AI-Powered Anomaly Detection for Workiva
Revolutionizing how financial professionals identify data inconsistencies in collaborative spreadsheets through intelligent automation and intuitive design.
Project Overview
Duration
3 months
Team
Nandha
Ranga
Role
Product Designer, UX Researcher
Tools
Figma, Python, Tableau
Focus Area
Anomaly Detection
The Challenge
Workiva users were spending 73% more time than necessary reviewing collaborative reports for data anomalies and formatting inconsistencies. With datasets growing exponentially and compliance deadlines tightening, manual detection methods were failing at scale.
Research & Discovery
Understanding the Problem Space
Through extensive user research with Business Analysts and MIS professionals, I uncovered critical pain points in the current anomaly detection workflow. Users were relying heavily on domain knowledge and manual scanning, leading to inconsistent results and missed outliers.
User Interviews
6 participants across Business Analysts and MIS students using structured 18-question framework
Thematic Analysis
Identified 3 core themes: automatic flagging, learnability, and visualization needs
Competitive Analysis
Evaluated existing solutions and identified gaps in enterprise-grade anomaly detection
Key User Insights
"Ideally it would tell me what the problem could be as well. So let's say there's an increase in acquisitions this month. Maybe we can tell me that it's possibly because this variable seems to have too many missing values."
"An alert system basically like the moment it finds out an alert and it should be real time. It should actually tell you where exactly is the error instead of just telling that there is something wrong."
Flagging
Users need automated systems that can identify anomalies and provide contextual explanations for why data points are flagged as unusual.
Real-time Alerts
Immediate notification systems that pinpoint exact locations of errors rather than general warnings about data quality issues.
Visual Hierarchy
Clear color-coding and visual indicators that make anomalies immediately recognizable within large datasets.
User Personas & Journey Mapping
Design Solution
AI-Powered Anomaly Detection Interface
The solution integrates machine learning algorithms with an intuitive interface that provides real-time anomaly detection, contextual explanations, and actionable insights directly within the Workiva spreadsheet environment.
Impact & Results
The solution addresses critical workflow inefficiencies and positions Workiva as a leader in AI-powered financial data analysis. The design received recognition at the GS Designathon 2022, validating its commercial viability.
Key Learnings
- AI integration must prioritize explainability over pure accuracy for enterprise adoption
- Real-time feedback loops are crucial for maintaining user trust in automated systems
- Visual hierarchy and progressive disclosure help users navigate complex data insights