Name: Manage Data for the New Era with Emerging Technologies
Start: 2025-05-14T15:00:00Z
End: 2025-05-14T15:00:33.000Z
Location: BrightTALK
Rating: 5

Data is the foundation of any organization and therefore, it is paramount that it is managed and maintained as a valuable resource. 

Subscribe to this channel to learn best practices and emerging trends in a variety of topics including data governance, analysis, quality management, warehousing, business intelligence, ERP, CRM, big data and more.

Customer retention has never been more important - or challenging. With rising acquisition costs, keeping the customers you already have is paramount, but they're constantly bombarded with competing offers. The key to standing out and fostering loyalty lies in delivering personalized experiences that make them feel valued and understood. Are you truly leveraging your customer data to create these stickier relationships?

The surge of customer data coupled with fragmented digital touchpoints presents a complex challenge. Many companies are overwhelmed - struggling to translate raw data into actionable insights and personalised experiences that resonate with individual customers. Traditional marketing tactics fall short, leading to missed opportunities to build lasting relationships, drive loyalty, and achieve sustainable growth. This is precisely why embracing a connected customer strategy has never been more critical.

Join Anna Samkova, Co-Founder & Strategic Advisor at Albany Advisory, for this session to explore how to unlock exponential growth by turning customer data into personalized experiences with the C.L.I.E.N.T. Growth Framework.

Key Takeaways:

- Implement the C.L.I.E.N.T. Growth Framework to transform raw customer data into a powerful engine for personalization
- Discover proven strategies to activate data in ways that feel personal, purposeful, and commercial
- Gain actionable insights into what truly meaningful personalization looks like in 2025 and beyond – and how to deliver it effectively
- Identify and avoid common personalization pitfalls that can damage customer trust and undermine your ROI
- Understand how to build a connected customer strategy that drives customer lifetime value, loyalty, and sustainable revenue growth

The C.L.I.E.N.T. Growth Framework: Your Roadmap to Personalized CX Success

Sustainable business relationships center on both long-term collaboration and minimizing environmental impact throughout the supply chain. Effective manufacturing and supply chain processes are increasingly reliant on transparency, adaptability, and digital tools that allow for predictive, rather than reactive, planning.

This session presented by CEO Justin Newell will demonstrate -- using a few examples -- how organizations such as Volkswagen have leveraged generative AI and intelligent decision-making systems to enhance sustainability and efficiency within logistics, supply chain, and manufacturing. 

AI is most powerful when it processes dozens or even hundreds of factors simultaneously -- far beyond the capacity of human decision-making or traditional systems. AI-driven optimization engines allow companies to simulate countless supply chain scenarios in real time, identifying the most sustainable and efficient outcomes.

When manufacturing and logistics processes are optimized through AI, delays are minimized, expectations are met more consistently, and customer satisfaction rises due to great transparency and on time delivery performance. The transparency and speed that come from real-time data and predictive analytics foster trust and reliability -- both cornerstones of sustainable business relationships.  Solutions such as this not only reduce operational complexity but also help reach environmental and service quality goals more effectively.
This can lead to more sustainable business relationships — both in longevity and from an environmental impact -- and increased customer satisfaction. 

Key Takeaways 
1. Trends in AI for 2025 and 2026.
2. Real-world examples of AI/ML in action.
3. How complex logistics can be better managed with AI and digital decision making.
4. Innovative solutions to improve manufacturing operations and product lead times with AI.

Reshaping the Role and Impact of AI in Manufacturing and Supply Chain Logistics

In the era of zettabyte scale information, organizations often find themselves “data rich but insight poor.” While data volume has exploded, the ability to translate that data into competitive advantage lags behind. The consequence? Missed opportunities, inefficient operations, and a failure to capitalize on the full potential of today's technology.

