Data Drives Growth

If real estate lenders and investors are to make sound economic decisions accurate data is essential, yet the data available is often of poor quality and offers conflicting information. In order to minimise inefficiencies, it is critical that effective remediation techniques are utilised to determine your value creation strategy. 

 

Despite the importance of comprehensive data remediation, innovations in the field were few and the process long continued to remain time-consuming, burdensome on resources, and a source of disquiet amongst over-tasked employees. However, recent advancements in artificial intelligence (AI) technology has seen successful investors, financial institutions, and other real estate managers utilise automated data remediation in order to improve decision-making, eliminate inefficiencies, and maximise the value of assets. 

  

Data remediation is a multifaceted process that is often misunderstood. Generally speaking, it is the cleaning, organising, and transferring of data from different formats (e.g., spreadsheets, PDFs, public registries, etc.) to allow for meaningful analysis. In reality, much of the data remediation process’ importance lies in the correcting and replacing of incomplete, inaccurate, and obsolete data. 

 

Data Remediation is also a hugely laborious process, and one that saw analysts like Yousra Badra, now supervisor to Recognyte’s data and analytics team, working well past regular office hours to complete assignments on time. Working as part of a team that sorted data manually, Yousra recalls the lengths she and her colleagues would go to in order to ensure their findings were accurate. For example, depending on the task this could mean making dozens of calls, conducting deep-dives on the internet, opening and scouring documents one at a time, and on occasion even driving to physically inspect the asset themselves. This process was not just hugely wasteful, but as a 2007 Accenture Survey showed, middle managers like Yousra spend more than a quarter of their time looking for necessary information that is often incorrect. And yet, data remediation remains a crucial part of effective asset management because managers need to understand their assets to make strategic economic decisions. 

 

Typically, real estate lenders and investors accumulate large sums of data over an asset’s lifetime, and yet in a 2019 KPMG Global PropTech Survey the results showed that only 25% of respondents had a well-established data strategy that allowed for the effective collecting and analysing of datasets. In order to compensate, the data available on hand would be sent to teams like Yousra’s, who after remediating the data would begin sorting through the resultant data tape – a term used to describe the files containing organised, post-remediated data – before moving on to the verification process. Often, their findings would return poorly categorised assets, gaps in vital information, and data that was no longer relevant to the assignment. 

 

Recognising these systemic failings, developers specialising in portfolio management like the team at Recognyte worked to develop software that would automate the process while limiting the inefficiencies associated with manual data remediation. As a result, analysts are now able to streamline the process by uploading data to the system of their choosing, such as Recognyte’s own DataScout. These technologies utilise machine learning techniques, natural language processing, and image recognition to cross-reference different sources, return more accurate asset evaluations, and do so while leaving a clear audit trail. 

 

All that being said, it is important to keep in mind that while automated data remediation has improved the process, challenges remain. The biggest obstacle facing those tasked with sorting large portfolios of assets is that much of the information available comes from sources the analysts have no control over. This, of course, can lead to corrupted datasets that paint inaccurate pictures. However, steps can be taken to limit such errors, including the hiring of competent analysts to supervise the automated process. 

 

In today’s contemporary real estate climate, managers of all kinds are faced with a choice. They can do nothing and remain dependent on poor data. They can continue to utilise manual data remediation techniques and accept the associated burdens. Or they can embrace change and reap the benefits of automated data remediation. Regardless of the decision made, it is essential that as is true of Yousra, independent investors, financial institutions, and real estate managers alike remember one simple truth: “everything starts with data remediation.”