Unlock the Power of Your Data

In today's data-driven world, data has become the most valuable asset. However, raw data is of little value without proper classification and collection. This conference will provide you with the knowledge and tools to refine your data, ensuring it's accurate, secure, and ready to fuel your organization's success.

Why Attend?

Master Data Classification: Gain a deep understanding of data classification concepts, standards, and best practices.
Enhance Data Quality: Learn how to improve data quality through effective data collection and cleaning techniques.
Ensure Data Security and Privacy: Explore strategies to protect sensitive data and comply with regulatory requirements.
Leverage Advanced Techniques: Discover how machine learning and data integration can elevate your data management capabilities.
Network with Experts: Connect with industry leaders, data scientists, and cybersecurity professionals.

Key Topics:

Fundamentals of Data Classification:
Defining data sensitivity and regulatory compliance
Implementing effective data classification processes
Understanding data classification standards and frameworks
Best Practices for Data Collection:
Ensuring data quality and integrity
Ethical considerations in data collection
Exploring data collection strategies and techniques
Advanced Data Classification and Collection Techniques:
Leveraging machine learning for data classification
Data integration and fusion techniques
Data governance and security best practices

Don't miss this opportunity to transform your data into a strategic asset.

Primary Audience for Conference Attendees

AI and Data Science Professionals:

Data Scientists
AI/Machine Learning Engineers
AI Researchers
Software Engineers
Product Managers
UX Designers
Data Analysts
Business Intelligence Analysts
Database Administrators

Data Governance and Security Professionals:

Data Governance Officers
Data Privacy Officers
Chief Information Security Officers (CISOs)
Cybersecurity Analysts
Compliance Officers
Legal Professionals

Business and Industry Professionals:

Executives and C-Suite Leaders
Business Analysts
Project Managers
Marketing and Sales Professionals
Operations Managers
Financial Analysts

Academic and Research Community:

Researchers and Academics
Students and Graduate Students

Main Conference Subject Areas

• Foundations of Data Classification
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  • Welcome and Keynote: Conference overview and the evolving landscape of data.
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  • Understanding Data Sensitivity and Regulatory Compliance
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    • Data Privacy Regulations: GDPR, CCPA, HIPAA
    • Identifying Sensitive Data: A Practical Guide
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  • Data Classification Standards and Frameworks
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    • NIST Data Classification Standard: A Deep Dive
    • Industry-Specific Standards: PCI DSS, HIPAA, and More
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  • Implementing a Data Classification Process
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    • Data Inventory and Assessment: A Step-by-Step Approach
    • Classification Methodology and Tools: Best Practices
    • Continuous Monitoring and Review: Ensuring Data Security
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• Best Practices for Data Collection
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  • Data Quality and Integrity
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    • Data Cleansing and Standardization: A Comprehensive Guide
    • Data Validation and Verification Techniques
    • Data Quality Metrics: Measuring Success
  •  
  • Ethical Considerations in Data Collection
    •  
    • Informed Consent and Privacy: Legal and Ethical Implications
    • Bias and Fairness in Data: Addressing Algorithmic Bias
    • Ethical Guidelines for Data Collection: A Framework
  •  
  • Data Collection Strategies and Techniques
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    • Data Mining and Scraping: Best Practices and Challenges
    • API Integration: Connecting to Data Sources
    • Data Streaming and Real-time Processing: A Deep Dive
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  • Panel Discussion: Data Collection Challenges and Future Trends
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• Advanced Data Classification and Collection Techniques
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  • Machine Learning for Data Classification
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    • Supervised and Unsupervised Learning Techniques
    • Natural Language Processing for Text Classification
    • Computer Vision for Image and Video Classification
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  • Data Integration and Fusion
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    • Data Warehousing and Data Lakes: A Comparative Analysis
    • Data Integration Techniques: ETL, ELT, and More
    • Data Fusion and Enrichment: Combining Data Sources
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  • Data Governance and Security
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    • Data Governance Frameworks: Implementing Effective Governance
    • Data Security Best Practices: Protecting Sensitive Data
    • Data Access Controls and Permissions: Managing User Access
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  • Workshop: Hands-on Data Classification and Collection
    • Practical exercises on data classification, data quality, and data integration
    • Case studies and real-world examples

Experiencing Alta/Snowbird

Alta and Snowbird are two world-renowned ski resorts located in the Wasatch Mountains of Utah, connected by a single lift ticket. They are known for their exceptional snowfall, challenging terrain, and breathtaking scenery.

