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Findings from CoreLogic Survey: The Role of Data Science in Deriving Greater Insights into Property Market Dynamics
SILVER SPRING, Md. - Marylandian -- Few industry segments depend more on getting the most out of investments in big data analytics and data science than the property technology (PropTech) sector. However, when it comes to finding insights from hundreds of thousands -- if not millions (sometimes billions) -- of data points to drive desired business outcomes, PropTech leaders may be falling short of meeting expected performance objectives, according to a recent BTR-100 survey commissioned by CoreLogic.
"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.
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Specific reasons for suboptimal performance of data science operations cited by senior data science, business intelligence and technology management professionals in the survey, include:
To read the rest of this industry briefing report from CoreLogic, visit:
https://bit.ly/DrivingROIwithDataAnalytics-CoreLogic
"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.
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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%
To read the rest of this industry briefing report from CoreLogic, visit:
https://bit.ly/DrivingROIwithDataAnalytics-CoreLogic
Source: BizTechReports
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