Case Study:
Near Real-Time Insights for Smarter Commercial Buildings with AWS IoT
Are We Really Using Our Office Spaces the Way We Think?
If you have worked in a busy hospital, you have probably heard the same frustration we heard on this project:
In every large organization, there is always a debate about space. Some teams feel the office is too crowded. Others say meeting rooms are impossible to book. Yet, when we take a closer look, the truth is often different from what people expect.
That is exactly what happened in this project. A global organization managing millions of square feet wanted to understand how their spaces were truly being used. They had badge data, but something felt off. The numbers did not match what people were seeing on the ground.
So, we stepped in to answer one big question:
How much of this building is actually being used, and how can we measure it accurately in near real time?
How We Turned a Complex Building into a Smart, Data-Driven Space
The organization had a massive footprint of corporate real estate and needed accurate insights to optimize space, support staff, and reduce operational costs. The first instinct was to use badge data to measure occupancy, but it quickly became clear that badge data alone could not tell the whole story.
People often badge at once and move across the building throughout the day. Some do not badge at all. Others badge multiple times. The data simply was not reliable enough.
This is where we introduced a new approach:
IoT sensors are connected through AWS, feeding real-time information into a scalable cloud analytics pipeline.
Smart Sensors Connected Through AWS IoT
We deployed sensors at key points like floor entrances and exits. These sensors:
- Published data through MQTT
- Used mutual TLS for secure communication
- Connected directly to AWS IoT Core
- Allowed us to collect signals without touching the client’s internal network
This gave us clean, continuous, and reliable data about how people moved through space.
The Results: A Scalable Architecture for Smarter Buildings
The outcome provided clear, validated insights that leadership could trust.
Clean and Reliable Occupancy Data
We proved that badge data was not showing the full picture. The sensor-based data provided accurate and dependable measurements of space usage.
Operational Insights
– Cafeteria Services
The analysis confirmed that the current operating hours were already optimal.
– Meeting Space Optimization
We identified peak usage times and behaviors, helping staff improve room availability and reduce booking friction.
– Amenity Usage
Measures how wellness, collaboration, and support spaces are used (e.g., collab space on NT11).
– RTO Monitoring
Tracks the effectiveness of return-to-office strategies using reliable occupancy trends.
– Floor Counts
Provides floor-level usage insights to support leasing and portfolio decisions.
– Life Safety
Offers near real-time occupancy data to support emergency drills and safety procedures.
– Historical Data
Invaluable for validating design decisions and guiding future fit-outs and renovations.
– Lease Space Reduction
Identifies underused areas and enables strategic, data-driven consolidation.
– Design Validation
Validates the performance of meeting rooms, hot desks, collaboration zones, and amenities.
– Corporate Asset Validation
Confirms usage of major assets such as cafeterias, training centres, and libraries.
– Right-Sizing New Fit-Outs
Uses historical occupancy patterns to determine ideal space allocation for upcoming projects.
Designed for Global Scale
The architecture we delivered was built to scale. Additional sensors and new buildings can be added with minimal effort. Every component is cloud-driven, flexible, and cost efficient.
This project sets a strong foundation for long-term digital transformation in how buildings are managed. It also reinforced the value of a data-first strategy for any organization looking to improve space, comfort, and efficiency.
A Cost-Optimized AWS ETL Pipeline
Once the sensor data reached AWS, the entire processing workflow ran automatically.
1. Data Ingestion
Incoming sensor events were streamed through AWS Kinesis Firehose.
2. Storage
Raw and processed data landed in Amazon S3, forming a secure and scalable data lake.
3. Processing
AWS Lambda functions transformed and aggregated the data with no servers to maintain.
4. Analytics
We used AWS Athena to run SQL queries directly on the S3 data.
5. Visualization
We built two layers of insights:
- AWS QuickSight dashboards for fast, near real-time business analysis
- A 3D heatmap created through Autodesk Forge and a BIM model for immersive visualization
This allowed teams to see movement patterns, hot spots, cold spots, and usage trends in a simple and powerful way.
AWS Services Used
The following AWS services were used to support real-time data collection, storage, processing, scalability, and visualization:
- Amazon EC2
- Amazon ELB
- Amazon S3
- AWS IoT Core
- AWS Kinesis Firehose
- AWS Lambda
- AWS Athena
- Amazon QuickSight
- AWS IoT Events
- AWS Step Functions
- Amazon Route 53
- Amazon CloudFront
- Amazon CodePipeline
This stack allowed us to build a cloud-native solution that is efficient, reliable, and easy to grow.
Building the Future of Smart Buildings
This project shows how combining IoT devices with AWS analytics can transform commercial buildings into intelligent, data-driven environments. When we move away from assumptions and rely on real-time data, we unlock new levels of operational efficiency and create better experiences for employees.