Data Architecture Online | Free Online Event

8:00

Trends in Data Architecture

Presented by Donna Burbank

With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this session to see the results of a recent DATAVERSITY survey on emerging trends in Data Architecture, along with practical commentary and advice from industry expert Donna Burbank.

9:00

Competitive Analytics Architectures: Comparing the Data Mesh, Data Fabric, Data Lakehouse, and Data Cloud

Presented by William McKnight

In this session, we’ll look at some Data Architecture practices that have emerged in the past few years and are being consciously worked into many enterprise data programs. These are keepers and it will be required that we move towards them, so it’s best to mindfully work them into the environment.

Data mesh is a subdividing of the enterprise Data Architecture into subsystems manageable by non-central teams. The data fabric is a metadata-driven always-on data and pipeline manager. The data lakehouse integrates the data warehouse and the data lake. The data cloud brings widespread data to bear in a single platform.

Learn more in this session and determine which, if any, will provide value to your architecture objectives.

10:00

Semantic Layer Architecture: Top Applications and Implementation Approaches for Enterprise Data Management (with Real-World Examples)

Presented by Lulit Tesfaye and Urmi Majumder

Over the last decade, we have seen some of the most exciting innovations emerge within the enterprise knowledge and data management spaces. Those innovations with real staying power have proven to drive business outcomes and prioritize intuitive user engagement. Within this list are a semantic layer (for breaking the silos between knowledge and data) and of course, generative AI (a topic that is often top of mind on today’s strategic roadmaps). Both have one thing in common—they are showing promise in addressing the age-old challenge of unlocking business insights from organizational knowledge and data, without the complexities of expensive data, system, and content migrations.

In 2019, Gartner published research emphasizing the end to “a single version of the truth” for data and knowledge management and that by 2026, “active metadata” will power over 50% of BI and analytics tools and solutions to provide a structured and consistent approach to connecting instead of consolidating data.

By employing semantic components and standards (through metadata, business glossaries, taxonomy/ontology, and graph solutions), a semantic layer arms organizations with a framework to aggregate and connect siloed data/content, explicitly provide business context for data, and serve as the layer for explainable AI.

This session will present case studies that take a deep dive in the technical architecture of a Semantic Layer, exploring the components that enable semantic capabilities, such as metadata management, data catalogs, ontology/knowledge graphs and AI infrastructure. The presentation will emphasize how these components interconnect organizational knowledge and data assets, enhancing systems like recommendation engines and semantic search and explore the top three common approaches we are seeing at play in order to weave this data and knowledge layer into the fabric of enterprise architecture, highlighting the applications and organizational considerations for each.

11:00

Break

We will resume at 11:30am

11:30

KEYNOTE: The Impacts of AI on Your Data Architecture

Presented by moderator John O'Brien and panelists Jacqueline Rinehart, Frances Stoor, and Scott Brokaw

In today’s rapidly evolving landscape, data and technology leaders face mounting pressure to create modern data architectures that fuel digital transformation and artificial intelligence (AI) initiatives. The COVID-19 crisis accelerated digital adoption by five years, prompting leading AI adopters to invest even more in AI. However, despite the urgency for modernization, many companies struggle to make foundational shifts necessary for innovation.

12:30

Data Architecture Supporting Data Quality

Presented by Mark Horseman

In an era of disparate data silos spanning various business verticals, achieving robust data quality is paramount. This talk shows how making a cloud-based data warehouse the central point in a data quality program can overcome challenges. By leveraging a well-documented data model with solved data lineage, analysts gain a holistic view of the data landscape. Root cause analysis, initiated at the cloud source saves valuable time by eliminating the need to track down source systems or diagnose downstream data issues separately. Furthermore, proactive data checks and reporting mechanisms empower data stewards, custodians, and trustees to maintain high-quality data.

1:30

Data Modeling for AI

Presented by Dave Wells

Data modeling is an essential skill set for modern data management that has an important (but frequently unrecognized) role in artificial intelligence. Data modeling and AI are both evolving fields. Everyone is aware of AI evolution because it is at the forefront of technology news. It seems that evolution of data modeling is a well-kept secret. Data modeling of the past was aligned with design of relational databases. Today’s data modeling addresses many data types — relational, key-value, wide-column, document, graph, and more — at all levels from business semantics to design and implementation.

AI models depend on data. Discriminative AI models classify existing data and use it to infer predictions and conclusions. Generative AI models create new data that is collected, stored, managed, and used as feedback. Modern data modeling provides the foundation to organize, understand, prepare, and manage data for AI. Data models provide business context, describe data content and organization, support feature engineering and data preparation, and reinforce model interpretability. Join this session to learn about the strong synergies of AI and data modeling.