But technology alone is not enough. True scalability demands a people‑first culture that champions collaboration, shared data definitions, and continuous learning. By aligning technical choices with clear business goals and fostering an environment where data informs every decision, companies can bridge the gap between big data and big insights - moving from dashboards that describe the past to systems that actively shape the future.

In this session, we delve into the challenges preventing organizations from maximizing their data investments. We explore how to bridge the gap between raw data and strategic insights by building scalable, purpose-built systems that leverage cutting-edge technologies like AI and machine learning. Discover practical approaches to data integration, governance, and analytics that empower your teams to make data-driven decisions, optimize processes, and unlock new revenue streams.

Join Dmytro Basan to discover how to turn your data into a strategic weapon.

Key takeaways:

- Define Clear Business Objectives: Align your data strategy with business goals to ensure a measurable ROI.
- Build a Scalable Data Foundation: Invest in a modern data architecture that can handle growing data volumes and diverse data sources.
- Embrace Automation and AI: Leverage AI-powered analytics to automate insights discovery and accelerate decision-making.
- Promote Data Literacy: Empower your employees with the skills and tools they need to access, analyze, and interpret data effectively.
- Measure and Iterate: Continuously monitor the performance of your data initiatives and adjust your strategy based on results.

Unlocking Data's Potential: Scaling Systems for Real Insights

In today's fast-moving digital world, data has become one of the most valuable assets for any organization. However, simply collecting data is no longer enough. To fully unlock its potential, businesses must focus on making their data "AI-ready"—meaning clean, organized, accessible, and trustworthy for advanced analytics and artificial intelligence. This presentation explores the critical components of AI-ready data, emphasizing practical steps for effective data management, advanced reporting capabilities, and how these elements together drive real competitive advantage.

Through a mix of strategy, real-world examples, and simple explanations, this session highlights how organizations can move from traditional data practices to modern, intelligent systems that enable faster decisions, deeper insights, and more responsive customer experiences. Whether you're a business leader, data professional, or technologist, this talk offers actionable guidance on preparing your data ecosystem to meet the demands of AI-driven innovation.

Key Takeaways

- AI-Ready Data Defined: Understand what it means to have data that is structured, cleaned, and governed for use in AI systems.
- Data Management Strategy: Learn the essential practices for collecting, integrating, cleaning, and securing data across the enterprise.
- Smarter Reporting: Discover how AI-ready data enables predictive insights, real-time dashboards, and automated reporting.
- Competitive Edge: See how leading companies use AI-ready data to innovate faster, personalize services, and make smarter decisions.
- Actionable Steps: Walk away with a roadmap to begin transforming your data landscape for AI-readiness—starting today.

AI-Ready Data: Management, Reporting and Competitive Advantage

In an era where GenAI is proclaimed as both the savior and destroyer of traditional database development, organizations face a critical challenge: how to effectively leverage AI while maintaining SQL performance and expertise. 

This presentation cuts through the hype to explore how AI can serve as a bridge between expert and everyday developers. Through scenarios based on common industry challenges, we'll examine the potential pitfalls and opportunities in combining AI with traditional SQL expertise, including how seemingly minor query differences can lead to significant performance and cost implications in cloud environments. Drawing from industry observations and emerging practices, we'll examine concrete strategies for using AI to accelerate junior developer growth while reducing the burden on senior developers. 

Attendees will leave with actionable insights on combining AI tools with human expertise to improve query performance, reduce costs, and build stronger development teams. Whether you're skeptical about AI's role in database development or eager to implement it, this session provides practical guidance to help inform your organization's approach to AI adoption in database development.

Key Takeaways
- Learn practical approaches to SQL optimization with AI assistance that can significantly improve database performance. 
- Discover how SQL coders are adapting to AI tools - with over half of developers now using AI coding tools.  
- See demonstrations of identifying and solving common performance issues that can dramatically impact database performance. 
- Get an implementation roadmap based on successful software transformation approaches from industry experience. 
- Walk away with actionable team development strategies that combine AI tools with human expertise to build stronger SQL development practices.

