Achieving strategic, London-wide outcomes, such as measuring retrofit success and accelerating Awaab’s Law compliance, requires more than just technology. It requires a robust, iterative approach to partnership and data development. At LOTI, we’re using a ‘test and learn’ methodology to scale our IoT (Internet of Things) sensor data project with Warmer Homes London (WHL).

Why ‘Test and Learn’?

Scaling any major, cross-organisational data initiative presents significant risks, from technical integration challenges to securing internal capacity and partner buy-in. To mitigate these risks and ensure the project can scale effectively, we are pursuing a ‘test and learn’ approach tailored to three distinct categories of ‘early adopter’ personas across the Strategic Partnership.

The benefits of this approach include testing different approaches rapidly and concurrently, working in the open and sharing our findings as we go. By setting a hypothesis against each persona, we can design three pathways that we can test to see which best helps to achieve our outcomes. 

• Persona: High-flyers
  • Description: Partners who are already using sensors in their SHF properties.
  • Challenge: How to incentivise and support sharing of existing data at a London scale.
  • Hypothesis to test: Subsidy and Value-Add: Incentivising data sharing through Information Governance support and co-designed insights (i.e. combining with smart meter data) provides sufficient value.
• Persona: Innovation Leads
  • Description: Partners with digital capabilities outside of their retrofit teams.
  • Challenge: How to coordinate internal teams (Smart City, Retrofit, Maintenance) for effective deployment and management.
  • Hypothesis to test: Internal Coordination: Leveraging existing digital innovation capabilities and networks (like a LoRaWAN) can accelerate sensor deployment and integration.
• Persona: New Enthusiasts
  • Description: Partners interested in adoption but new to IoT sensors and hesitant about costs.
  • Challenge: How to overcome cost barriers and build confidence in procurement and implementation.
  • Hypothesis to test: Financial Incentive: A co-designed, targeted subsidy scheme can support partners to adopt sensors and share data, proving valuable for WHL and SHF partners.

These three personas provide an opportunity to test different pathways to achieve Warmer Homes London’s outcomes, representing over 50% of homes that will be retrofitted under Warm Homes: Social Housing Fund in the next three years.

The Iterative Process

For each of the three hypotheses, we will run a rapid, concurrent test cycle:

1. Experiment Design: Define the specific steps, resources, and partners involved (i.e. co-designing the subsidy scheme with New Enthusiasts or mapping current systems with High-flyers).
2. Collect Data: Execute the step (i.e. High-flyers sharing sample sensor data; New Enthusiasts providing feedback on the subsidy model).
3. Analyse Results: Collect qualitative and quantitative feedback on the process, efficiency, and value of the generated insights.
4. Iterate Based on Findings: Either scale up the viable pathways by increasing data flows and partner involvement, or adjust/pivot the approach if results indicate it’s not working.

This continuous iterative process ensures we can fail fast and learn quickly, allowing the project to adapt in real-time to partner needs and technical challenges. By rapidly iterating and sharing our findings, we minimise risk and accelerate the identification of the most efficient, cost-effective models for pan-London data projects.

Preparing for Scale

Beyond the initial hypothesis testing, the project is moving forward with key infrastructure work packages to enable pan-London scale:

• Grant Funding Agreement (GFA): Developing the GFA is critical for the subsidy scheme, as it will formally include the Data Sharing Agreement (DSA), ensuring a secure and ethical legal basis for partners to share sensor data with WHL in exchange for funding.
• Data Product Development: Using the LOTI Data Methodology, we will develop a prototype data product. This product will be the engine that brings together the sensor and smart meter data to deliver the agreed-upon insights, such as understanding cost/benefits of interventions and uncovering enhanced asset archetypes.

By focusing on these three persona-based pathways, we are not just deploying sensors; we are actively mitigating the risks of slow adoption and low data sharing identified in the earlier pilot. 

This data-driven, iterative approach is essential for successfully scaling complex projects that deliver measurable and impactful outcomes for residents and for London’s net-zero ambitions. 

To follow all the latest updates on our project, visit our Warmer Homes London: IoT Sensor project page.