Goal 2: Enhance Data Capabilities and Services to Empower Public Health Scientists and Healthcare Professionals


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Whether to provide more complete information to public health policy makers or to accelerate response during a health emergency, data are at the heart of CDC’s ability to generate insights that increase the health security of the nation. To best support and enable the public health professionals of the agency and its partners,  CDC must advance data capabilities across the data lifecycle of collection and surveillance, data sharing, collaboration, analytics, and response management. Furthering the agency’s operational data capabilities using advanced visualizations, analytic processes, and prediction will allow information to be communicated and actioned with greater speed and accuracy. The result is improved decision making and public health outcomes.

Objective 2.1: Put Data Into Action: Mature agency enterprise data analytics and data visualization capabilities.

Objective Description:

To put data into action, CDC will continue to enhance data readiness, expand access to analytics tools, and develop the data skills of our public health workforce. Advancing data readiness includes establishing an operating model to scale CDC’s EDAV (Enterprise Data Analytics and Visualization) cloud-based platform to meet the need for robust analytic services. Data readiness also means that data is “analytically ready.” This is accomplished by ensuring that data cataloging activities capture required metadata to make data from different sources discoverable and available to be integrated, packaged, and shared with the analytical and decision-making communities that exist across the public health ecosystem. CDC will continue to build upon the analytics and visualization tools already available on the EDAV platform by adding new tools that enable better, faster analysis. Of course, efforts to build more robust analytics and visualization capabilities assume a user population with certain skills. CDC is providing education and training to equip staff with the skills to incorporate analytics and visualizations into their decision-making processes. By extending the education capabilities available through the agency’s Data Academy, consumers of EDAV services will be able to fully leverage analytics and visualizations.

Objective 2.2: Data Interoperability: Ensure program and operational data is easily available across CDC and with CDC partners: state, tribal, local, and territorial.

Objective Description:

The unique nature of the CDC mission requires extensive information sharing, which is why interoperability is one of the most important outcomes of data modernization. Historically, dependence on legacy systems and a lack of data standards has meant that data exchange requires a significant amount of ad hoc manual intervention. Over time, the interdependencies established between different systems and data sources and the spread of one-off “point to point” exchange mechanisms slow down data exchange further. To eliminate legacy silos and achieve interoperability, CDC and its partners must focus on four key areas.

  • First, CDC must accelerate the adoption of standards that capture data exchange formats, structures, and protocols. Many standards are in use today so much of this work involves engaging with members of the public health community to align, harmonize, extend, or adapt practices rather than creating a unique CDC standard.
  • Second, the agency should continue to use classification systems to make data exchange more fluid. Classification ensures that data are labelled in a way that communicates context around issues important to CDC communities.
  • Third, with the goal of making more data available in real-time, CDC must design data and systems architectures to support frictionless data exchange. This means establishing machine-to-machine connectivity with security policies incorporated from the onset that provide data when it’s needed to those with the appropriate authorization.
  • Finally, a governance program ensures adherence to standards that maintain the health and efficiency of the public health data ecosystem.

Objective 2.3: Building the data community and culture.

Objective Description:

As modernization makes public health data more integrated and automated, the way that different data practitioners interact becomes even more critical. Public health data practitioners who are key collaborators with the agency must have a common operating model that ensures data is available and fit for use. CDC and its partners must continue to build an operating model that specifies key roles and responsibilities for what and where critical data positions operate within the agency, how resources get assigned and issues escalated to enable consistent data quality management and remediation. A common operating model will provide practitioners with a clear understanding of what functions they must perform plus the training and tooling required to perform those tasks. As part of a common operating model, the agency must develop key processes for making decisions about what data are to be treated as enterprise data, how changes will be managed as the public health data ecosystem continues to evolve, and who is responsible for performing activities such as data quality management across the enterprise.

Outcome and Mission Impact:

Transforming public health data and systems to be more interoperable while expanding access to powerful analytic tools allows CDC and its partners to anticipate and respond more quickly to the known and unknown threats that emerge in the future. Increasing the capacity of various information technologies to exchange and use data without special effort on behalf of the user reduces the barriers to data access and makes data sharing easier and more seamless. Besides improving the public health community’s ability to collaborate, enhancing data interoperability enables speed to action by eliminating technical and operational barriers to sharing, consuming, and analyzing data. Increasing the availability of analytic-ready data (i.e. discoverable, understandable, accessible, and able to be integrated) will be a key measure of progress. As data sharing is automated and systemically enabled at scale, maintaining data quality and integrity across the information delivery chain is critical. CDC will have a dedicated team that ensures data is trusted and consistent across the entire community. The agency will be able to act with greater speed and agility, while global health capacity and domestic preparedness are strengthened by increasing the agency’s capability to collaborate and form new partnerships with state, tribal, local, and global public health authorities.

The Office of the Chief Information Officer is part of CDC’s Office of the Chief Operating Officer.

Page last reviewed: February 24, 2022