Unclean data is a pervasive issue within the real estate industry, impacting portfolio performance and decision-making. Asset managers are finding their portfolios hindered by inaccurate, incomplete, or outdated real estate data. This article explores the repercussions of dirty data and highlights solutions using AI in real estate and data room technology to mitigate its effects.
Dirty Real Estate Data’s Impact on Asset Managers
Dirty data negatively influences crucial stages like acquisition and underwriting, persisting throughout the asset lifecycle. Siloed processes and non-integrated software worsen the problem, resulting in difficulty maintaining liquid assets. Manual data entry, inconsistent formatting, and lack of permission control contribute to this issue.
Unveiling the Consequences
Even minor spreadsheet errors can have catastrophic outcomes for significant portfolios. Inaccurate data contaminates investment, portfolio, and asset management decisions, potentially leading to legal issues and a reduction in creating liquid assets
AI’s Role in Data Optimisation
The use of AI in real estate, such as Stonal’s platform, automates commercial real estate data collection, ensuring consistent updates and cleanliness. By streamlining the process, asset managers can guarantee accurate, reliable data that enhances decision-making capabilities.
Time and Cost Implications
Dirty data extends reporting processes, with fixing inaccurate numbers consuming valuable time. A Gartner study reveals that bad data can cost companies up to 15% of revenue, as analysts spend considerable time validating data. Automating data collection and processing with AI-powered platforms reduces time wastage.
Tackling Manual Real Estate Data Entry
Manual data entry not only consumes time but also results in transcription errors. Automation through software like Stonal’s AI-powered platform saves time and minimises the risk of unclean data.
Addressing Operational Risk
Dirty data escalates operational risk for asset managers due to failed processes or systems. Siloed processes hinder data sharing, leading to difficulties in locating and interpreting key data points. Centralised data repositories, such as Stonal’s data room, combat operational risk by digitising assets and offering version control.
Quality Data is Key
Data-driven decision-making requires trust in the data’s quality. AI in real estate reduces errors from manual entry, ensuring swift and accurate data collection. Storing data in a centralised location, like a data room, eliminates anomalies and inconsistencies, bolstering asset managers’ confidence in their data and underwriting liquid assets.
Conclusion
Dirty data’s adverse effects on real estate portfolios and decision-making can be mitigated through AI-powered solutions and data room technology. Reliable, clean data is essential for informed decision-making, and by adopting these technologies, asset managers can prevent the far-reaching consequences of dirty data.
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