Co-Founder and SVP of Engineering
Michael is SVP of Engineering and Co-Founder at Stardog, where he manages their world-class engineering organization and helps shape the direction of the Stardog Enterprise Knowledge Graph. He has over 15 years of experience in the AI, Semantic Technology, and Graph Database fields. Prior to Stardog, Michael performed research on the use of graph-based technologies in pervasive computing environments while at Fujitsu Labs of America. Michael is a graduate of the University of Maryland in Computer Science and an alumnus of its MINDLAB, a seminal research group in Semantic Web technology.
Talks and Events
2022 Talk: Plotting A Course Through The Bermuda Triangle Of Enterprise Data With An Enterprise Knowledge Graph
Today’s organization demands rapid insight from increasingly hybrid, varied, and changing data. Traditional enterprise data management systems can’t keep up with this growing complexity and insights get lost somewhere in the Bermuda Triangle of data storage, data catalogs, and analytic applications. Conventional graph or relational data architectures lack the access, context, and inferencing required to meet the grueling demands for innovating and monetizing advanced analytic solutions. Data catalogs provide an inventory of information assets, however if the catalog is disconnected from the rest of the enterprise data, you’re left with a meta-data silo. This leaves data and analytic teams constrained by architectural limitations for highly scalable, discovery-style analysis in relation to business problems. Data fabrics have emerged as a modern solution to address these data needs. An Enterprise Knowledge Graph is the key enabling ingredient to a data fabric. As a unified graph data model enriched with logical definitions, it provides a flexible data layer that dynamically weaves together data across the organization. With an Enterprise Knowledge Graph you can connect to data regardless of where it’s stored, bring to life your data catalog, and empower your data and analytics teams with the data and insights they need, faster.
2021 Talk: How to Build a Data Fabric
The enterprise data landscape is increasingly hybrid, varied, and changing. The emergence of IoT, rise in unstructured data volume, increasing relevance of external data sources, and trend towards hybrid multi-cloud environments are obstacles to satisfying each new data request. The old data strategy centered around relational data systems is fundamentally broken.
Enterprise data fabrics offer a new way forward. The data fabric weaves together data from internal silos and external sources and creates a network of information to power your applications, AI, and analytics. Quite simply, they support the full breadth of today’s complex, connected enterprise. But how to achieve a data fabric?
In this session, you’ll learn:
- Key steps for building a data fabric and how to leverage existing data management investments
- The technical skills and resources required to begin your data fabric implementation
- An MVP methodology for data modeling to drive compounding business value
2020 Talk: Insufficient Facts Always Invite Danger: Combat Them with a Logical Model
While understanding the context of data is key, it’s important to remember there is no universal context. What makes sense in one case may not in another. We see this often in MDM scenarios; we can’t agree on the definition of a Customer and the process grinds to a halt. Not only does a logical data model let us better represent our data, it provides us the flexibility to look at the data from a different perspective and brings more agility to how we leverage data in the enterprise.
View the 2020 talk in the KGC media library.