Visualise relationships between customer and end-user research insights + Ideas as knowledge graph to integrate with epics and requirements

What is the challenge?

Currently, UX research, product design, product operations, Sales, marketing and engineering may work in siloes due to a lack of integration of customer and end-user insights, needs and preferences into their respective processes.

Part of the problem is the inability to visualise research insights and Ideas in a graph form, and to relate long lists of insights efficiently to Ideas, epics, requirements, and design artefacts such as wireframes and prototypes.

Keeping UX research separate from Product management does not help in integrating evidence with production effectively and efficiently.

What is the impact?

• More compelling visualisation of relationships between Ideas.

• More compelling visualisation of research observations and insights that one could directly link with epics and requirements, rather than allowing them to become obsolete in a dusty report, and stay hidden in a spreadsheet.

• Connecting atomic research insights directly with epics and requirements also reduces the amount of manual labour from working (i.e. copying and pasting) across three different external software/tools.

• Better integration of research insights with decisions including ROI, OKRs and Key Experience to make UX research an integral part of strategy.

• Enable cross-functional collaboration around user and customer insights including needs, and preferences.

Describe your idea

1) Visualising volumes of user insights as a knowledge graph/social network graph:

a) importing data from a spreadsheet to view as knowledge graph, with pre-determined

AND/OR

b) creating links between items within Aha! from a long list of observations, and visualising nodes and relationships in graph view

2) Relating nodes or clusters of insights to epics and requirements.

Examples of noteworthy graph visualisations include graph views in Obsidian, Kumu, and Connected Papers.

Although many knowledge graphs rely on Big Data and complex neural networks, the aim here would be to display simple relationships between qualitative and quantitative insights to make sense of UX research themes, categories and individual observations and connect these with various content in Aha! to can better inform product strategy, design, decisions and management.