From data silos to actionable insights: Innovating within legacy constraints for the digital enterprise
Market View
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Interview with
Dr. Ade Awonaike
CHIEF PRODUCT AND DATA OFFICER
RENTT
about
Dr. Ade
Awonaike
Dr. Ade Awonaike is an accomplished data and technology leader with extensive experience across utilities, financial services, and property technology sectors. Currently serving as Chief Product and Data Officer at rentt, he holds a PhD in Data Science and has led numerous successful digital transformation initiatives. His expertise spans GIS, machine learning, and enterprise data strategy, with a particular focus on modernising legacy systems while driving innovation.
How have you approached the challenge of extracting value from legacy systems while driving innovation?
Rather than proposing expensive overhauls, I’ve found success in bridging legacy systems with modern platforms. In one instance, we implemented a cloud data platform on Microsoft Azure that seamlessly connected with existing infrastructure. This approach was particularly effective when working with an organisation comprising multiple underlying businesses, each serving distinct markets and operating independently for over two decades with its own supply chain. By creating a centralised platform, we enabled one business to leverage suppliers from another when their service delivery models and locations aligned better with current strategic priorities. This not only improved operational efficiency but also transformed decision-making capabilities across the organisation. The key is to align system integration with clear business outcomes; it’s not just a technical exercise but about delivering tangible value that addresses specific organisational pain points.
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When people begin to see tangible benefits and gain capacity for more meaningful work, their resistance to change naturally diminishes.
You've mentioned that cultural factors often present bigger obstacles than technical ones. How do you address this challenge?
In my experience, particularly within the utilities sector, legacy systems often become deeply embedded in organisational culture, making teams naturally cautious about change. We addressed this by focusing on quick wins that delivered immediate value. For instance, we automated regulatory reporting processes that had been manual and prone to errors. By leveraging scripted tools and low-code platforms, we reduced report generation time by 60% while significantly improving accuracy. This freed up teams to concentrate on higher-value tasks such as analysis and strategy.
When people experience tangible benefits and find themselves with more time for meaningful work, their resistance to change naturally lessens. The key is to build trust through incremental improvements rather than attempting a wholesale transformation. Alongside automation, we placed a strong emphasis on the explainability of our models, ensuring that subject matter experts fully understood and could engage with the process. This transparency was instrumental in turning sceptics into advocates. When others within the organisation start championing the change instead of you, that’s when you know the transformation has truly taken hold.
Could you share an example of how you've used data analytics to drive innovation?
At rentt, we are developing a machine learning enabled platform that is set to transform the traditional property rental process. Building upon existing property management data, this platform functions like a 'dating site' for the rental market, intelligently matching landlords with potential tenants based on multiple compatibility factors. This approach goes beyond the standardised methods of high street letting agents, creating meaningful and personalised connections that fundamentally reimagine the rental experience. By leveraging advanced analytics, we are not merely enhancing efficiency but pioneering a new value proposition for the market. This project exemplifies how innovation can stem from creatively applying existing data to solve real-world problems and unlock fresh opportunities.
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Instead of starting from scratch, we are building upon existing property management data to create what I'd describe as a 'dating site' for the rental market.
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Rather than having teams manually inspect pipes, we created heat maps showing precise locations where leaks were likely to occur, leading to a 25-30% reduction in actual leak events.
You've had success using location-based data analysis. Could you elaborate on its impact?
In a previous role, I led the integration of GIS capabilities with machine learning to transform leak detection and asset management. Using tools like ArcGIS Pro and Python, we developed a system that combined sensor data, historical maintenance logs, and environmental factors to predict potential pipeline failures. Instead of relying on manual inspections with traditional equipment, we created heat maps pinpointing precise locations where leaks were most likely to occur. This proactive approach resulted in a 25-30% reduction in actual leak incidents within just 7-8 months.
The key to this success was presenting complex data through intuitive visualisations. Rather than overwhelming teams with raw numbers, we provided actionable insights; identifying the exact street and even specific locations to investigate. This spatial perspective proved incredibly impactful as it aligned with how people naturally interpret their surroundings. Additionally, we incorporated historical weather patterns, such as temperature fluctuations and seasonal variations, into our predictive models. Although real-time weather data integration wasn’t available at the time, the historical analysis alone significantly enhanced our forecasting accuracy, enabling us to prevent issues before they occurred.
How do you approach data quality and governance when working with legacy systems?
In the utilities industry, I led a comprehensive data governance initiative to address quality issues at their source. We introduced automated validation processes that flagged inconsistencies at the point of entry, complemented by targeted training sessions to enhance how users handled data. This dual approach of technical solutions and user education resulted in a 25-30% improvement in overall data quality within six months. More importantly, it built organisational confidence in the data, enabling the pursuit of more ambitious analytics projects. Validation rules were carefully defined based on both regulatory and operational requirements, ensuring alignment with business objectives. The implementation involved extensive collaboration with teams, including running parallel manual checks during the pilot phase to build trust in the system. This methodical approach not only improved data quality but also established a foundation for sustainable, data-driven decision-making across the organisation.
about
rentt
rentt is an innovative property technology company transforming the rental market through advanced data analytics and machine learning. The platform revolutionises how landlords, property managers, and tenants interact by providing intelligent matching capabilities and streamlined property management solutions. Currently operational in the UK, rentt is preparing for international expansion.
We are the global market leader in geographic information system (GIS) software, location intelligence, and mapping.
Esri UK is the exclusive provider of Esri solutions in the UK. We take a geographic approach to problem-solving, brought to life by modern GIS technology. We are committed to using science and technology to build a sustainable world.
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