Intelligent SQL Performance: Bridging the Gap Between Expert and Everyday Developers Through AI

Architectural innovation has become essential for organizations seeking to balance decentralization with control, or struggle for real-time insights while maintaining robust data governance and compliance.

This session presented by industry thought leader Donald Farmer covers practical strategies for reducing vendor lock-in, optimizing cloud and on-premises workloads, and ensuring data quality at scale, all while leveraging AI to automate and enhance governance.

Attendees will discover how forward-thinking enterprises are reimagining their data architectures to maximize agility, trust, and business impact.

Architectural Innovation for Insight and Governance at Scale

The data and analytics landscape is undergoing a profound transformation. Emerging trends in Gen AI, open table formats, collaborative analytics, and next-generation data platforms are converging to reshape the future of data architecture. In this webinar, we'll explore how:
- Gen AI is revolutionizing data roles and workflows. 
- Open table and open file formats are breaking down data silos, enabling seamless collaboration, and democratizing data access.
- Next-generation data platforms, including data lakehouses on data meshes, are emerging as powerful paradigms for managing distributed, decentralized data ecosystems

We'll delve into the architectural principles, design patterns, and implementation strategies that define these next-gen data platforms. Participants will gain insights into:
- Creating more flexible, scalable, and domain-oriented data infrastructures.
- Adapting to rapidly changing business requirements.
- Harnessing the power of Gen AI, open standards, and collaborative analytics.

Join us as we explore the pivotal trends and technologies transforming data architecture. Discover how to design and implement a unified data architecture that drives business innovation, scalability, and reliability.

Unifying Data Architecture for the Future: Gen AI, Open Standards, Collaborative Analytics, and Next-Gen Data Platforms

As organizations scale, managing data across diverse platforms and environments becomes increasingly complex. A modern data architecture must be flexible, scalable, and integration-ready to support real-time decision-making, AI-driven analytics, and regulatory compliance. In this session, we will explore:
1. The core principles of a resilient data architecture. 
2. Strategies for seamless multi-cloud and hybrid integration.
3. How to balance performance, security, and cost efficiency.
4. Best practices for future-proofing your data ecosystem.

Attendees will gain actionable insights to enhance data agility, optimize integration workflows, and maximize the ROI of their data infrastructure.

Future-Proofing Data Architecture: Strategies for Scalable and Seamless Integration

Learn what it really takes to make substantive AI progress. Tune into this talk to learn the seven tenets of enterprise transformation in the AI era which underscore both the current data situation of many companies and how to leverage this technology for the best results. 

1. Despite the long-standing hype, AI currently only includes natural language processing, not understanding. 
2. To make the best use of the AI, enterprises must build a modern knowledge foundation, i.e., data that’s logically connected, contextualized and extensible with the help of shared, interlinked, symbiotic graph data models. 
3. Organizations can’t be data mature if they’re not in data sharing mode whenever possible or pushing the data sharing envelope. Holding onto data doesn’t make sense if you’re not sharing with the partners who need what that data can provide. 
4. Transformation doesn’t matter if you’re not providing enterprise-wide and even boundary-crossing transparency in the process.
5. It doesn’t make sense to buy “AI products” without having the ability first to feed the AI the best, high quality, relevant, disambiguated data relevant to your business. 
6. Most of the AI software providers out there aren’t focused on helping you solve your data problems.
7. Most enterprises have huge, unresolved data problems. Therefore, most enterprises won’t benefit all that much from AI until they move up the data lifecycle management learning curve.

This talk will explore what effective transformation looks like in the age of AI and provide best practice guidance from organizations in leading industries.

About the Speaker
Alan Morrison is an independent consultant and freelance writer on data tech and enterprise transformation. He has a contributor to Data Science Central and TechTarget with over 35 years of experience as an analyst, researcher, writer, editor and technology trends forecaster, including 20 years in emerging tech R&D at PwC.

