“Significantly less than half (42%) of PropTech professionals surveyed reported returns on investment from big data and AI initiatives that exceeded $1 million. Leaders struggle with fragmented data sources, integration challenges and talent to identify and exploit the insights that will set them apart from competitors in this extraordinarily competitive environment,” says John Rogers, Chief Innovation Officer of CoreLogic, whose team commissioned the study, entitled: The Role of Data Science in Deriving Greater Insights into Property Market Dynamics.
Specific reasons for suboptimal performance of data science operations cited by senior data science, business intelligence and technology management professionals in the survey, include:
- Fragmented data sources are difficult to rationalize — 45%
- Internal and external data is difficult to integrate — 40%
- Transition from analysis to intelligent modeling is too slow — 36%
- Absence of dedicated solutions optimized for property data-analytics — 35%
- Lack of internal maturity with AI/ML applications — 31%
- Data scientists lack property-sector domain expertise — 20%
“The volatility in today’s housing market places a premium on having the most up-to-date information for those operating in markets related to property,” says Rogers. “It is difficult for property-related companies to handle all the variables that influence price forecasting. It is for this reason that big data analytics and modeling are quickly becoming critical components for property-related companies.”
To read the rest of this industry briefing report from CoreLogic, visit:
https://bit.ly/DrivingROIwithDataAnalytics-CoreLogic
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