DataInformation ArchitectureInformation TechnologyVisualization

Increasing Visualization Impact Through Experience Design


By Joey Jablonski, Vice President, Cyber and Analytics Board of Advisors, The University of Texas at San Antonio

Compelling visualizations are a powerful business driver for data-driven organizations. They enable impactful data presentation, a shared language for corporate objectives, and better decision-making through measurement and adjustment. Visualization-enabled organizations build federated models to create compelling dashboards driven through shared standards for data, technologies, and organizational metrics.

Many organizations struggle to start a visualization program—let alone one that generates self-sustaining momentum. The stall usually occurs after a single team has been successful but struggles to drive adoption beyond their line of business or functional group. While there are many technical and organizational reasons that stall adoption, the most common ones include:

  • Impactful design is hard, especially when data is messy and business processes are complex. Visualization exposes these aspects of organizational debt.

  • Fragmented tooling prevents critical mass. Driving the organization-wide adoption of new tools takes time and commitment to building critical mass.

  • Ungoverned KPIs and OKRs create organizational disconnects on key metrics, how to influence them, and what elements of business processes to include for each metric.

Today’s analytical challenges are growing in complexity—a result of increased data volumes and the decreased time between events and taking action. Domains like cyber security and marketing analytics continue to see increases in the volume and complexity of data sets. In these cases, they measure domain success in shortening periods to respond to actionable information and measure the outcomes to ensure success. When deployed with a holistic adoption plan, visualization is a powerful tool for data exploration and increasing access to complex data sets.

The Experience Design (XD) methods allow us to understand our users’ needs, enabling easier-to-use and more impactful engagement design systems. XD principles can act as accelerators in our visualization strategy, helping build more insightful dashboards, drive wider adoption through the organization, and ensure that data is both impactful and actionable. 

Beginning with the user journey, we must build visualizations that quickly build trust with our users. We must also minimize the number of user choices to avoid confusion while they explore the data. By designing a journey that accounts for user work methods and handoffs between systems and teams, we can build valuable visualizations that identify where in the journey a user is, and provide simplified layouts for identifying the next best step in their exploration journey.

Successful application of XD principles is an iterative process. As an organization embarks on this journey, early learnings about user behavior will inform the creation of mockups and clickable prototypes to facilitate experimentation with users and gather real-world feedback. This early research and user engagement will become the first release of new visualizations and dashboards, but the process does not stop. The need to continue user research and iterate the design is critical; it ensures the ongoing adoption of new capabilities and layout adjustments to accommodate the evolution of business processes across the organization.

When designing visualizations, there are vital questions to ask to ensure we are leveraging the learnings from XD and applying them to make our dashboards impactful:

  • Do I solve a problem by answering a question we could not before? XD principles guide us to ensure that any screen or stage we define must create value and enable users to learn or do something they could not before. We should design our dashboards to ensure every visualization we build and deploy gives us new insights into our business.

  • Do I enable the next action to be identified? Data should be actionable, and visualizations should empower users to better understand conditions to make informed and measurable decisions to act on. The flow within our tools should be well documented, and users should always have a cue to where they are in the data discovery process.

  • Can I iterate to improve over time? We will constantly be learning about our data consumers, and our ability to test and iterate on visualization types, dashboard layouts, and data location enable us to be responsive to changing business needs.

By adopting visualization tools and techniques, organizations can develop a shared language and measure the outcome of changes in business processes. To be widely adopted, visualization tools must meet users where they are and provide actionable easy-to-consume data. Making low-friction visualizations part of the user journey and helping them navigate various types of data fidelity as they collaborate across the organization can drive further adoption of visualization capabilities.

Author Bio – Joey Jablonski (LinkedIn) is the Co-chair of the Cyber & Analytics Board of Advisors at the University of Texas at San Antonio and the VP of Analytics at Pythian where he leads strategic engagements assisting customers in developing their data strategy, defining and executing on data governance programs and building analytical models to power the modern data-driven organization. Prior to Pythian, Joey was VP of Product at Manifold, where he brought a product mind-set as part of all engagements—allowing for delivery of value quickly in any project, and building over time to drive adoption of new data-centric capabilities in an organization. Joey led engagements across industries including high tech, pharmaceuticals and for the US federal government. Before Manifold, Joey held executive leadership positions at Northwestern Mutual, iHeartMedia and Cloud Technology Partners. He brings 20+ years of experience in software engineering, high performance computing, cyber security, data governance and data engineering.