5 big data challenges holding companies back
Businesses are looking at how to better tap the potential of big data and proptech
Data is becoming an indispensable aspect of decision making in commercial real estate.
Sensors are monitoring how space is used, providing key insights to businesses shifting towards hybrid work models. Artificial intelligence and automation technologies can track business processes, helping identify opportunities to improve employee and customer experiences.
Yet many companies are facing challenges in gathering and analyzing data. Often times datasets provide an incomplete picture, limiting the value of insights gleaned, while setting up systems across 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 Outdated systems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 such as spreadsheets to collate data, risking not only manual errors but also keeping other relevant data separate. Such data silos often make it harder for teams to share and use information, ultimately impacting the quality of insights.
“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
A lack of staff with the right skills is a common limitation in collecting and analyzing data efficiently.
For example, many 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.”
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3. Inconsistent standards
Companies in the midst of digitizing operations often have varying processes for collecting data in different teams. This can result in incompatible data formats, requiring time-consuming standardization that stymies information sharing, analysis and adoption of the new technologies.
For businesses starting to invest in proptech, the wide array of IoT devices and vendors can also appear a minefield, with potential incompatibility issues 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, which provides insight into human behaviour, is subject to data privacy regulations that differ between jurisdictions, from general data protection laws in Europe, Brazil or Singapore, to varying U.S. state privacy laws. 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 different departments, and even with the office space itself, companies can gain insights into how to improve every aspect 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.”