1. Which dataset did you use?

I used a dataset of aggregated telemetry data from Oracle Fusion which enables the calculation of various AI metrics. For this visualisation competition, I focused on two monthly aggregated measures:

• Cumulative AI-assisted tasks completed (by task type)
• Distinct AI users

Additional context on the telemetry approach is described in this Oracle AI blog post: https://blogs.oracle.com/ai-and-datascience/quantifying-ai-success

2. How did you analyze or prepare the data?

I used a Racing Bars visualisation to effectively highlight month-over-month growth in adoption of different AI-assisted task types, as well as the overall AI user base.

To achieve a visualisation better suited to the telemetry data, I added the following enhancements to the Racing Bars plugin:

• Pause/Resume button for animation control, allowing users more time to take screenshots for presentations.
• Improved y-axis label layout for readability of task names.
• New configuration options:
  • Keep Bars: if a task is missing in a given month, choose to (a) drop the bar (previous default), (b) set value to zero, or (c) carry forward the prior value. For this visualisation, I used (c) because the metrics are cumulative.
  • Display Legend: toggle legend on/off (kept off due to the high number of task types).
  • Format YYYYMM: converts YYYYMM into a readable month format (e.g., “Jan 2025”).

3. Who is the intended audience for your visualization?

• Customer Success Managers (CSMs) who can use this data to engage with their customer base.
• Oracle senior leadership who can use this data to better understand trending features in our products.
• Customers interested in understanding adoption of Fusion’s embedded AI features.

4. What is your visualization about, and what question or problem does it address?

The goal of this visualisation is to make AI trends immediately visible, helping to spark conversations with customers about AI adoption and how Oracle can support their adoption journey. Current trends show particularly strong growth in AI features embedded in HCM's goals and performance feedback modules.

5. Did you use any Oracle Analytics AI features when building your visualization (e.g., AI Assistant)? If so, please describe how they were used.

No.

6. Did you upload your visualization image and DVA file?

Visualisation image and DVA file attached to this post, and also submitted to the competition via email.