SILVER SPRING, Md. - Marylandian -- Data science is revolutionizing value generation and customer journeys in the fintech and financial services sector. It has emerged as a critical differentiator that enables companies to meet critical business objectives and complete time-sensitive processes—such as credit risk scoring and property value assessments—more efficiently and affordably.

However, a scarcity of data scientists and the complexity of managing data scattered across multi-cloud and hybrid platforms present challenges for companies looking to harness the full potential of big data and analytics. Many leaders are rapidly concluding that simply putting diverse data into cloud environments or data lakes does not automatically lead to the generation of meaningful insights that advance their market positions.

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"Extracting value from large data sets is not a simple proposition. It requires sophisticated integration, transformation, enrichment and orchestration that is very difficult to execute across heterogeneous enterprise computing infrastructures. Data scattered across many different locations in various formats create confusing and difficult-to-rationalize environments. Bringing this data together at scale is far from trivial," explains Mark Weaver, head of real estate tech solutions and data partnerships at CoreLogic, a leading global property information, analytics and data-enabled solutions provider.

Success is Found in the Nuances

Organizational structures and processes established to execute big data analytics strategies are often flawed. Poor design and inappropriate allocation of expertise and resources are common in the industry, leading to outcomes that inhibit return on data science investments.

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Adding insult to injury is the nomadic nature of the data science talent pool. Because data scientists and engineers are in such high demand, they often move from one company to another—and from one industry to another—as different organizations bid for their services. As a result, many data scientists and engineers tend to be generalists rather than industry-specific specialists. It presents a major challenge for organizations interested in establishing sophisticated data analytics programs in the property sector.

"Success in data science, predictive modeling, and analytics is often found in the nuances. If you're a data scientist or analyst from the automotive industry and suddenly find yourself in a prop-tech or fintech environment, there is a whole array of industry knowledge that needs to be mastered quickly," says Heidi Russell, director of strategic accounts at CoreLogic.

To read the rest of this industry briefing report, visit:
https://bit.ly/CoreLogicFintechEntries