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NORC at the University Chicago
NORC’s continual research into data engineering tools and methods ensures that our clients get the full benefit from their data.

NORC has a strong commitment to continuous improvement of its data engineering and data management capabilities. This ongoing research includes evolution of our data management abilities, improvement of data delivery tools, expansion and refinement of methods for incorporating extant data sources, and development of forward-looking approaches to data quality assessment. While we use a wide array of standard data management tools, we also identify and implement new data engineering solutions for common problems. One of NORC’s core competencies is distilling large quantities of data into key insights. By employing a systematic approach to data management, NORC optimizes accessibility, enabling clients to make critical decisions using clean, easily digested, data. We continuously find ways to improve the data delivery process, including data transformations per documented methodological specifications, variable derivation, database dictionary, automated codebook generation, disclosure risk analysis as well as improvements in topline table generations and visualization of important relationships.

Through our ongoing research, NORC continues to improve capabilities and offerings across all phases of the data lifecycle. We develop interactive tools for assessing data quality for a variety of data types, employ proprietary methods for integrating data through record linkage and data fusion (including statistical matching), and develop improved methods for supplementing our probability samples with records from other sources. Enhancing datasets with additional features (variables, covariates, etc.) or the addition of more cases (increased sample size) helps improve our data products so that clients can make the most informed decisions using the data we provide. NORC focuses on our clients’ specific data engineering needs to ensure they get the most from their data.

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Data Engineering Research Experts

Sampling Strategies for the National Agricultural Statistics Service

Create a unified vision of sampling methodologies for NASS, the premier U.S. agricultural statistical agency.

Client:

National Agricultural Statistics Service

Helping to End Addiction Long-term (HEAL) Data Support

Collaboration, data sharing, and data infrastructure activities for an innovative opioid and pain research initiative

Client:

National Institutes of Health (NIH)