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.
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