Community Performance
From Data Points to Decision Points
Overview
With the advancement of IoT and big data technologies, property management is rapidly evolving into a more digital and intelligent process. To handle the large community networks and management needs, this project introduced a smart community property monitoring dashboard for management teams. This platform is designed to integrate real-time, multidimensional data about community residents, vehicles, incidents, and assets. It simplifies complex offline operations into clear visual insights, creating a smart operations center that combines situational awareness with decision support.
Timeframe
4 Weeks
My Role
Research, UX/UI & IA Design, Branding, Prototyping + Testing
Tools
Sketch、photoshop、wps
Multi-source Heterogeneous Data Integration: requires real-time coordination from over a dozen data sources, including IoT devices (access control, vehicle barriers, etc.), business systems (maintenance requests, payments, complaints), and user apps to ensure stable, accurate, and low-latency data flow.
Clear Information Architecture and Visual Presentation: Modern visual design and logically organized interface layouts reduce the overall cognitive load, ensuring that critical data stands out immediately for decision-making.
Intuitive Interaction Pathways: Create a unified, easy-to-understand language for engaging with multi-dimensional features like “time switching, map drilling-down, and module filtering,” allowing non-technical managers to explore data effortlessly.
Challenge
Objective
For Property Headquarters: Enable macro-level monitoring and cross-community benchmarking of operational performance nationwide to support strategic decision-making.
For Regional Project Companies: Gain real-time visibility into key daily operations data (e.g., total fee collections trend, repair satisfaction, and device risk), to significantly elevate operational management and performance.
For Frontline Operations Teams: Respond quickly to community emergencies (e.g., surges in equipment alerts) to boost resolution efficiency, ensuring community safety and an improved user experience.
Competitive Market Analysis
How should the positioning of community data dashboards for both the group and project companies be defined? Where should the core data focus lie? To address these questions, we first conducted a systematic analysis of Country Garden and Vanke's core competitive offerings.
The Community Performance Dashboard's unique approach combines Country Garden's broad strategy with Vanke's operational expertise, avoiding the barriers of typical BI solutions. Instead of aiming for total dominance, we prioritize two main goals: "app user operations" and "core business revenue." By connecting online operational data with offline management metrics, we developed a dedicated dashboard that truly reflects the platform's unique "community ecosystem operations."
Conclusion
User Research
The survey revealed a shared goal among three key roles—ranging from group directors to frontline workers: to move away from traditional, fragmented, and outdated information methods and toward creating a unified decision support system that spans operational levels and provides real-time, detailed data access.
Fragmented Data Visibility: Operational data is dispersed across various systems. Headquarters must compile multiple reports to achieve a comprehensive national overview, severely hindering unified business insight.
Delayed Decision-Making: Reliance on outdated, fragmented reports prevents real-time monitoring and proactive intervention. Regional teams lack immediate insight into anomalous project fluctuations.
Difficulty in Drill-Down Analysis: Managers lack the tools to trace macro-level drops (e.g., satisfaction rate) back to specific micro-level service issues, preventing necessary layer-by-layer strategic analysis.
Key Points
Build a unified data hub: Integrate diverse data sources including IoT devices, business systems, and user apps to break down information silos.
Design a hierarchical visual narrative: Build an information structure that covers national overviews down to community specifics, supporting decision-making at various levels.
Create intuitive interaction paradigms: Offer simple, user-friendly exploration paths for multidimensional data visualization across time, space, and business operations, empowering managers to gain data insights independently.
What should we do?
Design Principles
To achieve the balance between macro strategy and micro-operation, I have outlined the following principles for the entire design process.
Focus & Clarity
Focused on key themes like “App User Data” and “Core Business Revenue,” the design uses modular layouts and clear visual hierarchies to make the most important metrics immediately noticeable. This allows managers to quickly understand essential operational trends.
Intuitive Exploration
All data operations—such as selecting timeframes, clicking maps, or switching business segments—follow straightforward, consistent rules. This allows managers to analyze data seamlessly across different dimensions, identify issues independently, and determine root causes without needing repeated training.
Seamless Context
Design emphasizes specific business scenarios (e.g., online services), ensuring previously set filters (e.g., time range, selected area) remain active when switching views. This maintains analytical continuity and keeps user focus.
Information Architecture
Based on the user personas and core requirements set earlier, we ultimately built a three-dimensional information architecture based on the framework of “Global Overview - Spatial Drilldown - Metric Switching.” This design directly addresses three main pain points: data silos, delayed decision-making, and difficulty in gaining deep insights. It consolidates scattered business data into four key sections—App Overview, Map, Property Payment, and IoT Data—providing managers with a unified decision-making view that covers both macro and micro levels.
User Flow
We mapped key paths for core scenarios—such as drilling down from a national overview to neighborhood monitoring—to validate the “spatial drill-down” interaction logic and ensure it aligns with the design principle of “intuitive exploration.”