7 Tenets of Data and Enterprise Transformation in the AI Era

Digitalization and artificial intelligence (AI) are revolutionizing the process industry by capturing and leveraging tacit knowledge -- insights and expertise that are often unspoken and deeply embedded in the workforce. Through advanced data analytics, machine learning, and AI-driven decision support systems, manufacturers can now extract and codify this implicit knowledge. 

What You Will Discover in this Session:
1. Capturing Tacit Knowledge: Learn how AI can capture and codify tacit knowledge from experienced workers using AI tools, ensuring valuable expertise is retained and utilized.
2. Optimizing Operations: Discover how AI can optimize various aspects of operations, such as predictive maintenance, process optimization, and real-time monitoring, leading to increased efficiency and reduced disruptions and downtime.
3. Case Studies and Best Practices: Study real-world examples and best practices from leading companies that have successfully implemented AI and digitalization strategies.

This workshop, presented by CEO Andreas Eschbach will equip you with the knowledge to leverage digitalization and AI for improving efficiency and competitiveness in the process industry.

Unlocking Human Knowledge: Harnessing AI and Digitalization in the Process Industry

Managing data at scale presents challenges in accuracy, consistency, and accessibility. AI is reshaping this landscape by automating data quality monitoring and enabling natural language interactions for faster insights.

This session explores two AI-driven advancements in data management. AI-powered data quality and anomaly detection addresses inconsistencies by identifying duplicate records, detecting anomalies, and enforcing validation in real time. Natural language querying removes complexity from data access, allowing users to ask questions in plain language and receive structured responses without technical expertise.

Tune into this session to discover how implementing these innovations can reduce manual intervention, improve reliability, and make data-driven decision-making more efficient.

AI-Driven Data Management: Enhancing Accuracy and Accessibility

Web application environments are more complex than ever. Many span multiple clouds, often include containers, are connected via APIs, and delivered via CDNs. While exploits against known vulnerabilities remain common, advanced campaigns use a variety of tactics to compromise web applications. An abundance of tools claiming to address these challenges has left many security teams unclear as to where to focus their attention. On top of this, security teams are worried about how to address increased usage of genAI in development, and attacks that leverage AI. Join John Grady and Melinda Marks as they discuss recent research on the application protection market and modern application security to help security teams solve the challenges they face.

Trends in Application Security and Increasing AI Adoption

Generative AI (GenAI) could contribute $2.6 trillion to $4.4 trillion annually to the global economy according to consulting firm McKinsey, but achieving business goals requires using GenAI in a trustworthy and secure manner. As enterprise business units pour resources to drive innovation, security teams need to avoid compliance violations and data loss. Security for GenAI is a many-layered cake requiring security teams to consider everything from the (sensitive) data informing the model to prompt responses that might result in (sensitive) data exfiltration. Join Todd Thiemann as he discusses recent research on data resilience and data loss prevention in the context of GenAI.

Data and Identity Security for GenAI Infrastructure: Facilitating Innovation While Avoiding Compromises

The introduction of AI has created an imperative for the transformation of virtually every business system. This means every aspect of apps and infrastructure are in transition, leaving security pros to react to a massively changing attack surface. How will security teams respond? Where are security leaders focusing first? How will security strategies need to change to respond to this critical transformation? Join Dave Gruber, Principal Analyst, Enterprise Strategy Group as he shares a fresh industry perspective and data from his recent research on this topic.

AI-led Security Operations Strategies: Are You Keeping Up?

Cybersecurity has traditionally focused on identifying, analyzing, and mitigating vulnerabilities. However, as attackers evolve, including utilizing AI, the world of vulnerability management must undergo a massive transformation. This presentation will explore how AI-powered systems can reshape each facet of cyber risk and vulnerability management, improving both defensive capabilities and hacker tactics. This webinar will teach you the innovations driving this new approach, the emerging threats resulting from AI-enhanced attacks, and the future of risk management in an AI-driven world.

