At the Modern Data Quality Summit (MDQS) 2024, we believe that AI readiness starts with high-quality data. Our mission is to empower data leaders with the tools and knowledge to ensure their data is AI-ready.
Industry Sessions
Hours of Networking
Industry Experts
Attendees
The Modern Data Quality Summit 2024 is a 2-day event, catering to the specific needs of both business and technology professionals. Whether you’re a strategic leader or a hands-on practitioner, you’ll find sessions designed to empower your unique role in the data ecosystem.
Forge your data strategy & drive growth. Get the insights you need to govern your data landscape, lead with confidence, and drive results
Deep dive into the technical trenches. Discover the tools & techniques to build your AI-ready data infrastructure. Learn from industry experts and propel your AI initiatives
Deep dive into the technical trenches. Discover the tools & techniques to build your AI-ready data infrastructure. Learn from industry experts and propel your AI initiatives
The Modern Data Quality Summit 2024 is a 2-day event, catering to the specific needs of both business and technology professionals. Whether you’re a strategic leader or a hands-on practitioner, you’ll find sessions designed to empower your unique role in the data ecosystem.
DQLabs
Eckerson Group
CapitalG
DataVox
Adobe
United Community Bank
DQLabs
Prima
SanjMo
DBP
Dataception
data.world
SecureCare
American AgCredit
Raj Joseph, CEO of DQ Labs, makes a strong case for preparing data for AI use. He shares startling statistics about AI adoption and success rates, showing why good-quality data matters so much for AI to work well. He goes onto present a new way to think about getting ready for AI, covering important areas that many organizations miss. Watch how Raj explains a practical step-by-step approach to prepare for AI, making listeners curious about how they might use these ideas in their own work to make AI projects more successful.
DQLabs
As organizations navigate the complexities of data governance and quality, this session provides crucial insights into effectively leveraging AI while addressing its inherent challenges. Wayne Eckerson discusses the intersection of data quality, governance, and AI’s impact, emphasizing the challenges Chief Data Officers face in maintaining their roles amid rapid technological evolution, particularly with the potential and risks of generative AI. He also explores various data products and how organizations can use them to manage decentralized data effectively.
Eckerson Group
In this session, Bruno Aziza, partner at CapitalG, explores the critical intersection of AI, data quality, and governance in today’s enterprise landscape. Drawing from his extensive experience with tech giants and startups, Aziza delves into the heightened importance of data quality in the AI era and crucial aspects of AI governance throughout the model lifecycle. He discusses key use cases driving AI adoption in enterprises, emerging trends like multimodal AI and agentic frameworks, and strategies for balancing innovation with data integrity. This session is essential for data leaders, CTOs, and innovators looking to harness AI’s potential while maintaining robust data practices. Aziza’s insights, coupled with practical advice for entrepreneurs navigating the AI revolution, will help attendees future-proof their data strategies and drive meaningful AI adoption in their organizations.
CapitalG
Data quality is a critical concern in the age of big data and AI. As organizations increasingly rely on data-driven decision-making, ensuring the accuracy, completeness, and relevance of data becomes paramount. This is particularly challenging with unstructured data, which constitutes a significant portion of business information. Hear from our star speaker at MDQS 2024, Bill Inmon, the father of data warehousing, as he discusses the challenges and importance of unstructured data quality. You’ll find his insights on the business value of textual analytics and the nascent state of data governance for unstructured data particularly interesting.
DataVox
Session overview:
In the era of AI, data quality has become a critical factor for successful implementations. A study by MIT Sloan Management Review found that 85% of executives believe AI will offer significant competitive advantages, yet only 39% have an AI strategy in place. This gap highlights the importance of foundational elements like data quality. Jessica Talisman’s focus on taxonomies, thesauri, and metadata quality aligns with best practices in information architecture for AI. Her emphasis on structured data for AI systems shows the importance of well-organized data in improving machine learning model performance. Watch the session to learn her recommendations and tactics for continuous improvement and incremental addressing of metadata challenges, reflecting the iterative nature of AI development and deployment.
Adobe
Mark Mullins, Chief Data Officer (CDO) of United Community Bank (UCB), provides valuable insights into the evolving landscape of data management in the banking sector. He discusses UCB’s growth journey and the critical role of data in maintaining their award-winning customer service. Mullins delves into the complexities of data risk management, highlighting the need for cross-functional collaboration in today’s digital banking environment. He shares intriguing perspectives on balancing technological advancements with regulatory compliance and the human touch in banking. Mullins also offers a glimpse into how emerging technologies like AI are reshaping data strategies, leaving the audience curious about the future of data management in finance.
