UX Design for Data: Connecting Users to Related Content

2 Minutes Read

Most organizations have started using a knowledge graph to connect otherwise disparate sets of content and data in a way that allows them to glean new insights about their company and their customers.  Knowledge graphs can also be used as the foundational layer of a recommendation engine to suggest relevant content and data to enterprise users and consumers who are using a product.  Recommending content and data is not new, but using a knowledge graph to do so is relatively new.

But, so what?

In a traditional CMS setting, content managers are able to create and connect content together manually.  In more advanced implementations, business rules can connect content based on taxonomies, or tags, that tell the system what content is about.  This works well for small sets of knowledge and content.  However, when content and data sources grow beyond the managed content in a single repository, this becomes exponentially more challenging to do with humans and even if it is done, humans are not able to process and define all of the relationships across all content sources at the speed of data and content creation.

In comes the knowledge graph...

Imagine a sizable enterprise organization with multiple content management systems, people systems, CRMs, call centers, social media accounts, transactional systems and more.  These systems and their content and data can be connected using knowledge graphs and ontologies which describe the relationships between each entity.  This is a game changer and allows companies to see for the first time how all of their content and data inside and outside of their organization connects.  While this is exciting and it is easy to imagine the potential benefits, when you start to next imagine how you surface all of this content and data, relationships between the entities, and data insights to users - you can start to see the user experience problem.

How UX can save the day.

Predictive UX specializes in making sense of connected content and data.  We have been working with partners and clients for years to surface once-disconnected content and data in search, search results, dashboards, learning management systems and more. 

We understand the power and importance of metadata, how to use it to facet search results effectively and how to normalize what seems completely different into something users easily understand.  We are designing diagnostic tools, metadata hubs, customer ecosystems - all powered by knowledge graphs.  We are even designing an admin UIs for fine tuning knowledge graphs so business users can tweak data relationships and results.

We are excited about our work and taking a measured approach to assure the ROI of connected data and content for our clients by understanding how users think.  Understanding the way people need to interact with this new bounty of information so that is actionable and meets business and user needs is the key to a successful implementation.

Case Studies

For more insights on the UX of knowledge graphs and how to realize the ROI of one for your organization, contact Predictive UX to schedule a discovery call.

Picture of Karen Passmore

Karen Passmore

Karen Passmore is the CEO of Predictive UX, an agency focused on product strategies and user experience design for AI and data-rich applications. Karen talks about UX, AI, Inclusive Design, Content and Data Strategies, Search, Knowledge Graphs, and Enterprise Software. Her career is marked by product leadership at Fortune 500 companies, startups, and government agencies.