For most high-tech manufacturers, managing your data is one of your greatest challenges—due to the large amounts of vast and complex heterogeneous technical data being generated at rapid speed. This data, once taken advantage of, has untapped potential to revolutionize operations—but that requires the right tools and expertise. Recent trends in generative AI and data science allow organizations to rethink their overall data analytics strategy, which experts at Semiconductor Digest Online cover in a recent feature.
Executives weigh in on generative AI
Applying traditional data science and machine learning (ML) approaches to extract value from this data has limitations which include: scalability issues, siloed data constraints, manual intervention, and model interpretability. To address these issues, companies have turned to advances in generative AI to up-bridge the gap and to aid domain specialists in applying more advanced data science and machine learning techniques to their workflows.
However, according to a 2024 survey by Pager Duty and Wakefield Research, 100 percent of the 1,000 executives surveyed expressed concerns about the security risks and ethical concerns associated with generative AI solutions. Out of those that were interviewed, all 1,000 execs believe that human intervention is essential to successfully implement generative AI, yet the extent of human involvement varies as 73 percent think it requires a moderate amount, and 23 percent believe only a low level is needed.
How can manufacturers stay ahead of the curve while maintaining security and including humans in the AI process? With visual data science.
4 ways visual data science empowers manufacturing
Visual data science puts the data at your fingertips. With it, you can empower engineers and management in high-tech manufacturing to make informed, data-driven decisions that enhance efficiency, quality, and profitability. You can also reduce the risk of production costs and performance requirements with advanced analysis.
Through real-world examples and case studies from experts in the field, find out in this short webinar how the Spotfire® visual data science approach enables:
- Proactive problem-solving: Shift from reactive troubleshooting to proactive issue prevention through data analysis and visualization.
- Data accessibility for all: Break down data silos and empower engineers and decision-makers at all levels with intuitive visualizations and interactive dashboards.
- Manufacturing excellence: Drive process optimization, quality assurance, anomaly detection, predictive maintenance, and root cause & corrective actions with powerful Spotfire analytics.
- AI-powered insights: Leverage Spotfire machine learning and AI capabilities, including the Spotfire Copilot AI tool, to gain deeper insights and streamline complex workflows.
Hear from semiconductor industry experts
Learn more about the tangible benefits of adopting a human and AI approach with a future look at the high-tech manufacturing industry from Spotfire experts featured on Semiconductor Digest Online.
At SEMICON Europe this month, our experts met with the Semiconductor Digest Online team where Spotfire was featured on the front cover of their most recent magazine issue. If you haven’t already received it, take a look at the most recent article on the importance of leveraging visual data science in semiconductor manufacturing.