Designing for data visualization
2016 - 2019 Key Contributions:
Designed and shipped 40 charting user stories that helped SAP Analytics Cloud successfully secure International Business Communication Standards (IBCS) Certification
Granted Patent: Patent No.: US 10,810,263 B2 - User Interface for Accessing Hierarchical Data.
Problem Space
In today’s world, data is one of the most valuable assets—it drives decision-making, tracks key metrics, and tells compelling stories. Yet, for many users, especially those with varying levels of data literacy, interpreting complex data can be a significant challenge.
As a UX designer specializing in data visualization for SAP Analytics Cloud, my goal was to simplify that complexity. I focused on designing intuitive tools that empower users to grasp insights at a glance through clear, effective visualizations.
My Role
As a UX design specialist on the SAP Analytics Cloud UX team, I worked with multiple development teams, product experts, and product management to investigate the challenges our customers are facing with using data visualization to present business data.
Process
(Due to confidential agreement, I cannot show any documentation or design that was produced before shipment. )
To start designing for data visualization, it often starts with requirements gathering, and asking the question “what story does this data visualization tell?”. For example, a horizontal line chart tells a story about a change over the course of time. It is not meant to communicate where you have spent your money, but it could tell you how much money you have spent over the year.
With the support from the product management and customer experience team, I was able to ask this question to our customers, who were able to tell me what type of data they are working with, and why they need a new data visualization to represent their data.
One of the chart types I shipped out is called Histogram. Customers needed this chart because they want to see a distribution of data over a numeric axis rather than categorical.
Through customer interviews, I was able to capture the common questions they use Histogram to answer:
What is the distribution of salaries?
What is the average distribution of monthly precipitation?
What is the distribution of marathon finishing times?
When designing for data visualization, we have to understand how charts and data can be used together to communicate the correct message.
After understanding the use case, I researched into the properties of Histogram to understand how different data structure changes the messages it communicates. One unique property of Histogram is how it displays raw data vs aggregated data. In the example below, I have illustrated how adding an aggregated dimension to a Histogram changes the bucketed values.
As the next step, I deepened my understanding of the foundational concepts and data structures required to build a histogram. This allowed me to define technical requirements and identify key edge cases we needed to address. For example, while a histogram is similar to a stacked bar or column chart that supports multiple dimensions, it also requires a dynamic label color system that adjusts for contrast against varying backgrounds. At the time, however, our platform only supported static label colors.
To illustrate these challenges, I created multiple interactive prototypes using Flinto, highlighting potential usability issues customers might face.
Through several validation sessions with key stakeholders, we finalized the histogram design and delivered it in just a few months. It has since become one of the most visually impactful charts available in our platform, frequently used by customers to compare quantitative data and power predictive tools that surface meaningful business insights.