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The Growing Imperative Around Data Enablement in an AI-Powered Future

Poor data quality costs U.S. businesses nearly $13 million per year on average, according to a Gartner study. Much of that loss stems from bad decision making based on incomplete or inaccurate information. But there’s also an opportunity cost, as poor data quality prevents organizations from leveraging data-driven technology to reduce costs and improve efficiency. Organizations in the real estate industry, in particular, have access to technology that offers game-changing capabilities and insights. Unfortunately, many companies aren’t “data enabled” to produce the accurate, reliable and timely information these solutions require.

April’s OSCRE Innovation Forum brought together industry experts to explore the challenges of data enablement and the evolving landscape of data management, analytics, and artificial intelligence (AI). OSCRE’s Executive Director Richard Reyes and Technical Director Chris Lees were joined for the session by Jeff Huckaby, CEO and Founder at Versalytix.

During the webinar, the panelists discussed how organizations can gauge the current status of their data enablement and use data standards as a foundation for lasting improvement.

 
Measuring Analytical Maturity 

While the tenets of data enablement are well-established, how they manifest in the real world varies widely from one company to the next. That can make it tricky to determine where your organization stands on the road to full data enablement. During the webinar, the panelists discussed one useful tool that was developed by Gartner to address this problem. 

Gartner’s Data Analytics Maturity Model lays out four stages of analytical capability:

  • Descriptive analytics answers the question, “What happened?” For example, the temperature inside a building soared because a component of the HVAC system failed.
  • Diagnostic analytics allows you to answer the question, “Why did it happen?” An inspection of the system revealed that the compressor shut down due to worn bearings.
  • Predictive analytics will help you answer the question, “What will happen?” Analyzing maintenance and repair records across multiple properties shows that this compressor model has a history of being hard on bearings and is likely to fail again within 18 months.
  • Finally, prescriptive analysis empowers organizations to answer the question, “How can we prevent or change what will happen?” Based on the projected failure timeline, facility managers amend the maintenance schedule so that the bearings are checked, cleaned and lubricated every nine months rather than once a year, reducing the likelihood of another unexpected breakdown.

Pinpointing which of these analytical approaches your organization takes to problems like equipment performance, space utilization and portfolio optimization can provide a starting point for taking steps to improve data quality.

 
AI is Making Data Enablement a Requirement for Business Survival

Being able to anticipate what will happen in the future and use that information to benefit the organization – the top two tiers in Gartner’s maturity model – are big leaps forward. But AI is taking these capabilities a step further by enabling advanced analytics and enhancing and expanding automation.

For example, analyzing maintenance and repair records to determine that you need to inspect a piece of equipment more frequently will help you avoid a costly and disruptive failure. But it also means more work for technicians who may already be stretched thin. Using internet-of-things devices to collect real-time noise and vibration data, AI-enabled facility management software can detect anomalies in equipment performance and automatically issue a work order when attention is actually needed, not on some arbitrary schedule.

Most organizations in real estate haven’t yet implemented the data infrastructure required to take advantage of these innovations, and one of the primary obstacles is digital literacy. Businesses just don’t have the in-house expertise or skills to manage their data properly. But AI is helping here, too, by allowing stakeholders to interact with real estate technology using natural language. A tipping point is approaching when the industry will overcome their current data-related challenges and these applications and capabilities will become commonplace. Organizations that aren’t prepared for that eventuality risk being left behind – quickly.

 
The Role of Data Standards in Improving Analytical Maturity

Data standards, like OSCRE’s Industry Data Model™, allow real estate organizations to develop a comprehensive data management program without having to reinvent the wheel. Aligning internal data definitions with those used throughout the industry helps ensure compatibility with leading real estate technology solutions and makes it easier to train employees on their use. It also paves the way for data sharing with partners, clients and service providers that can deliver additional benefits. The data consistency that standardization brings is also critical to allowing different systems and applications to exchange information seamlessly. Finally, data standards are crucial for scalability – the ability to expand applications to meet growing demands.

OSCRE is here to help bring real estate’s digital future into focus. Click here to learn more about how OSCRE can help your organization develop a future-proof, standards-based data management framework, and about the many benefits of becoming an OSCRE member.