United Community Bank
DQLabs
Organizations grapple with data quality challenges that cost billions annually as we move towards a data-driven world. Despite its critical importance, many struggle to implement effective solutions, often citing resource constraints. In this insightful session, Kinda El Maarry, Director of Data Governance and BI at Prima, presents a practical approach to improving data quality without expensive tools or extensive infrastructure. Drawing from her experiences at Prima and HelloFresh, Maarry emphasizes that data quality is everyone’s responsibility. She introduces strategies to address the “black box” problem between data producers and consumers, highlighting the concept of data contracts. The presentation offers actionable tactics for establishing ownership, creating decision frameworks, and improving communication. Maarry also demonstrates how leveraging existing infrastructure and open-source technologies can be effective.
Prima
Data governance has become increasingly critical in the era of AI and big data. It ensures data quality, security, and compliance, which are essential for reliable AI systems. Sanjeev Mohan’s “layer cake” approach to data governance offers a comprehensive framework to address this need. The six layers he presents form a holistic strategy for managing data assets. By integrating these layers, organizations can create a robust foundation for their AI initiatives. Join in to learn how his industry-approved best practices work.
SanjMo
As data volumes grow and diversify, ensuring accuracy, completeness, and relevance becomes increasingly challenging, particularly with unstructured data. In this session, Prashant discusses the economic impact of data management, categorizes data types, and debunks common quality myths. He also introduces 12 key AI patterns for improving data quality, covering aspects like profiling, integration, and governance. The presentation also emphasizes how improved data quality drives better decision-making, growth, and operational efficiency.
DBP
In the rapidly evolving world of AI and data analytics, organizations face the challenge of effectively translating business needs into actionable insights. John Cook, CTO of Data Section, addresses this in his presentation on creating end-to-end AI-driven business solutions. He introduces the data product pyramid, a framework for mapping business objectives to analytics components, emphasizing rapid prototyping and continuous iteration. The session covers various AI tools beyond large language models, showcasing their applicability through a real-world credit scoring case study. Cook also previews their technology canvas for visually building data product pipelines. This comprehensive approach bridges the gap between business and technical teams, enabling organizations to deliver value quickly while adapting to changing market conditions, ultimately driving competitive advantage in today’s data-centric business landscape.
Dataception
Alex Gorelik discusses the importance of data classification in enhancing data quality, governance, privacy, and discoverability. He highlights the importance of semantic understanding for efficient data quality rules and privacy policy implementation. He emphasizes the need for automated classification techniques to handle the scale and evolving nature of data. Gorelik introduces VALDISCO, an open-source tool for detecting complex relationships between fields. He advocates for a combination of classification approaches, balancing methods based on use cases, and integrating with change management for optimal performance in cloud environments. The session encourages further exploration and collaboration on data classification techniques and use cases.
Knowledge graphs have emerged as a powerful tool in enhancing enterprise AI strategies, particularly in addressing challenges related to data integration, context, and explainability. Juan Sequeda’s exploration of knowledge graphs in enterprise AI aligns with this trend. His emphasis on using knowledge graph data catalogs to create trusted AI data experts addresses a critical need in the industry. The use of an insurance domain schema as an example highlights the practical applications of these concepts across various industries, demonstrating the versatility and potential of knowledge graphs in enterprise AI strategies.
data.world
In the era of big data, healthcare organizations are increasingly relying on analytics to improve patient care, reduce fraud, and optimize operations. However, the quality of these insights is only as good as the data they’re based on. Data quality and governance have become paramount in ensuring accurate, reliable, and actionable healthcare analytics. Drawing from his experience in both large corporations and smaller firms, Rokcon highlights the challenges of data silos and the advantages of cohesive data management teams. He emphasizes the need for robust processes and tools, including the potential of AI, in maintaining data quality. This session provides valuable insights for healthcare organizations looking to harness the power of their data, improve patient care, reduce fraud, and optimize operations through effective data quality and governance practices.
SecureCare
In this insightful session, Nick Morey, Director of Data Stewardship at American Ag Credit, shares practical strategies for building a business-relevant data quality program. Tune in to see him outlining how to build a process for developing data quality rules that empower decision-making and drive value.
American AgCredit
