AI Articles and Analysis about Data collection

Data collection is the process of gathering and measuring information on targeted variables in an established systematic fashion, which then enables one to answer relevant questions and evaluate outcomes.

Neurobiological and Cybernetic AI for Manufacturing, Part 1-min
Agricultural automation
Chemicals
Automation and robotics
Electronics
Data auditing
Heavy industry
Data collection
Manufacturing
Data integration
Data management
Discovery
Industrial automation
Process automation
Report generation
Research and development

Neurobiological and Cybernetic AI for Manufacturing, Part 1 – with Oleg Savin of Unilever

Modern manufacturing stands to benefit from integrating AI. The potential benefits are numerous, from improving efficiency and productivity by automating repetitive tasks to reducing unplanned downtime and cutting down on repair costs through predictive maintenance.
Understanding AI’s Expanding Role in Drug Discovery and Life Sciences R&D-min
Data auditing
Healthcare
Data collection
Pharma
Data integration
Data management
Drug discovery
Knowledge management
Research and development

Understanding AI’s Expanding Role in Drug Discovery and Life Sciences R&D – Liran Belenzon of BenchSci

This interview analysis is sponsored by BenchSci and was written, edited, and published in alignment with our Emerj sponsored content guidelines. Learn more about our thought leadership and content creation services on our Emerj Media Services page.
Tackling Chargeback Challenges at Scale with Machine Learning and Dynamic Arguments-min
Business intelligence and analytics
Consumer goods
Claim management
Finance
Compliance
Financial services
Customer analytics
Retail
Data analysis
Data auditing
Data collection
Data integration
Data management
E-commerce
Enterprise resource planning
Fraud protection
Predictive analytics and forecasting
Risk management

Tackling Chargeback Challenges at Scale with Machine Learning and Dynamic Arguments – with Roenen Ben-Ami at Justt

This interview analysis is sponsored by Justt and was written, edited, and published in alignment with our Emerj sponsored content guidelines. Learn more about our thought leadership and content creation services on our Emerj Media Services page.
Generative AI as a Catalyst for Enterprise Innovation_1x
Agentic Systems
Finance
Automation and robotics
Financial services
Business intelligence and analytics
Content Creation
Data auditing
Data collection
Data entry
Data integration
Data management
Document processing
Generative AI
Knowledge management
Large language models
Large Language Models
Machine learning infrastructure
model development
Predictive analytics and forecasting
Process automation
Report generation
Research and development
Training and development

Generative AI as a Catalyst for Enterprise Innovation – with Deborah Golden of Deloitte

Large enterprises' swift adoption of generative AI (GenAI) is driven by the quest for cost reduction and operational efficiency—but leaders risk falling behind competitors if these short-term gains stagnate without long-term innovation capabilities.
Keeping Up with AI Regulations
in a New Age of Data Privacy-min
Case management
Consumer goods
Claim management
Media
Customer experience
Software development
Customer service
Tech
Cybersecurity
Technology
Data auditing
Telecom
Data collection
Data integration
Data management
Identity theft
Security

Keeping Up with AI Regulations in a New Age of Data Privacy – with Leaders from OneTrust, Microsoft, and TELUS

This interview analysis is sponsored by OneTrust and was written, edited, and published in alignment with our Emerj sponsored content guidelines. Learn more about our thought leadership and content creation services on our Emerj Media Services page.
AI Regulation and Risk Management in 2024-min
Case management
Finance
Claim management
Insurance
Compliance
Data auditing
Data collection
Data integration
Data management
Enterprise resource planning
Risk management

AI Regulation and Risk Management in 2024 – with Micheal Berger of Munich Re

As AI adoption grows, so do the associated risks, including errors, biases, and unexpected outcomes. However, as AI becomes more integral to core business operations, managing operational and financial risks becomes paramount. One way to offset that risk is to ensure AI models. However, that can be difficult to achieve as the process is complex. 
Overcoming Barriers to AI Adoption in Telecom and Beyond-min
Data auditing
Media
Data collection
Telecom
Data integration
Data management
Knowledge management

Overcoming Barriers to AI Adoption in Telecom and Beyond – with Moutie Wali of Telus

This interview analysis is sponsored by OneTrust and was written, edited, and published in alignment with our Emerj sponsored content guidelines. Learn more about our thought leadership and content creation services on our Emerj Media Services page.
Lessons from Microsoft’s Responsible AI Journey-min
Agentic Systems
Software development
Business intelligence and analytics
Technology
Customer analytics
Cybersecurity
Data analysis
Data auditing
Data collection
Data integration
Data management
Generative AI
Machine learning infrastructure
Predictive analytics and forecasting
Research and development
Security
Software development

Lessons from Microsoft’s Responsible AI Journey – with Dean Carignan of Microsoft

This interview analysis is sponsored by OneTrust and was written, edited, and published in alignment with our Emerj sponsored content guidelines. Learn more about our thought leadership and content creation services on our Emerj Media Services page.

Data collection

Data collection is the process of gathering and measuring information on targeted variables in an established systematic fashion, which then enables one to answer relevant questions and evaluate outcomes.