The Five Signs that Your Data Services Provider is Ahead of the Curve

Property characteristic and mortgage finance data are the fuel that powers real estate analytics and technology, but it can be hard to get. CoreLogic breaks down data accessibility barriers, so brokerages and technology companies can focus on innovation and providing the best-in-class information to clients. Since data is the building block of today’s technology innovations, to stay competitive, data providers must do more to leverage the growing number of data sources. These data sources are becoming both more complex through rapidly advancing technology and more extensive, making it even harder to distill meaningful information.

As such, data service providers are embracing the latest trends to move into the next phase of their evolution. Regardless of what industry you come from, when you’re seeking new intelligence to power your business, it’s important to keep the following five qualities in mind as you’re evaluating a potential provider.

Data Privacy

With the enactment of General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA), you have probably encountered more instances of companies taking steps to improve access to your data. A higher level of transparency on process and utilization is expected of businesses that accumulate personal data as well as the security measures behind their collection and storage. Responsible data providers do not ask for irrelevant information from consumers, are transparent in their approach and empower consumers to manage their sharing preferences.

In addition, a robust vendor procurement program should be in place to ensure that wherever the data goes, its confidentiality remains assured. This should be coupled with a program that leverages the latest encryption techniques and algorithms, protecting data from prying eyes.

Data Quality

Data quality proves its worthiness by meeting the standards of accuracy, coverage, recency, and completeness. When absolutes are not available, accuracy can be approximated with statistical modeling, artificial intelligence (AI) and machine learning, or external sources of information can further enrich the data. By predicting and preventing the emergence of “dirty data,” a system can take unqualified information into account or ignore it prescriptively.

In the world of data collection, CoreLogic® recognizes that order is everything. We begin by applying strict standardization to all incoming data. Because we gather data from a wide array of sources in numerous formats with all kinds of anomalies, we adhere to a rigorous program to create a structural foundation.

CoreLogic takes the vast number of disparate sources of property data and connects them with a unique ID to create a persistent, single source of truth for a property. This innovative industry standard called CLIP®, together with our advanced geospatial data sets, creates a 360-degree view of the property that fuels the housing ecosystem.

Having accurate data won’t always yield high-quality outcomes if only a portion of available attributes is complete. The frequency at which your data sources are updated also contributes to your data quality. At CoreLogic, we take advantage of the extensive suite of data sources available to us and apply intelligence and innovative data science methods to ensure the utmost quality and completeness. Our model allows us to impute values more accurately across a national property landscape.

Data Security, Risk Management and Mitigation

Hackers and social engineers have access to more data today and continually adapt their approach to steal data. Measures should be in place to detect, prevent and respond to data concerns. Common best practices include employee education, phishing campaigns, multi-factor authentication, vulnerability scans and employee background checks. Automation of risk processing as well as continually advancing threat monitoring and detection methods can identify anomalies that lead to data loss.  By applying AI, cybersecurity teams can address the ever-escalating sophistication of attacks. 

CoreLogic data centers are built with business continuity and threat prevention in mind so that your experience is continuous, and your data is safe. CoreLogic has a formal, ongoing internal program within CoreLogic that seeks to educate, enable and mobilize our employees to protect our company’s data.

Data Storage

Transitioning to software-defined storage offers scalability benefits by running data on outside commodity servers with little or no modification. Cloud platforms have become the norm due to cost savings, productivity increases and resource optimization benefits. A multi-cloud approach is preferred since it allows a data provider to operate independently of any single vendor and improves business continuity in the event of a provider failure.

Data Integration

The latest evolution of data integration includes stream processing; this means that data is processed as soon as it is received. Data that comes in from websites, legacy applications, devices, social media or other ingestion sources are immediately prepped for analysis. Machine learning shepherds this process by learning from processed events and automation patterns to accurately extract and deliver data appropriately.


Overall, successful data service providers implement a continuous process of studying best practices, adapting business processes and planning for the future.

With that in mind, these are the top five practices to look for:

  • Transparent collection of both public and personal data as well as robust software to protect data.
  • The highest measures of accuracy, coverage, recency, and completeness should be applied to your datasets. At CoreLogic, we connect critical touchpoints of property datasets to deliver accurate, comprehensive and up-to-date data.
  • Physical security measures, employee education and automation should be considered to ensure strong data security.
  • Utilizing scalable storage has twofold benefits: cost savings and business continuity in the event of a provider failure.
  • Stream processing of data leads to data optimization and efficiency for you and your projects.

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