Alta is a historic ski area known for its steep slopes and deep powder snow. It's a popular destination for advanced skiers and snowboarders. Alta is also known for its no-frills atmosphere and its commitment to preserving the traditional skiing experience.

Snowbird is a larger resort offering a wider variety of terrain, including beginner and intermediate slopes. It's also home to the Cliff Lodge, a luxurious hotel with stunning mountain views. Snowbird is popular for families and groups of friends.

Both resorts are part of the Cottonwood Canyons, renowned for incredible skiing and snowboarding conditions. They are just a few miles apart and easily accessible from Salt Lake City International Airport, a short 45-minute drive away. 
 

Here are some cool adventures:

Heli-Skiing Adventure: Experience the ultimate powder skiing adventure with a helicopter tour to remote, pristine snowfields.
Hiking: Explore the stunning alpine scenery on trails ranging from easy to challenging.
Mountain Biking: Tackle thrilling downhill trails or cruise on scenic cross-country routes.
Scenic Tram Rides: Soar to the summit of Hidden Peak for breathtaking views of the Wasatch Mountains.
Alpine Slide: Race down the mountain on a thrilling alpine slide.
Wildflower Viewing: Witness the vibrant beauty of wildflowers in bloom throughout the canyons.

Heli-Skiing Adventure (Optional)

Experience the ultimate powder skiing adventure with a helicopter tour to remote, pristine snowfields.

Join us for a thrilling day of heli-skiing. We will fly you to remote, pristine snowfields where you will experience the ultimate powder skiing adventure. Our experienced guides will lead you to the best terrain, ensuring a safe and unforgettable experience.

Call for Presentations & Papers

This pivotal conference will bring together leading experts, practitioners, and researchers to explore the foundational and advanced techniques critical for effective data management in today's complex digital landscape.

We invite you to share your knowledge and contribute to a vibrant discussion on best practices, innovative solutions, and future trends in data classification and collection. We are seeking compelling presentations, insightful papers, interactive workshops, and concise lightning talks that offer original research, practical applications, and thought-provoking analyses.

Main Conference Subject Areas

Foundations of Data Classification

This track will lay the groundwork for understanding and implementing robust data classification strategies. We encourage submissions on:

• Welcome and Keynote: Proposals for an opening keynote addressing the evolving landscape of data and the increasing importance of effective data classification.
• Understanding Data Sensitivity and Regulatory Compliance: Deep dives into Data Privacy Regulations such as GDPR, CCPA, and HIPAA, along with practical guides for identifying sensitive data.
• Data Classification Standards and Frameworks: Comprehensive discussions on established standards like the NIST Data Classification Standard and industry-specific mandates such as PCI DSS and HIPAA.
• Implementing a Data Classification Process: Step-by-step approaches to data inventory and assessment, best practices for classification methodology and tools, and strategies for continuous monitoring and review to ensure ongoing data security.