Mid-Fidelity Wireframes
Centered around the main scenario of “zooming down from a national map to a residential neighborhood,” we outlined its interactive structure using mid-fidelity wireframes. At the same time, we examined how to present auxiliary functional nodes such as “accessing video surveillance,” which together form an initial decision-making loop from problem discovery to on-site verification.
High-fidelity
Based on the established wireframes and design principles, we finalized the high-fidelity visual design. Utilizing a systematic visual hierarchy and intuitive interaction paths, principles like “Focus & Clarity” and “Intuitive Exploration” were translated into clear interface language, resulting in a professional, efficient data decision platform.
Internal Testing
Before finalizing the proposal, we held an internal team review to gather diverse perspectives and improve the design draft. The feedback mainly focused on the presentation logic of business data and interaction efficiency.
Review Scenario 1
Assume you are the Operations Director at the Property Management Headquarters, preparing for the monthly business review. Focus on the "Property Payment" module, and quickly extract key insights for your report based on this month's actual collections, annual cumulative data, and trend analysis.
“Are the monthly actual collection and annual cumulative data clear and intuitive? Does the data presentation effectively support real-time monitoring of fee collection status?”
“Does the 12-month payment trend bar chart clearly reflect business changes? Does it effectively help you identify key months requiring attention?”
Review Scenario 2
Suppose you are a Property Manager for a specific area. Your first task each day is to quickly assess the overall operational health of all communities under your supervision. Today, pay special attention to "Maintenance" quality and efficiency.
“Are the key metrics on the dashboard clearly visible? Can they effectively indicate overall risk?”
“How can you quickly switch to the 'Maintenance' section? Are its core metrics (e.g., resolution rate, satisfaction rate) prominently displayed and easy to comprehend?”
“If the 'Satisfaction Rate' is anomalous, how would you systematically drill down to locate the specific problematic community? Can surveillance footage be conveniently used to further verify the on-site situation?”
Task1
Testing participants confirmed that the dashboard’s monthly actual collection and annual cumulative data are clearly and accurately displayed. The 12-month payment trend chart intuitively reflects business changes, facilitating quick grasp of long-term trend fluctuations (e.g., identifying anomalous months), thus meeting basic business monitoring needs.
Task2
Testing participants confirmed that dashboard metrics immediately reflect overall operational status. When detecting an index anomaly, they can quickly identify problematic communities through step-by-step drill-down operations (e.g., from national to city to neighborhood). Furthermore, the system clearly correlates and displays other relevant data (e.g., surveillance) to support further issue verification.
Review Feedback
Comprehensive Overview—This map is highly intuitive, providing a clear overview of user and business data across all regions nationwide. It's a hundred times better than sifting through Excel reports.
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Efficient Drilling—Smooth drilling from provincial to municipal to neighborhood levels allows for quick identification of problem areas. The video surveillance feature is highly practical, offering true remote management capabilities.
Decision Support — The presentation of business trends and key metrics is clear and straightforward, enabling managers to quickly grasp the dynamics, pinpoint priorities, and support subsequent analysis and reporting.
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Iterations
To ensure the highest quality of the final design, we have carried out the following key iterations based on internal review feedback and identified areas for improvement in high-frequency use cases.
Iteration 1: Intelligent Prompts for Abnormal Fluctuations in Key Metrics
Issue
When key metrics (e.g., “satisfaction”) fluctuate significantly, users must manually compare multiple charts to identify causes, which increases cognitive effort and time spent.
Solution
When fluctuations surpass preset thresholds (e.g., satisfaction falls below 60%), a subtle red alert icon appears on the metric card.
Hovering or clicking the icon reveals an intelligent prompt (e.g., "Low satisfaction may relate to maintenance issues; recommend checking footage for verification"), directly guiding the user to the most critical investigation path.
Issue
Currently, users scroll through over a dozen business metrics using left/right arrows, resulting in low efficiency and cumbersome operation.
Iteration 2: Revamping Core Business Data Transition
Solution
Replace the switch controls with a dropdown menu that expands when clicked. Clicking the current data title reveals all options, allowing for quick navigation and greatly improving browsing and exploration efficiency.
Summary & Impact
1. Validated a New Operational Paradigm:
We delivered a complete design blueprint for a digital operations system. This solution successfully reconstructs the traditional management model into a new paradigm centered on data visualization and online collaboration.
2. Verified the Decision Support Model:
Internal validation confirmed the design's core value in enhancing decision quality, specifically by shifting management from lagged judgment to real-time, collaborative, and proactive intervention.
3. Designed a Cross-Departmental Synergy Mechanism:
By establishing a centralized information hub, the solution clearly defines a feasible path for breaking down organizational silos and achieving smooth, efficient cross-team collaboration.
4. Established the Foundation for Data Assetization:
The design consolidates scattered operational data, transforming it into a traceable and associable decision context laying a crucial foundation for generating future business insights and guiding optimization.