How to Solve Your School District’s Data Quality Problems

Data quality is mission critical to the success of any major organization. School districts are no different. They generate a lot of data. For instance, they generate academic, financial, staffing, and other data from various functional areas, including transportation, food services, custodial services, and all the other functions of a school district. This data can drive funding levels, funding eligibility, resource allocation decisions, program decisions, and other important aspects of managing a school system. School districts must also comply with federal and state regulations, which requires the reporting of accurate data. In many instances, superintendents must individually sign off on the accuracy of data.

Encountering Data Quality Problems in School Districts

Given the complexity of school district data environments, school leaders sometimes encounter data quality problems. For instance, school district leaders may encounter discrepancies in the data available to them. Further, a particular program’s data may indicate that the program’s participation in the current year is drastically different than the previous year. Or, a school district leader may hear, anecdotally, that school principals expect the participation to be much higher or lower than the data indicates. These kinds of occurrences merit investigation. When school district leaders set out to investigate them, they need to identify who can provide them with answers. Since most of the data generated in a school district comes from the district’s information systems, school district leaders may be inclined to first turn to information technology.

Should the District Blame Information Technology for Potential Data Quality Problems?

For most district users, information systems are a black box. As a result, it is easier for them to attribute data discrepancies to potential system issues or errors. However, more frequently the district systems’ code follows the business logic or rules dictated by the individuals responsible for the relevant area. For instance, the program administrator for a particular district program typically dictates what attributes a student should have for inclusion in the program participation count, or what conditions trigger a student’s removal from the program, among other business logic or requirements specific to that program. In other words, the information systems follow the logic of the program administrator.

In most cases, information technology departments have no control over how the data is created or updated. Nor do they control the logic or business rules used to generate the data. Further, they do not have authority over the people who enter the data. The information technology department is responsible for ensuring that the systems adhere to the logic that the department receives, and they are responsible for storing the data according to industry best practices.

While the information technology department shares certain responsibilities for delivering data to school leaders, other departments are often also responsible for various aspects that contribute to the generation of a particular piece of data. So if responsibility is shared, how should district leadership go about solving a data quality issue?

Navigating Data Roles to Solve Data Problems

Since many individuals and departments play some kind of role in the data life cycle, we need to understand the complicated network of roles surrounding a particular datum, from creation all the way through to destruction. School district leaders must understand who plays what role with respect to the datum in every stage of its life cycle. This understanding empowers school district leaders to solve data quality issues, because they would know who to turn to for identifying the source of the problem. But how can school district leaders gain this understanding?

The RACI Matrix: the Data Quality Map

The RACI matrix offers one solution. RACI is an acronym for various roles involved in a process. It stands for Responsible, Accountable, Consulted, and Informed.

  • Responsible – the person or department who does the work to complete the task.
  • Accountable – the person or department ultimately answerable for the correct and thorough completion of the deliverable or task.
  • Consulted – the person or department whose opinions are sought.
  • Informed – the person or department who is kept up-to-date on the progress of the task or deliverable.

The RACI matrix provides a structured methodology through which school district leaders can identify and assign roles that each individual or department should play in a data life cycle. This facilitates school leaders gaining a full perspective on the network of data roles, and helps isolate the source of data quality issues.

Applying the RACI Matrix to Data Quality

When using the RACI matrix for this purpose, we create a matrix that represents each piece of data. Across the columns, we place the different tasks or processes associated with that data. In the example matrix below, we have populated the columns with some common or potential tasks or processes that a school district might encounter for a generic piece of data, such as the number of students in a particular program. The actual tasks used when creating a matrix would be specific to the school district, the data, the organizational structure, the information system, and all the other factors that influence the data lifecycle. This example is just meant to demonstrate how the RACI matrix can help you gain an understanding of the data ownership within your school district.

Down the rows, as we continue completing the matrix, we place the different individuals or departments involved in the lifecycle of that data. At the intersection of each row and column, we assign the respective RACI role, indicated by the first letter of each role type, R-A-C-I. According to the RACI matrix method, the accountability role can only be assigned to one party; whereas, the other roles can be assigned to multiple parties.

Sample Matrix – Number of Students in Program Data

Sample of Raci Matrix Use for Data Quality

As you can see in the matrix, when looking at the full life cycle of a piece of data, no one person or department has full accountability for data quality; each activity or phase of the data life cycle has its own accountability., indicated by the bold letter As in the matrix. This reflects the reality at most school districts. Once you have constructed a RACI matrix for the data for which you have encountered a data quality issue, you can navigate this accountability structure to identify the source of the problem.

If you have more questions about how the RACI matrix applies to you or if you are interested in learning more about how Gibson can help you with your data quality issues, please feel free to contact me, Ali Taylan, Gibson’s Chief Information Officer.


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