The Future of Security Risk - Security Vulnerabilities in the AI Era

As AI technologies rapidly transform enterprise operations, we're witnessing an unprecedented shift in workforce dynamics. Gen Z and Alpha generations are entering a workplace where AI isn't just a tool but a fundamental business driver, requiring a complex blend of technical prowess, adaptability, and ethical decision-making capabilities. While these digital natives possess an intuitive understanding of technology, the challenge lies in channeling this affinity into meaningful business value and innovation.

The stakes are higher than ever for CIOs and technology leaders. Unlike previous technological transitions, AI's impact on workforce development demands a complete reimagining of talent strategies. Organizations must move beyond traditional technical training to foster a unique combination of AI literacy, human-centric skills, and ethical judgment. This requires a transformative approach to talent development that bridges the gap between native digital fluency and the strategic capabilities needed to drive AI-powered innovation and value creation.

In this session, Giles Lindsay (Chief Information Technology Officer, Lif3away Inc.) provides a practical roadmap for CIOs to build and nurture a future-ready workforce that can thrive in an AI-augmented business landscape.

Key Takeaways
- Transform digital natives into strategic AI leaders, combining technical proficiency with business acumen and ethical decision-making capabilities.
- Create effective learning frameworks that balance AI literacy with distinctly human skills like creativity, emotional intelligence, and critical thinking.
- Implement practical approaches for continuous skill development and adaptation, ensuring your workforce remains relevant and innovative in a rapidly evolving AI landscape.
- Integrate ethical considerations and bias awareness into AI talent development, building teams that can responsibly drive AI adoption while maintaining transparency and fairness.

From AI Users to AI Leaders: Cultivating Next-Gen Digital Talent

As AI investments surge across large enterprises everywhere, CIOs are facing a new spin on familiar challenges. The rise of "FOMO AI" (Fear of Missing Out on AI) is driving a renewed focus on issues like business engagement, board influence, team development, and change management. 

Despite these challenges, the wave of technological advancement also presents new career opportunities for CIOs at both the executive management and board levels. CEOs are adding new technology officer positions to their executive committees, and boards are eager for technology representation. 

In this session, Martha Heller, CEO of tech executive search firm Heller, discusses the future of the CIO career and shares unique insights on how to leverage AI to achieve both business and career success.  

Topics for discussion include: 
- How CIOs are educating their boards about AI value
- New technology executive roles on the executive committee
- Evolving career paths for CIOs.
- Levering AI leadership needs for Board Director positions.
- And more.

Meet the Speaker
Martha Heller is CEO of Heller, an executive search firm specializing in technology leadership roles. She has placed hundreds of senior technology executives throughout her career. Martha has authored two books on IT leadership - The CIO Paradox: Battling the Contradictions of IT Leadership and Be the Business: CIOs in the New Era of IT – and is a contributor to CIO.com. Her e-newsletter, The Heller Report, has become a must-read for the industry.

The Impact of AI on the CIO Career

Modernizing Storage Operations for a Cloud-Driven World

New Age of Managing Data

As data continues to grow in volume, variety, and velocity, emerging technologies like artificial intelligence (AI), machine learning (ML), and automation are reshaping the landscape of data management. 

This session from industry thought leader Efe Ogolo explores how these innovations are driving smarter data governance, real-time decision-making, and enhanced data quality. Join Efe Ogolo, as he discusses the strategic implications of AI-driven data pipelines, the rise of autonomous data platforms, and how organizations can future-proof their data ecosystems in this era of intelligent data management.

Manage Data for the New Era with Emerging Technologies

Artificial Intelligence

Machine Learning

Data Management

Data Governance

Data Quality

Data Platform

Emerging Technologies

intelligent data management

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Manage Data for the New Era with Emerging Technologies

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