Insights
Insights from the Industry
Why your generative AI systems are stupid
by David Linthicum
With over 30 years of experience in enterprise technology, David Linthicum is a globally recognized thought leader, innovator, and influencer in cloud computing, AI, and cybersecurity. Linthicum currently serves Global 2,000 clients as Deloitte’s Chief Cloud Strategy Officer. In this article, Linthicum addresses the challenges and limitations of current generative AI systems for delivering business value, highlighting recent public concerns over inaccurate responses from generative AI systems. The author notes that the quality of AI responses is directly dependent on the quality of the data used to train the model, emphasizing the need for clean and accurate data. Addressing data quality and bias issues is crucial for the successful implementation of generative AI systems in business. Read more
Real-world data quality: What are the opportunities and challenges?
by McKinsey & Company
Enterprises (including those in health care) need real world data—both internally and externally procured—to train new artificial intelligence-based applications. High quality data sets are required to enable robust analysis and deliver valuable insights; without these, firms experience AI failure. This article introduces several useful approaches for addressing data quality—particularly as associated with externally procured data. Read more
Data Quality in AI: Challenges, Importance & Best Practices in '24
by AIMultiple Research
Data quality has a critical role in artificial intelligence (AI). This article outlines its impact on the performance and reliability of AI models. This includes the challenges organizations face in ensuring data quality, including data collection, labeling, storage and governance, and then describes some best practices for delivering AI-ready data. Sempras consultants assist our clients with addressing these challenges—and deploying best practices to assure AI data readiness. Read more
“You can have all the fancy tools, but if [your] data quality is not good, you’re nowhere.” —Veda Bawo, Director of Data Governance, Raymond James
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