5 min read

Challenges in Data Collection and Overcoming Them: Insights for the Real Asset Investment Industry


  • author-avatar
    Maja Christenson
  • author-avatar
    Magnus Hornef

Navigating the landscape of data collection in the real asset investment sector presents unique challenges that require nuanced solutions. This article delves into three pivotal areas: the inherent complexity of data sources, the critical importance of traceability and quality, and the need for data structure and its effects on property transactions. EVORA’s Chief Data Officer Magnus Hornef offers solutions aimed at enhancing the efficacy and reliability of data collection processes, drawing from a decade of experience within the field.

Challenge 1:

Navigating the Complexity of Data Sources 

Property portfolios dispersed across multiple countries obviously face the challenge of scattered data, as practices and standards vary from market to market, but the challenge can be just as big in domestic portfolios. The diverse landscape of data sources, encompassing both connected and non-connected devices, monthly invoices, sub-meters, the presence or absence of national data hubs, and so on, makes data collection complicated – regardless of where your property portfolio is located.    

This fragmentation leads to an overwhelming reliance on manual processes, such as the use of shared Excel sheets and extensive email chains, making data compilation a resource-draining and oftentimes unreliable process.  

Magnus Hornef shares his perspective on finding a path through this complexity: 

The data itself always exists in one form or another – the big challenge is accessing it. Too many have a consultant and manual approach, making it more complicated and time consuming the more data sources and data that needs to be collected. By using an advanced data management platform that automates collection and simplifies new connections, organisations can save time and ensure seamless flow of business-critical information.” 


Challenge 2:

Ensuring Traceability and Quality of Data 

The significance of accurate sustainability data cannot be overstated, given its potential impact on property valuations and interest rates. It should be handled with the same careful oversight as you would pay to bookkeeping, to ensure impeccable traceability and quality. Yet, issues with data quality and traceability persist, often due to manual data collection methods that are prone to errors and lack accountability. 

Excel files with manually entered values poses risks to the traceability and integrity of critical data, making your portfolio vulnerable in an audit. And recognising errors, such as faulty meters or anomalous consumption patterns, becomes challenging without a robust framework for data verification. 

Magnus Hornef offers his insights on how to uphold the integrity of sustainability data: 

“Data quality is about processes, not number of errors. Real asset investors need systems that provide end-to-end traceability from data entry to reporting so that each data point can traced back to where it came from. Combine that with automated validation as well as curated, and you can establish a process where you learn from each data error and thereby enhance data quality for decision-making and reporting.” 


Challenge 3:

Addressing Structure and Property Transactions 

The sale of a property illustrates a critical juncture where data management practices are put to the test. Traditional processes often result in a restart of data collection efforts, with previous agreements nullified, and crucial data trapped in the systems of former owners.  

When ownership of a property is transferred, the task of mapping up meters and setting appropriate data structures often starts all over again. With a comprehensive mapping of what meters data originates from, and what parts of the energy use it concerns, companies will be able to sell a property, with an appending package of raw data – beyond final reports like GRESB reports.  

Highlighting the importance of data management in property transactions, Magnus Hornef elaborates on the need for a systematic approach that remains robust across ownership changes: 

“Developing a systematic approach to data management that transcends ownership changes will be essential in future transactions. This entails the creation of a digital data package that accompanies the property throughout its lifecycle, ensuring continuity and accessibility of information post-transaction. To facilitate this, adopting a universal data structure that is documented, transparent, and easily transferable between stakeholders is crucial. Such a structure not only simplifies transactions but also adds value to the property by providing clear insights into its energy performance and sustainability metrics.” 


Solve your data collection headaches with EVORA Managed Data Services, powered by METRY 

Addressing data collection challenges necessitates a strategy focused on digitalisation, quality assurance, and structured data management. By confronting these issues, real asset investors can improve their data’s efficiency, reliability, and value, driving better decision-making and sustainable growth in the real estate sector. 

EVORA Managed Data Services, powered by METRY, ensures every aspect of data collection is meticulously managed. Our technical expertise supports all necessary collection methods to integrate with your environmental data sources seamlessly. Our internal validation systems, powered by AI, detect irregularities or outliers, providing full traceability for each value in our database to meet audit requirements. Our platform allows for structured, clear, and exportable data, enabling systematic data management and the transfer of structured data packages. 

This proactive approach not only streamlines operations but also positions organisations to meet the evolving demands of sustainability and energy efficiency in the real estate sector. Download our data brochure, or contact us to learn more about how EVORA Managed Data Services can help you overcome your data challenges.