34. What is 34?!
Is it a distance? An amount? Temperature? Location? A time duration?

Without any context, it is difficult to effectively use a piece of data. What that data is and what it means is critical context required to get the most out of your data.

This is especially important in this era where data has become the most important asset of an enterprise. When you bring context to data and understand what it is as well as how it fits into your world, you create knowledge, and that is the fundamental building block of a modern enterprise.

While understanding the context of data is key, it is also important to remember that there is no universal context. What makes sense in one case may not in another.

We often see this in Master Data Management (MDM) scenarios where there is a disagreement on the definition of a customer and then 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 perspectives and bring more agility as to how we leverage data in the enterprise.

In this presentation we will discuss:

• How to define what a logical data model is and contrast it to current approaches, like data lakes and warehouses
• Examples of data models and how they can be used as lenses over our data
• Why logical models are a key component to a knowledge graph
• Real world use cases where a logical model was leveraged

Mike Grove

Founder & VP of Engineering,
Stardog

Michael has more than 15 years of experience in AI, semantic technology and graph databases.

Prior to Stardog, Michael performed research at Fujitsu Labs of America on the use of graph-based technologies in pervasive computing environments. 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.