Data Quality Component 4: Data Coherence

Data Coherence wedge

Data coherence includes uniformity across shared resource data, as well as logical connections and completeness within a single data set and across data sets. For example, a district might upload to its system data on the number of students in military families, and those data are then transferred to the SEA data system. Shortly after the transfer of data, the district makes a change to the number for that data element, but the change is only maintained in the district system’s memory and not within the state data system. The district and the SEA may then report different data for the same data element. This lack of data system coherence has been known to result in public concern and mistrust of data being reported and additional time spent by SEA and district staff to align their data. Coherence also ensures internal consistency across time and outputs and programs, and enables logical distinction between concepts and target populations. Data coherence also involves having adequate structures for combining data for various uses that includes system compatibility across districts, SEA programs and business offices, and federal agencies. The following considerations outline how SEA staff may consider how new or revised internal data coherence processes play a role in the larger data-quality system.

  • Establishing data-coherence protocols: SEA staff will need to ensure there is a cache coherence protocol (i.e., policies that ensure program data systems within the SEA have the same values for shared data) for new data element(s) internally. Establishing compatibility and coherence across the systems will necessitate defining the systems, parameters, and terms used consistently across separate data systems maintained by the various programs within the SEA as well as data systems with the districts that feed data to the SEA. This includes compatibility of the computer technology and software used by the systems.
  • Ensuring SEA and district data coherence: In addition to ensuring coherence of internal systems, the SEA will also need to ensure there is data-system compatibility and coherence between the state data system and district data systems. Establishing compatibility and coherence across the systems will necessitate communication and training for data and program staff across the systems. SEA staff will need to develop key messaging around data needs and data quality to ensure districts, as well as external data partners, have the same understanding of system needs for data coherence to exist.

Data coherence, a key factor in data transparency, is a necessity for high-quality data systems and affects how stakeholders can make the best use of available data. Under ESSA, new data reporting requirements have presented a variety of data-coherence challenges for SEAs. For example, data elements at the state and local levels need to be uniformly defined and operationalized across shared data sources, in consistent formats, and reflect changes from one data system to another. Ensuring this uniformity when adding new data elements often involves investments in infrastructures and human capacity. If the data are to be useful to schools, districts, policymakers, and the general public, then a coherent data system must include the following features: highly user-friendly, easily accessible, and consistent over time.

SEAs can use the Data Systems Self-Reflection Checklist to consider how their current actions promote quality data systems. To download the Data Systems Self-Reflection Checklist, click here.

For more information on how Georgia is currently approaching data coherence, please see the vignette below.

Data Coherence Vignette: Georgia’s Tunnel, or a Comprehensive Data System for Optimal Data Use
Georgia Department of Education logo

The Technology Services team at the Georgia Department of Education (GaDOE) has implemented a statewide data system that is highly coherent and integrates functions to ensure it is user friendly and easily accessible. When Bob Swiggum began as the Chief Information Officer at GaDOE in 2009, he heard from district staff that the state education data system was not useful for decision-making and reporting information to local stakeholders and, therefore, rarely used. That perspective changed drastically with the implementation of GaDOE’s comprehensive state longitudinal data system (SLDS). The “SLDS tunnel”—as it is familiarly known—integrates key functions of all GaDOE departments and is accessible through every district’s existing student information system. Data coherence is maintained across the multitude of district data systems, including those that are managed by outside vendors. New federal and state data requirements and data needs that local and state staff discuss with GaDOE are incorporated into the system through managerial processes (e.g., SEA and district data management committees, online alerts from the SEA to districts through the data system, SEA assistance to individual districts, technology and data conferences) GaDOE has implemented. For more detail and diagrams of the GaDOE departments that are integrated into the SLDS tunnel, the type of data within the system, and the infrastructure used, go to the GaDOE SLDS page here.

A unique feature of GaDOE’s data system is its easy accessibility for diverse stakeholders. A local education staff member begins by logging into their school district’s data system. The district data system automatically loads in data from the state SLDS. No additional username or password is required. Not only do teachers, principals, and district staff have access to data within the tunnel, but so do students and parents. Because data security is a top priority, the system grants access to specific levels of data based on an individual’s role. In addition to real-time data, such as individualized education programs (IEPs), gifted or English-learner status, foster care, and course schedules, the SLDS tunnel provides professional development resources, instructional resources, and screening tools. For further information, contact the GaDOE at

Data Coherence Resources:

AdvancED. Consistent Improvement: Achieving Systems Coherence in a Data-Rich World. Alpharetta, GA: AdvancED, 2011.

Komuravelli, Rakesh, Sarita V. Adve, and Ching-Tsun Chou. 2014. “Revisiting the complexity of hardware cache coherence and some implications.” ACM Transactions on Architecture and Code Optimization 11, no. 4: 1–22.

Mathematica Policy Research. Developing a Coherent Plan for Effectively Using Data (InFocus Brief). Princeton, NJ: Mathematica Policy Research, 2013.

State Longitudinal Data Systems (SLDS) Grant Program:

Eklund, Julie, Anita Huang, Todd Ikenaga, Jan Kiehne, Hans L’Orange, and Jeff Sellers. SLDS Webinar: The Match Rate Dilemma. Washington, DC: U.S. Department of Education, Institute of Education Sciences, National Center for Education Statistics, 2015.