Tag: Machine Learning
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Business Intelligence in the Era of AI
Svetla Yankova, Business Intelligence Director at Progress, explores some of the ways Artificial Intelligence (AI) impacts Business Intelligence and explains why AI is so important to her, and to all of us. -
Metadata Mastery: Empowering Business Leaders for Collaboration and Consistency
Progress Semaphore propels CIOs and data leaders into a future where metadata-driven insights are table stakes for effective transformations and enhanced collaboration. -
Unintentional Biases in Big Data: The Case of Bank Loans and Insurance
Underwriting, lending and insurance premium calculations are some examples of where the financial and insurance industries need to keep a watchful eye for data bias. -
The Role of Policy-Driven Solutions in Ethical Decisioning
Having the right policies in places can help combat bias in AI/ML decisioning. Follow these practices to craft an ethical automated decisioning solution. -
Understanding Data Bias and Its Impact on AI/ML Decision Making
With the growing prevalence of AI/ML, it’s vital we understand how data may be biased and what this means for business decisioning. -
The Role of Generative AI in Your Modern Digital Experiences: Five Technical Considerations You Can’t Ignore
Generative artificial intelligence (AI) has the potential to revolutionize customer experiences, but businesses should also consider its technical implications. -
What is Hyperautomation and How Does It Impact Key Industries?
Hyperautomation is emerging as a powerful solution that unlocks digital value and productivity and transforms employee and customer experiences. -
Opportunities for Decision Automation in the Energy Field
Automation can improve energy production and distribution, allowing for better renewable energy integration and forecasting, and optimizing energy production and consumption. -
Survey: 65% of Execs Believe Data Bias Exists in Their Organizations
Data bias is becoming an increasingly pressing issue for businesses that leverage artificial intelligence and machine learning, but many organizations struggle to address it effectively.
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