Level: Beginner to Intermediate
Prerequisite: None
What’s the big deal about “graphs”? There is a lot of buzz about graph data, graph databases, and knowledge graphs, but what is it really all about? In this course we’ll explore graphs less as a technology and more as a mindset. This full-day course with hands-on exercises will teach you how to “think in graphs” and will show you firsthand how that sort of flexible thinking makes it easier to develop robust, innovative solutions to your specific business problems.
This course will take graph concepts out of the realm of academic theory and show you what they mean in practice. The hands-on exercises won’t require a laptop, just pen and paper and a desire to not just push your thinking, but the very way you think. You’ll learn how to represent complex phenomena as a flexible network of concepts instead of as a rigid relational schema, and you’ll see how easily that network model can adapt in response to shifting business requirements or unexpected new knowledge.
Importantly, we’ll explore some basic complexity theory to understand the critical difference between complicated problems and complex ones. This subtle differentiation can revolutionize the way you approach business problems once you realize that most of our technologies are designed for dealing with the complicated, not the complex. When we apply tools optimized to deal with complication to problems full of complexity, we struggle. When we apply tools designed for complexity, like graphs, to complex problems, answers emerge easily.
Our hands-on exercises are designed to let you learn the concepts above experientially. By developing and evolving a hand-drawn graph model of a problem of your choosing, you’ll be able to identify exactly where the complexity of your problem lies. Then, we’ll use your graph model to explore one of the best features of graphs—how easily they can be combined. By connecting your individual exercise outputs with those of other workshop participants, we’ll see how graphs allow larger systems to be built incrementally from smaller ones—dramatically speeding up development and iteration cycles, making it easy to incorporate diverse stakeholders who all have a slightly different perspective on the problem being solved—and ultimately to identify solutions that would have been missed without a graph approach.
Lastly, we’ll survey the landscape of graph technologies—from databases to algorithms to visualization layers—to see how they can help support the graph mindset you’ve just developed. We’ll understand some of the tools that allow graphs to offer unique answers, insights, and recommendations, and we’ll experiment with non-network interfaces that will help you understand that just because your data might be stored as a graph doesn’t mean you have to interact with it that way! Non-network-based visualizations allow users to leverage graph data without having to interact with a confusing network UI. Just because you will have developed a graph mindset by the end of this workshop doesn’t mean your end-user will as well, so we’ll end with a discussion of how to take your new mindset and make it accessible to those consuming your solutions.
You Will Learn
- How to think in a “graph mindset” to enhance the way you model problems and deliver solutions
- What the terms “semantic data,” “graph data,” “taxonomy,” and “ontology” really mean for your day-to-day work
- The difference between complexity and complication and why it’s critical to how you engineer solutions
- What a graph database is, the different flavors of graph database, and how they compare to relational databases
- When graph technologies are a great fit for a task and when they really aren’t
- How graphs address “the fragile schema problem” and why this is so important for those responsible for implementing systems
- The types of analytics well-suited to graph data, like recommendation engines
- How to visualize graph data in ways that add the most value for the situation
- How graphs can be a critical component to data storytelling that drives buy-in and action
- How to supplement graph technology with other technology to create more advanced systems, like how using graphs to augment large language models (LLMs) can reduce generative AI hallucinations
Geared To
- CIOs and CTOs
- Enterprise architects
- Data analysts and business analysts
- Business intelligence professionals
- Analytics professionals
- Data scientists
- Developers