The Practitioner's Guide to Graph Data

Chapter 3. Getting Started: A Simple Customer 360

We have often seen technical teams understand the benefits of graph thinking in the context of discussing a data problem that most large businesses face: trying to extract value across disparate data sources. Standing at a whiteboard sketching out the problem inevitably produces one hairy graph.

You can imagine this same scenario. You are drawing at a whiteboard and actively discussing how your system’s data is spread in different silos across the company’s systems. Your team agrees that what it really needs is direct access to your customers and their data. To illustrate this, almost every time, your coworker draws the customer at the center of the whiteboard and connects the relevant data to the customer. After stepping back, you all realize your colleague just drew a graph.

In our experience, these whiteboarding exercises illustrate the power of using graph thinking to build a data management solution. Graph applications start with data management because, either conceptually or physically, previous technology choices forced us to shape graph data into tabular solutions. The problem is that tabular-shaped data is no longer a one-size-fits-all design for today’s applications.

This is especially true for those applications that have to cater to the user’s demand for personalized context. The rising demand for personalization has put top-down pressure on data availability and relevancy. This pressure has forced organizations to ...