Data Science and Effective End-to-End Data Analytics Processes Emerge as Success Factor in Establishing Competitive Advantage in the Property Sector
By Lane Cooper, Editorial Director, BizTechReports
Decision-makers who depend on effective analysis of high-quality property data are under pressure to harness big data analytics to capture insights that identify new market opportunities, capture targeted leads, and mitigate risk.
Many segments of the community —real estate, insurance, mortgage lenders, and fintechs — are in a race to hire talented data scientists that are in very short supply.
All too often, expensive talent is allocated to perform the administrative work associated with data-wrangling from a wide array of internal and external sources.
Integrating data science into operations will be vital in executing go-to-market strategies and lead to competitive differentiation for organizations tied to the increasingly dynamic property sector, according to John Rogers, chief innovation officer at CoreLogic.
Decision-makers who depend on effective analysis of high-quality property data are under pressure to harness big data analytics to capture insights that identify new market opportunities, capture targeted leads, and mitigate risk. This is especially true for executives with mortgage banks, real estate organizations, insurers, and innovative fintechs.
The problem is that many organizations fail to establish the systems and organizational structures required to execute big data analytics effectively. Inadequate processes and procedures for gathering and effectively analyzing data can result in companies overextending limited resources while decreasing their ability to harness vital insights into constantly changing market dynamics.
“Most organizations are struggling for a great variety of reasons. While many aspire to work with clean data, many fail to establish the systems and processes required to execute this critical aspect of big data analytics effectively. Moreover, many segments of the community —real estate, insurance, mortgage lenders, and fintechs— are in a race to hire talented data scientists that are in very short supply,” explains Rogers.
On those occasions that data scientists are identified and brought on board, many organizations fail to provide the environment necessary to get a full return on their investments in this category of human capital.
“All too often, expensive talent is allocated to perform the administrative work associated with data-wrangling from a wide array of internal and external sources. Too much time, money, and human resources are allocated to reprofiling data and then integrating data sets so that organizations can govern appropriately,” says Rogers.
CoreLogic data demonstrates the significant operational impacts these circumstances have on organizations. Companies end up spending too much and getting back too little in terms of actionable insights that drive effective data-driven decision-making processes.
It doesn’t have to be that way.
“Effective big data analytics strategies -- supported by talented data scientists -- can help organizations closely tied to the property sector mitigate risks, improve margins, and accelerate value delivery to the market. This is especially important in industries closely tied to property, where many demographic shifts, new business dynamics, and multi-peril threats caused by climate change are constantly changing the landscape,” explains Rogers.
Adding insult to injury is the fact that data for many organizations is scattered across an array of repositories located in legacy on-premises data centers and multiple public cloud resources. Aggregating and rationalizing structured and unstructured data from these disparate environments are both complicated and time-consuming, especially when using manual processes.
Bridging the ROI Gap With Purpose-Built Platforms and Clean Data
Addressing these challenges requires a tremendous amount of coordination across multiple disciplines within organizations by establishing common platforms that create the collaborative environments needed to analyze internal and external datasets.
Such a platform can play a pivotal role in integrating data science, analysts, and diagnostic tools into workflows that aggregate data from today’s hybrid environment of on-prem, private cloud, and multiple public cloud sources.
“That is why we have launched CoreLogic Discovery. It is a purpose-build data analytics platform that allows organizations to analyze their own internal data and then compare it to the largest repository of high-quality industry data to secure actionable intelligence that advances mission-critical objectives,” concludes Rogers.
To learn more about the Discovery Platform by CoreLogic, visit:
http://www.corelogic.com/discovery-platform