Data fabric and data mesh are not mutually exclusive. Join this session to learn about how D&A leaders can put an end to a five-year long debate comparing fabric and mesh. You can deploy them independently, or best-case together. You will find out how you can deploy the fabric design to unify data management and mesh operating model to distribute data management in a sensible manner.

For over a decade, we have sought a holistic, unifying theory of data management. This presentation documents the quest, and touches on the data and analytics infrastructure model (DAIM), metadata, data fabric, data ecosystems, and FinOps. Each of these is required and together they address everything from infrastructure to AI to strategy communications.

Data management resources face increasing challenges due to growing demand, including the need for AI-ready data. It is essential for data management leaders to make sure that data management is not misdirected and aligns with the business value. Proper alignment will maximize the value of data management initiative and support business growth.

D&A leaders must develop DataOps as an essential practice to redefine their data management operations. This involves establishing business value before pursuing significant data engineering initiatives, and preventing duplicated efforts undertaken by different teams in managing the common metadata, security and observability of information assets within the data platforms.

To achieve success, it’s essential to establish effective governance, standardise enterprise practices, and balance overall goals with the needs of individual business units. Governance provides a structured framework for analytics and decision-making, while standardisation enhances efficiency by establishing common practices. Together, these elements promote a culture of transparency and accountability, enabling organizations to adapt to change and drive sustainable growth.
In this session, I’ll share how Elsevier’s well-executed Master Data Management strategy helped us strike that balance.

Metadata, data quality and data observability tools provide significant capabilities to ensure good data for your BI and AI initiatives. Metadata tools help discover, and inventory your data assets. Data quality tools help business users manage their data at sources by setting rules and policies. Data observability tools give organizations integrated visibility over the health of data, data pipeline and data landscape. Together the tools help organizations lay good foundation in data management for BI and AI initiatives.

Data ecosystems, built on data fabric design and infused with AI, promise an integrated, cost effective, and operationally simple approach to varied data management challenges. However, they don't yet always deliver on that promise. This research explores the maturity of various ecosystem components and provides a guide for D&A leaders and others looking to invest in data foundations for competitive differentiation.

This session will delve into the challenges data management leaders face and provide recommendations to address them. By understanding and tackling these challenges, leaders can optimize their data management strategies and drive informed decision making.

Learn how one of Europe's leading insurers is leveraging AI in it's Next Generation Data Governance strategy. This presentation reveals the essence of AI ready data, comprehensive metadata frameworks, product thinking and data excellence to power data governance and AI adoption at scale.
Essential insights for data leaders seeking to combine transformative AI capabilities with enterprise-grade data management.

Data management continues to evolve and is increasingly becoming a dedicated function led by heads of data management. Gartner has been investigating what makes heads of data management successful. This session will discuss the key characteristics around organizational structures, operating models, architecture and technology to establish characteristics of successful data management.

Malcolm Hawker describes MDM as a ‘must have’, while Juan Sequeda has described it as a ‘fancy integration’. As many CDO’s use MDM to solve decades-old problems, others turn to data catalogs as a natural starting point in their data journeys. This divide highlights the difficulty CDO’s face when prioritizing data initiatives: should they start with data management, or governance? Come hear two data experts debate:

- MDM Build vs. Buy
- Where should CDOs prioritize? MDM or Catalogs?
- What role do data products play in this choice?

Discover how Data Mesh is transforming data management by decentralizing delivery and empowering business-driven D&A initiatives. You will find out what data mesh is, its benefits, and the most common challenges. We will provide a successful path based on the experience of early adopters, allowing you to avoid the most common pitfalls and adopt data mesh successfully.

Data integration is evergreen, serving as a foundation element for any resilient data management strategy.
This session gives guidance on:
1. Data engineering top practices: What are the best practices to improve data integration?
2. Technology trends: What are the trends guiding the data integration technology?
3. Prioritization: Which of these top practices would prove to be the most impactful to your organization, given your current level of maturity?

Is D&A governance another data management initiative or is it more? Join us in this two-speaker debate session that clarifies the differences and synergies between the two practices, and why D&A leaders should care about making that differentiation. You will learn the best practices to complement D&A governance with data management practice.

Urgent Investments in data, analytics and AI use cases has put the spotlight once more on strong data management foundations. Is our Data even Ready for upcoming AI, analytics and data sharing initiatives is now top of mindshare for heads of data, CDAOs and their counterparts. Data Fabrics have emerged as a long term, foundational data management architecture that you should now pursue for sustained D&A success. This session will:
1. Help understand what data Fabrics are and what they mean for your data strategy and architecture
2. Help decide how to build and where to buy
3. Navigate the vendor landscape to assist in tech procurement decisions to aid your fabric journey

Three out of four companies are betting big on AI – but most are digging on shifting ground. In this $100 billion gold rush, none of these investments will pay off without data quality and strong governance – and that remains a challenge for many organizations. Not every enterprise has a solid data governance practice and maturity models vary widely. As a result, investments in innovation initiatives are at risk of failure. What are the most important data management issues to prioritize? See how your organization measures up and get ahead of the curve with Actian.