsights
The quality of data coming through the pipeline remains one of the key impediments to AI success, but it looks like businesses are more aware of data needs.
What is the main challenge to AI success?
The quality of data being collected and analyzed is the main challenge to AI success. Poor data quality can lead to inaccurate results and inconsistent behavior of AI models, which ultimately affects trust from customers and stakeholders.
How can organizations improve data quality?
Organizations can improve data quality by establishing strong data governance, ethics, and standards. This foundational approach ensures that future AI development adheres to a consistent data collection and quality structure, which can enhance model performance and reduce failures.
Are companies becoming more data-driven?
Yes, there is evidence of a shift towards data-driven practices. A survey by NewVantage Partners found that 84.6% of companies now have a senior data leadership title, which is a 20% increase from five years ago. However, only about 20% of businesses consider themselves truly data-driven, indicating that hiring data executives alone is not sufficient for transformation.
Data Quality Remains Biggest Detriment To AI Success - RTInsights
published by Horizon Systems
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