Large, industrial-strength graph problems require the massive parallel processing ability and robustness that is naturally provided by DataStax Enterprise (DSE) Graph, powered by the industry’s best version of Apache Cassandra™. However, just because you can configure a 500 node cluster, doesn’t mean you should. It’s easy to get mired in the technology and forget that mere mortals will need to be able to use your software to complete real work. A well architected underlying architecture is pointless if your product isn’t being used.
When it comes to data-driven user interfaces, like those utilizing graph data, it’s vital to understand your users and what they’re trying to achieve. (Hint: Although visually intriguing, node charts alone seldom give users the answers they seek.) Equally important is recognizing the actual data that you have in order to choose the best way to visualize it. For example, users may want to browse by time, but does your data have a temporal dimension?
Further, graph data dimensionality is different than relational data. While relational data has a fairly fixed set of dimensions, graph data is dynamic. And while you can certainly use graph to power similar use cases as other NoSQL systems, for instance your next recommendation engine, graph is capable of so much more. This graph data model creates a new user experience opportunity that can make it more challenging to design a scalable architecture and UI that provides both a meaningful path through the data and is performant. The dynamic nature of graph data can also complicate requirements discovery. It can be difficult for users to imagine how a UI of this complexity will work when described solely through written requirements or static charts. Often, users need to get hands-on with data to discover the art of the possible and the value they hope to derive from your product. This requires arming technology teams with new methods and tools for rapidly spinning up data-driven prototypes and proof of concepts that business stakeholders can get their hands on.
This seminar discusses how to apply the latest UI tools and methods to create valuable user experiences with graph data.
Graph Best Practices:
- How users want to explore, discover and process large amounts of data
- Human limits for absorbing data versus technology capacity
- Tailoring experiences to the intended audience and data
- How to align the visualization paradigms with your DSE Graph problem
- Live examples of ways to share complex data meaningfully in user interfaces
Tools for the Job:
- UI Libraries for different outcomes including flow diagrams, geo maps, sankey, node chart, tree map and more
- Schneiderman’s mantra - how to survey, scan and inspect your data
- How to identify your Dominant Dimension for proper visualization paradigms
- 5 actionable steps to move from technical experiment to "wowing" business stakeholders
- Next steps on the path to production (i.e business consensus, funding and rapid prototyping)
Other Seminars in this series:
April 6: Customer 360 in Graph
April 20: Getting Your Graph Project Funded