CNA January Series: From Data to Root Cause
Moving Beyond Surface-Level Problem Statements in CNAs
The purpose of this post is to focus on two parts of the CNA data analysis phase that truly determine whether the process becomes meaningful or remains superficial: working with data and conducting a root cause analysis. These two components are the backbone of the CNA.
Making Sense of Data Before the Meeting Even Begins
In education, data is not scarce. Schools have access to academic performance data across all subject areas, collected multiple times a year and housed in different platforms and formats. Attendance records are updated daily, discipline data is tracked continuously, and school climate surveys, teacher-related data, and community perception data are routinely gathered as part of ongoing improvement efforts. Teacher data alone may include certifications, years of experience, turnover, attendance, instructional assignments, professional learning participation, and walkthrough or observation trends.
Because of this abundance, data is often analyzed in isolation. Academic indicators are examined by instructional teams to inform teaching and learning decisions. Attendance data is reviewed by smaller groups focused on patterns of chronic absenteeism. Discipline teams analyze behavior trends, often separately from academic conversations. Teacher-related data may be reviewed in human resources, instructional support, or leadership meetings, disconnected from student outcome discussions. Each of these practices serves an important purpose, and in many cases, they work well within their own domains.
The CNA process, however, creates an opportunity to step back and look at this data from a more systematic perspective. To me, that is the greatest value of the process. Instead of a series of parallel analyses, the CNA allows schools and programs to develop a shared understanding of how different conditions interact to influence student outcomes. By intentionally examining academic, attendance, behavior, teacher, and perception data side by side, the CNA creates space for deeper questions about how instructional practices, staffing patterns, and learning conditions intersect. This integrative view, more than any single dataset, helps move the conversation beyond isolated problem statements and toward a clearer understanding of priority needs.
Below are several ways to intentionally pair datasets to see this bigger picture:
Break down data by grade level and student subgroups.
This is often the starting point in CNAs, and for good reason. Any outcome can be disaggregated by grade level and student subgroup. Looking at these patterns side by side helps determine whether challenges are isolated to specific grades, persistent across levels, or concentrated within particular student populations. Comparing subgroup patterns across multiple indicators also helps distinguish between isolated issues and systemic ones.
Examine grade-level performance alongside teacher-level data.
Grade-level trends can be reviewed in relation to teacher-related data such as years of experience, certification patterns, turnover, instructional assignments, or participation in professional learning. The purpose is not to evaluate individual teachers, but to identify structural or staffing conditions that may be influencing outcomes at a grade or program level.
Examine trends over time rather than single-year snapshots.
Looking across multiple years helps distinguish between persistent challenges and short-term fluctuations. When patterns remain consistent over time, they are more likely tied to underlying conditions rather than isolated events. Trend analysis also helps identify whether changes align with shifts in staffing, program implementation, or instructional models.
Pair outcome data with implementation or process data.
Outcomes should be reviewed alongside information about how strategies are implemented. This may include fidelity checks, walkthrough data, professional learning participation, or monitoring logs. When expected outcomes are not observed, this pairing helps determine whether the issue lies in the strategy itself or in how it is being carried out.
At this point, it can feel like the analytical work is finished. The data has been reviewed, and areas of concern have been identified. But this is where a strong CNA goes one step further, and where the most important work begins.
Identifying Outcomes Is Not Enough
Statements such as “reading proficiency is low” or “chronic absenteeism is high” accurately describe outcomes, but they do not explain the conditions that produced those outcomes. They answer the question of what is happening, but they stop short of addressing why it is happening. If the CNA remains at this level of surface-level problem statements, they leave schools and programs with one broad issue and countless possible responses. For example, if a CNA identifies a high percentage of students with reading deficiencies, the list of potential interventions quickly becomes overwhelming. New curricula, additional programs, tutoring, software, professional development, staffing changes, all of these may seem relevant, yet none are clearly prioritized. Because federal funds are limited and intended to address specific barriers, schools cannot afford to operate at that level of generality. Strategic decision-making requires understanding which conditions are most responsible for the outcomes being observed. That understanding only comes from a root cause analysis.
Root Cause Analysis Means Slowing Down
Root cause analysis is the process of identifying the underlying conditions that contribute to observed patterns in the data. This part of the CNA is less about producing additional charts and more about reflection. Teams bring together quantitative data, qualitative information, and contextual knowledge of school or program operations to examine what may be driving the identified area of concern.
Returning to the example of low reading proficiency, root cause analysis requires looking beyond the outcome itself. Are reading gaps concentrated in specific grade levels? Do they align with attendance patterns or student mobility? Are instructional materials aligned to standards and student needs? What do classroom walkthroughs reveal about instructional practices? How consistently are evidence-based reading strategies implemented across classrooms?
As these questions are explored, the root cause often becomes more specific. The analysis may reveal that reading deficiencies are most pronounced in early grades, align with inconsistent implementation of foundational reading practices, and coincide with high staff turnover or limited instructional support in those grades. At that point, the CNA moves from a general statement about reading deficiencies to a clearer understanding of gaps in early literacy instruction driven by implementation inconsistencies rather than the absence of programs or materials.
What It Really Means to Move From Data to Root Cause
Moving beyond surface-level analysis requires three deliberate shifts.
First, analysis must move from isolated data points to patterns. Root causes are rarely revealed by a single metric. Strong CNAs examine trends across multiple years, draw from multiple data sources, and look for consistency across student groups.
Second, analysis must shift from student outcomes to adult-controlled conditions. Federal programs are designed to address barriers that schools and systems can influence. Effective root cause analysis therefore focuses on instructional practices, time and scheduling, access to materials and supports, monitoring and feedback systems, and family communication structures. Context matters, but context alone is not a root cause. A root cause must point to something the system can change.
Finally, conclusions must move from opinion to evidence-backed explanation. A root cause is not a belief or a consensus statement. It is a conclusion supported by multiple forms of evidence. Strong CNAs intentionally triangulate outcome data, process data, perception data, and resource data. When these sources converge, analysis moves from assumption to explanation.
A Closing Reflection
The purpose of a CNA is not to prove that schools face challenges because that reality is already well understood. The real work of a CNA is to help schools answer a more difficult question: what, within our control, is most responsible for the outcomes we are seeing, and how do we know?
When CNAs engage that question honestly, planning becomes purposeful, the process becomes clearer, and the CNA moves beyond paperwork into meaningful action.
