Role
Lead Designer
Product Execution Lead
Platform
WingspanAI Web
Teams
WingspanAI promises to be a remote control center for farm operations, offering advanced data solutions to increase efficiency and profitability. However, its usage was at an all-time low, revealing a large disconnect in how we highlighted data. Farm managers needed specific metrics and big-picture insights across large time-scales, not granular drive sessions with excessive manual consolidation.
After many iterations and technical challenges, we aggregated operational data into an interactive, comprehensive view with built-in progressive disclosure, making it easy for users to evaluate their overall farm's performance before drilling in for greater details.
Say hello 👋🏼 to the new and improved Operational Analytics.
We delivered a completely redesigned Operational Analytics at the end of Q3, doubling WingspanAI Web usage and dominated as the most used tool in the entire app. We were able to shift the purpose of WingspanAI Web from live monitoring to post-operational review.
Follow along to see how we got here.
The Problem
To deliver real value with a smart, data-driven tractor, we had to make insights accessible. Unfortunately, simple questions like how much a user saved by choosing an electric tractor over diesel became a scavenger hunt through every drive session with manual calculations at the end. Let's be honest, no one wants to crunch numbers after a long day in the field.
USER PROBLEM
User feedback revealed that users relied on multiple sources to gather information and manually consolidated the data themselves.
HYPOTHESIS
We believe that users want a single, easily accessible source that delivers valuable, actionable data immediately for more effective reporting and future operations planning.
1
BUSINESS PROBLEM
Low engagement and user complaints around the promise of a "data-driven" product.
HYPOTHESIS
We believe that designing an interactive dashboard that delivers better analytics will drive engagement and showcase fleet performance effectively.
2
There were distinct differences between our top user segments, dairies and vineyards, like their operating hours, key data points, and size. We prioritized the larger vineyard user base, while designing a scalable solution that could easily adapt to dairy users in the future.
To uncover the specific challenges our users faced with their tractor data today, I directly spoke with crew supervisors and farm managers.
Fragmented Data
Reconstructing a complete day's work from individual drive sessions was time-consuming and frustrating, especially for fleet-wide weekly reviews.
No Separation by Operation Type
Mowing will be evaluated differently than spraying, and managers couldn't differentiate between the drive sessions.
Missing Key Metrics
Essential Metrics like Acres Covered and Efficiency were missing, leaving users blind to the performance of their fleet.
Lack of Operator Tracking
Without tools to track operator performance, farm managers couldn't assess readiness for assist or automate modes.
Hard to Review Incidents
Incidents were reported independently of this feature, causing users to navigate back and forth.
No Savings Trends
The inability to track and compare savings prevented users from assessing the value of their electric fleet.
To deliver an intuitive and impactful user experience, I evaluated other analytics powerhouse tools and defined core design principles. This research guided my design process while maintaining ease of access and usability at the center of all the complexity and chaos.
Create custom dashoards
Free-Select Dates or Custom Date Range
Variety of Data Visualizations
Easy-to-use Search Tool
Tooltips for Statistics
Featured Insights
Exportable PDFs
Manipulate & Customize Databases
1
Minimize Information Overload
Present data in digestible, prioritized layers to help users focus on the most important information
2
Clear Data Visualizations
Use simple, effective visual elements to make complex metrics easy to understand at a glance
3
Logical Information Funnel
Organize content to guide users from high-level summaries to more granular details
4
Map-Centric Design
Highlight interactive maps that provide a holistic view of farm operations and context for incident reviews
Defining The Solution
Our goal was to support better review and future operation planning for our users by giving meaningful, actionable data. We started by defining user stories in the form of real questions farm managers needed answers to, which shaped the core functionalities and new metrics to highlight.
What work was done on my farm and where?
How was the quality of the work performed on my farm (coverage, efficiency)?
Were there any issues during my operations?
How well did my operators perform (idle time, efficiency)?
How much have I saved using MK-Vs over diesel tractors?
What is the expected run time of my fleet for different operation types?
Scalable Data Up To a Month
Aggregated Stats by Op Type
New Metrics like Acre Coverage
Savings Trends by Month
Incident Reviews with Context
Filter and Search Operations
Track Operator Performance
Video Playback of Operations
Battery Runtime By Op Type
To provide users with these answers, we needed to rethink how we bundled and displayed their data.
Technical Challenge
We noticed that different operations had different performance expectations. For example, with spraying, speed played a large role in determining the efficiency of the operations. If the operator drove too fast, all the input would be lost in the air and not enough would cover the vines, reducing the efficacy of the product.
The challenge was that our tractor only reported data in individual drive sessions, which were often too short to give users a clear picture of the day's work. How could we provide a comprehensive view of the farm across all operations without forcing users to sift through fragmented sessions?
Each implement has a unique type, like mower, which correlates to an operation type. We grouped all drive sessions with the same tractor-implement pair into a daily summary, accounting for breaks and handoffs. These daily summaries were then organized by operation type, enabling users to analyze trends over weeks or months.
In addition to the technical challenge of transforming data, we also developed new methodologies to capture and calculate other missing metrics. This approach brought us much closer to providing our users with actionable, accurate insights that answered their open questions.
This project was a journey of continuous iteration, with many feedback loops and adjustments to ensure the final product significantly improved our users' review and planning processes. From gathering the right data and aggregating it effectively, to presenting it in an intuitive, non-overwhelming way, every piece had to come together. Through progressive disclosure and methods like A/B testing, I created a design so that users could easily analyze their farm's performance, both from a high-level monthly view or down to a specific moment in time.
We completely transformed how users engaged with WingspanAI, from real-time monitoring to post-op insights. With easily accessible metrics, expandable incident reviews, and aggregate statistics, our users felt like they could truly wield the power of a dynamic, data-driven fleet at their fingertips.
However, this project took many months of iteration, feedback, R&D, and testing before we delivered it to our users. We faced many technical constraints along the way, but our collaborative teamwork helped us overcome all of the hurdles along the way. I grew a lot as a designer, learning to adjust on-the-fly to constraints and advocate for our work alongside Product and Engineering.