In today’s data-rich schools, leaders and teams need structured approaches that transform raw information into meaningful change. The 5D Data Analysis Process stands out as a powerful framework specifically because it provides a structured way to analyze and interpret data that yields immediate action steps, empowering educators to make improvements. It puts actionable insights directly into the hands of practitioners at all levels, enabling swift responses to identified needs across classrooms, schools, and entire districts. This approach transforms data from a passive measurement tool into a catalyst for positive educational change at every level of the system.
What Is the 5D Data Analysis Process?
The 5D Data Analysis Process—DEFINE, DIG, DISTILL, DISCOVER, and DECIDE—is a series of data analysis and interpretation protocols we often use at Marzano Research in our technical assistance and support, coaching and consulting, and professional learning. These five sequential steps build on each other and guide educational leaders and teams through the complex journey from identifying needs to implementing solutions.
This structured approach ensures that decisions aren’t based on incomplete information or personal assumptions and biases. By systematically working through each “D,” groups can:
- Set an area of inquiry centered on improvement.
- Select and visualize data sets.
- Uncover hidden patterns, both positive and negative, affecting student performance.
- Identify root causes rather than symptoms.
- Create measurable goals for improvement.
- Target resources where they’ll have the greatest impact.
- Implement data-informed changes with confidence.
Let’s explore each phase of this process. Then, we’ll look at an example scenario in which a district team uses it to create a goal for improving literacy outcomes.
DEFINE: Setting the Foundation for Inquiry
The first step is defining a clear focus for your inquiry based on an identified need or problem. Collecting, managing, evaluating, and applying data critically means using data purposefully. To be purposeful, you must DEFINE the precise questions you want your data to answer. During this crucial first step:
- Formulate specific questions within your focus area that can be answered through data.
- Establish the parameters of what you want to learn.
- Clarify the purpose behind your data collection efforts.
(This initial definition often also generates additional questions that you can prioritize for future inquiry cycles, creating a continuous improvement loop.)
DIG: Gathering Evidence
Once you’ve defined your focus, it’s time to dig for relevant data and evidence. In this step, you’ll explore demographic, program, outcome, and perception data to gain a comprehensive understanding of the need or problem.
- Take inventory of all available data sources related to your defined need.
- Identify what information you already have access to.
- Determine if new tools or processes are needed to gather additional data.
DISTILL: Finding What Matters
The digging process typically yields more information than you can reasonably process. Distillation involves:
- Identifying the most relevant data for your specific question or need.
- Cleaning and preparing data for proper analysis.
- Displaying information in ways that highlight meaningful patterns.
- Prioritizing time with the most valuable data sources for decision-making.
DISCOVER: Uncovering Patterns
Discovery is where analysis meets interpretation.
- Analysis: Identifying and defining patterns or findings within the data.
- Interpretation: Applying experience and professional judgment to make sense of these patterns.
During the DISCOVER step, you move from observation (analysis) to interpretation (making meaning). Your goal should be to remain grounded in objective data. This helps teams avoid making assumptions and minimizes the influence of personal biases and preconceived notions on conclusions. Discovering should ultimately reveal strengths and challenges and their root causes. When teams go beyond surface symptoms to determine the fundamental drivers of undesirable outcomes, it enables organizations to develop solutions most likely to be effective.
DECIDE: Taking Action
The final step turns insights into action. Use a process like REL Northeast and Island’s Adopt, Adapt, Abandon to:
- Establish SMART (specific, measurable, achievable, relevant, timely) goals for improvement based on data-identified challenges.
- Develop plans to address root causes, drawing from evidence-based strategies and solutions.
- Determine methods for measuring progress.
- Implement systematic change and continuous improvement processes.
A well-crafted goal builds directly from your identified challenge.
Example
Define
Defined Question: How effective is our middle school reading curriculum at developing students’ ability to comprehend informational text?
Dig
In exploring this question, the team gathered:
- State benchmark assessment results showing middle school students performed well in overall reading.
- Classroom-based assessments revealing many 7th and 8th graders hadn’t mastered key informational text concepts.
- When disaggregated by demographics, data showed student-athletes performed worse on both assessment types.
- Attendance records indicated students in fall sports missed days when foundational informational text skills were taught.
- Program data revealed inconsistent approaches—some teachers retaught concepts while others strictly followed the district pacing guide.
Had this team examined only the benchmark assessment data, they might have missed critical insights about factors affecting certain student groups. By digging deeper into multiple data sources, they uncovered a full picture of the challenge.
Distill
From collected data, the team prioritized:
- Fall reading benchmark assessment results for 7th and 8th grade, focusing specifically on the informational text domain
- Attendance patterns correlated with reading performance
- Teacher implementation surveys regarding curriculum pacing and reteaching practices
- Classroom formative assessment data showing concept mastery gaps
- Student perception data about their confidence with informational texts
The most significant pattern emerged in the fall benchmark data showing that informational text comprehension lagged behind other reading domains.
Discover
Identified Challenge: According to fall benchmark data, 47% of 7th graders and 51% of 8th graders are reading at level 3 or above in the domain of informational text.
Decide
By the winter administration of the reading screener, we will increase the proportion of 7th and 8th-grade students by 5% in each grade (from 47% to 52% for 7th grade and from 51% to 56% for 8th grade) who score at level 3 or above in informational text.
Now that they have a specific, data-informed goal, the team can now brainstorm what evidence-based strategies and best practices will be the best course of action for success.
Putting the Process into Practice
The 5D Data Analysis Process has been effective across numerous educational contexts. For example, in Wyoming, we partnered with several districts to enhance educators’ data use skills, resulting in more targeted interventions and improved student outcomes. Read about their journeys here.
Whether you’re addressing achievement gaps, curriculum implementation, instructional effectiveness, or any other educational challenge, the 5D Data Analysis Process provides the structure needed to move from data to decision making and action with clarity and confidence. With a framework like this, educational leaders and staff can develop an often-overwhelming volume of available information into strategies that genuinely improve teaching and learning.
Need support implementing the 5D Data Analysis Process with your team? We offer coaching and professional learning to help you maximize the impact of your data-informed decision making. Learn more at https://marzanoresearch.com/services/professional-learning-and-coaching/.
