IT and business executives frequently talk about information as one of their most important assets. But few behave as if it is. Even today, executives report on their financials, their customers, and their partnerships, but rarely the health of their data assets. And corporations typically exhibit greater discipline in managing and accounting for their office furniture than their data. The arrival of generative AI (GenAI) is sparking a discussion of how to adopt AI in measuring, monetizing, and managing data assets. Laney shares insights from his best-selling book, Infonomics, about how organizations can actually treat information as an actual enterprise asset. He discusses why data both is and isn’t an asset and property and what this means to organizations—particularly as they prepare to put AI to work broadly. He also covers well-honed approaches to and examples of organizations managing, monetizing, and measuring their data assets. 

Jain and Das discuss how organizations should secure their AI application and the critical data they are feeding into these systems to ensure compliance and prevent damaging data leaks.

Learn how National Student Clearinghouse (NSC) created an operational MDM platform, giving access to a large volume of streamlined, high-quality data. With billions of records, a legacy IT system, and an enterprise focus on moving to the cloud, NSC focused on modernization for the cloud data ecosystem, adhering to compliance regulations and enhancing matching across the enterprise. Discover how NSC is now empowered with a single platform to support and facilitate customer requests with one source of truth while benefiting from a collaborative hub for data management and governance.

Using data mesh to improve decision-making involves accelerating the adoption of many data elements.

The Department of Defense (DoD) initiated efforts toward advancing the agency's goal to improve decision making across all DoD entities. This goal rests under the foundational principle of accelerating DoD's adoption of data, analytics, and AI by prepositioning a common frame of reference for all DoD entities to converge and share data and AI models. Under the auspices of the DoD Chief Digital and Artificial Intelligence Office (CDAO), this effort will create an enterprise-level infrastructure of services intended to drive an integrated data, analytics, and AI strategy, while maturing a responsible DoD-wide AI ecosystem. This presentation highlights the case study on DoD’s efforts to establish a data mesh construct based on the following four elements: domain-oriented/decentralized data ownership and architecture, data as a product, self-service data infrastructure as a platform, and federated computational governance.

Advancements in data automation, low-code/no-code platforms, and APIs make it quicker and easier for organizations to start their data fabric projects, often in just a few months. Learn how these advancements enable smoother integration and management of data across the enterprise, leading to faster decision making, efficiency, AI readiness, and increased profits. Discover how leveraging data automation can accelerate your data fabric strategy so your data is more effectively fueling significant growth and sparking innovation.

With the rise of generative AI (GenAI) and large language models (LLMs), the data fabric can add a range of new facilities to accelerate data democratization.

The data fabric architecture has been steadily gaining traction in the enterprise to unify data across disparate sources into coherent data services. By leveraging the power of GenAI models in conjunction with smart data fabrics, organizations can automate the integration of data, provide natural language access to data and analytics, improve data quality while decreasing the need for labor-intensive data cleansing, and secure and govern data in real time. Fried explores the benefits of using data fabrics and GenAI to improve data management practices and provides examples of how these technologies can be used in real-world scenarios. He also notes the risks and lays out a practical path for applying this technology safely.

Data mesh is evolving due to changes in data architecture and technological advances.

There is no doubt that data mesh principles resonate with so many data professionals, particularly those looking to move beyond brittle, monolithic architecture. However, adopting data mesh can seem daunting, due to both a scarce but improving ecosystem of tools, as well as organizational change management. Luckily, data mesh lends itself to evolutionary adoption, helping organizations to leverage existing platform investment and gain incremental value. Cordo reviews architectures and best practices from real-world experience, grounded by the stories of two organizations.

New technologies can solve organizations’ operational problems.

In this session, Bagnall explores ETL's role in seamlessly integrating with data fabric architectures to empower organizations with the ability to efficiently manage, integrate, and analyze their data from diverse sources. He delves into real-world use cases, best practices, and the key features that make any ETL process a valuable ally in your journey toward a more agile and unified data ecosystem.

Data mesh plays a pivotal role within modern cloud architecture, while a semantic layer acts as a cohesive force within the data mesh framework.

Data mesh is swiftly gaining traction as an innovative strategy for expediting data and analytics advancements. It achieves this by distributing data product development through domain-oriented, self-service methods. Crucial to the success of this approach is the emergence of the semantic layer, serving as a foundational catalyst supporting composable model design, enhanced collaboration, and decentralized ownership. This enlightening session delves into the integral role of the semantic layer within a contemporary analytics architecture, elucidating its interconnectedness with the data mesh concept.