Businesses are looking at how to better tap the potential of big data and proptech
Data is becoming an indispensable part of decision making in commercial real estate. Sensors monitor the use of space and provide important insights for companies switching to hybrid working models. Artificial Intelligence and Automation systems can track business processes and help identify opportunities to improve employee and customer experiences. However, many companies face challenges when it comes to data collection and analysis. Data sets often provide an incomplete picture, which limits the value of the knowledge gathered, while the configuration of systems in different countries can be a regulatory minefield.
“Without good data practices that result in high-quality data and insights, companies are a disadvantage when it comes to reducing costs, preparing for risks and finding opportunities,” says Michael Thompson, Head of BI & Data Analytics, Americas, at JLL Technologies.
Here are some of the most common hurdles businesses are facing in making the most of proptech, according to JLL’s Transform with Technology report.
1. Outdated systems
Many companies use legacy tools like spreadsheets to collect data, with not only manual errors but other relevant data kept separate. Such data silos often make it difficult for teams to share and use information, which ultimately affects the quality of knowledge.
“Quality of information and the completeness of datasets are common problems,” says HoChun Ho, Head of Enterprise Data Governance at JLL. “To embrace the Internet of Things, companies have to be able to process the large volumes of data, which requires machine learning and automation tools.”
Establishing a data governance policy – and hiring for data governance skills – is key for employees to know how to collect and understand data, and where to access it. “Data governance enables companies to know they can trust their data,” Ho says.
2. Limited data skills
Staff Lacking with the right skills is a common limitation in efficiently collecting and analyzing data.
For example, more companies use dashboard software that assimilate and analyze all data collected across a company’s operations. But employees often require training to get the most out of such tools.
“The practice of gathering and using data needs to be integrated into workflows,” Thompson says. “While hiring data specialists supports the shift towards more data-centric decision making, good data practices rely on all employees being able to use data in their daily work.”
3. Inconsistent standards
Companies in the midst of digitization often have different processes for collecting data on different computers, which can lead to incompatible data formats that require time-consuming standardization and make it difficult to exchange information, analyze and adopt new technologies. For businesses starting to invest in proptech, the wide range of IoT devices and providers can also seem like a minefield, and potential incompatibility issues are a barrier to further investment.
“Although there are vendors focusing on software to integrate disparate products, there is no industry standard for storing and protecting data gathered by proptech devices, which can create friction that currently disengages employees,” says Michael Ewert, Global Head of BI & Data Solutions, JLLT. “Companies need to assess what they want to achieve and how much data standardization they need to support these outcomes, which is part of establishing a clear data governance policy.”
4. Complex privacy regulations
Proptech data that provides information about human behavior is subject to data protection regulations that vary depending on the legal system, from general data protection laws in Europe, Brazil or Singapore to different data protection laws in the United States. For companies trying to comply with requirements in multiple countries, this can be a major roadblock.
“All these different technologies have to be able to support increasingly strict privacy requirements, while different systems within a company’s network may contain more private data that needs greater protection,” Ho says.
Projects often stall because stakeholders are unsure about compliance around data collection that would drive decision-making, adds Thompson, highlighting the need to train – or hire – for expertise in data privacy law.
5. Lack of a holistic data strategy
With an expanding range of technologies that monitor environmental, occupancy and operational data, integrating all these data points can be a challenge.
Companies require a holistic strategy that defines every data stream and how it interacts with other building data, says Thompson. “A robust data strategy needs to define, check, organize and distribute data to the places it needs to go,” he says.
By investing in datahub tools like dashboards that assimilate information across departments and even into the office space itself, companies can gain insight into improving all aspects of their business.
“Every company needs to understand data to create their competitive edge,” Ewert says. “Occupiers are now focused on how to enable the right hybrid workplace, and investors on how to revamp existing buildings to meet future demands. Good data empowers these decisions.”