Best Practices for Data Collection

This track will cover essential considerations and techniques for acquiring high-quality and ethically sourced data. We are particularly interested in:

• Data Quality and Integrity:
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  • Data Cleansing and Standardization: Comprehensive guides to processes that ensure data accuracy and consistency.
  • Data Validation and Verification Techniques: Methods for confirming the reliability and correctness of collected data.
  • Data Quality Metrics: Approaches to measuring and improving the success of data collection efforts.
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• Ethical Considerations in Data Collection:
  •  
  • Informed Consent and Privacy: Discussions on the legal and ethical implications of data collection.
  • Bias and Fairness in Data: Strategies for addressing and mitigating algorithmic bias in data.
  • Ethical Guidelines for Data Collection: Frameworks and principles for responsible data acquisition.
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• Data Collection Strategies and Techniques:
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  • Data Mining and Scraping: Best practices and challenges associated with extracting data from various sources.
  • API Integration: Connecting to and leveraging external data sources through APIs.
  • Data Streaming and Real-time Processing: In-depth explorations of acquiring and processing data as it is generated.
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• Panel Discussion: Proposals for a panel discussion focusing on current and future Data Collection Challenges and Future Trends.

Advanced Data Classification and Collection Techniques

This track will explore cutting-edge methodologies and technologies for enhanced data classification and integration. We welcome submissions on:

• Machine Learning for Data Classification:
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  • Applications of Supervised and Unsupervised Learning Techniques.
  • Leveraging Natural Language Processing for efficient Text Classification.
  • Utilizing Computer Vision for robust Image and Video Classification.
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• Data Integration and Fusion:
  •  
  • A comparative analysis of Data Warehousing and Data Lakes.
  • Advanced Data Integration Techniques including ETL, ELT, and modern approaches.
  • Strategies for Data Fusion and Enrichment, combining diverse data sources for greater insight.
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• Data Governance and Security:
  •  
  • Implementing effective Data Governance Frameworks.
  • Data Security Best Practices for protecting sensitive information.
  • Strategies for Data Access Controls and Permissions to manage user access effectively.
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• Workshop: Proposals for hands-on sessions covering practical exercises on data classification, data quality, and data integration, along with real-world case studies.

Submission Types

We invite diverse contributions to enrich our program:

• Oral Presentations (20 minutes): Share your research findings, innovative applications, case studies, or best practices in a focused presentation.
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• Technical Papers (Full Length, 8-12 pages, IEEE format): Submit original, unpublished research that will undergo a rigorous peer-review process. Accepted papers will be published in the conference proceedings.
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• Experience & Insight Papers (4-6 pages, formatted for readability): This category is for practitioners, industry leaders, and innovators to share valuable lessons learned, practical case studies, real-world implementations, and insightful perspectives on the challenges and successes encountered in data classification, collection, and governance. These papers should focus on practical applications, best practices, and actionable insights. Submissions will be peer-reviewed for clarity, relevance, and practical value.
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• Poster Presentations: Visually showcase preliminary results, ongoing research, or innovative concepts. There will be a dedicated poster session for interactive discussions.
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• Panel Proposals (60 minutes): Suggest and moderate a discussion among 3-5 experts on a controversial, emerging, or complex topic within data classification, collection, or governance.
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• Workshop Proposals (60 minutes): Propose an interactive, hands-on session focused on practical skills, tools, or methodologies related to data classification, collection, data quality, or data integration.

Submission Guidelines

• Abstract: All submissions (except workshop proposals) must include a concise abstract (maximum 300 words) summarizing the problem, approach, key findings/insights, and conclusions.
• Author Information: Include full names, affiliations, and a brief professional biography (max 100 words per author).
• Keywords: Provide 3-5 relevant keywords that best describe your submission.
• Originality: Submissions must represent original work that has not been previously published or is not currently under review elsewhere.
• Audience Consideration: Presenters should be prepared to convey complex technical or theoretical concepts clearly to a diverse audience, including both technical and non-technical attendees.
• Formatting: Specific formatting guidelines for full papers will be provided upon the submission portal opening.

Review Process

All submissions will undergo a rigorous peer-review process by the Program Committee, comprising leading experts in data management, data science, cybersecurity, and related domains. Submissions will be evaluated based on:

• Relevance to conference themes
• Originality and novelty of contributions
• Technical merit and soundness (for technical papers)
• Clarity, organization, and presentation quality
• Potential impact and practical applicability

We eagerly anticipate your contributions and look forward to building a comprehensive and insightful program on mastering data!