Case Study · Data · Dashboard · MVP
Accessing PO and spend data required heavy manual effort and reports lacked historical context — making it nearly impossible to track trends, manage budgets, or make proactive decisions.
The Problem
Accessing PO and spend data required heavy manual effort. Reports lacked historical data, making it hard to track trends or manage budgets.
Approach
Validated the problem through data analysis and user interviews, built an Excel MVP, then partnered with a data viz team to create a Tableau dashboard.
Key Insight
Automating analysis wasn't enough. Users needed real-time, accessible visibility for faster decisions.
Solution
Tableau dashboard integrated with Workday & Ariba, providing real-time PO visibility with hourly refreshes and self-service filtering.
Impact
42% reduction in overspent POs. Eliminated manual reporting and drove strong adoption and faster decision-making.
Background
Day to day, accessing purchase order and spend data required a significant amount of manual effort. Users had to pull reports from Workday and analyze large Excel files just to find basic information. These reports only showed open orders and lacked historical data, making it difficult to identify trends, estimate future spend, or adjust budgets throughout the fiscal year. There was no easy way to quickly understand what was happening across orders, such as how long they had been open or whether a lab was waiting on multiple items.
This lack of visibility created broader business challenges. Blanket purchase orders were frequently being overspent, which required new POs to be created mid-cycle. This often led to invoicing issues, as invoices were submitted with outdated PO numbers, creating confusion for Accounts Payable and delaying processing. More importantly, limited visibility into current spending made it difficult for labs to effectively manage budgets for research, where timing and resource planning are critical.
For users, the experience was frustrating and time-consuming. It was difficult to quickly find relevant information, and the lack of clarity often led to reactive work instead of proactive decision-making.
Discovery & Strategy
I started by validating the problem from both a data and user perspective. To quantify the impact, I analyzed historical purchase order data and identified patterns where multiple POs were created for the same supplier and cost center within a single fiscal year, indicating that original POs had been overspent. In parallel, I conducted interviews with internal stakeholders and customers to understand how they were accessing and using spend data. Across these conversations, a consistent theme emerged: reporting was highly manual, time-consuming, and lacked the visibility needed to make informed decisions.
Based on this feedback, I developed an initial MVP using an Excel macro. I noticed that users were repeatedly taking the same steps to analyze large datasets, so I created a template that automated those actions and summarized key information. To make it accessible, I designed it with a simple button interface, keeping in mind that many users had limited technical experience with Excel.
MVP Insight
The MVP was well received and provided immediate value, but it also surfaced a key limitation. Users still had to manually pull reports and input data, which meant the solution wasn't fully addressing the need for speed and real-time visibility. This feedback made it clear that while automation helped, the real opportunity was eliminating the manual process altogether.
I partnered with a data visualization team to evolve the solution into a Tableau dashboard with direct integration to Workday. I shared the MVP, user feedback, and initial mockups to align on the vision. We launched with a focused set of core data fields, tracked usage, and iterated by adding features based on user needs and behavior.
What I Built
Based on user feedback and insights from the MVP, I developed a Tableau dashboard that integrated directly with Workday and Ariba to provide real-time visibility into purchase order and spend data. This eliminated the need for users to manually pull reports and perform their own analysis.
The dashboard automated key calculations and surfaced the most important data points users needed to manage their orders and budgets effectively. It was designed to be easily accessible via a shared link and refreshed hourly, ensuring users always had up-to-date information without additional effort.
To make the dashboard useful for a wide range of users, I included filtering capabilities that allowed users to quickly focus on specific cost centers, suppliers, or orders. I also tracked usage and user interactions to understand how the dashboard was being used and continuously refined the data fields and views based on feedback.
Results
The dashboard also drove strong adoption and behavior change. Users consistently highlighted the ease of use and improved visibility, especially those with less experience in Excel. By making key insights immediately accessible, users were able to make faster, more informed decisions.
Looking Back
This project reinforced the importance of deeply understanding user needs and building with empathy. By listening to users and validating their feedback with data, I was able to focus on solving the right problems rather than making assumptions. It also highlighted how effective the build–measure–learn approach can be in ensuring you're moving in the right direction and continuously improving based on real usage.
I also learned the value of starting small and iterating quickly. While the MVP provided useful validation, I spent more time refining it than necessary. In the future, I would move to a more scalable solution sooner once initial demand and value are confirmed.
Finally, this experience showed me how important it is to clearly communicate the "why" behind a solution. Aligning stakeholders around the problem and the value of solving it was key to gaining support and successfully bringing the dashboard to life.