Building a Positive Data Culture in Your School

When setting out to build a positive culture of data there is a clear need for successfully managing perceptions of what this means for those new to the idea and avoiding any potential anxiety right from the outset.


Many teachers have not had their class learning data or perception data visualized and measured before which can lead to a lot of emotions that we need to be aware of.


Before a school can create an openness to improve based on data there are some common concerns that teachers have that lead them to deflect and disregard data.

Remain aware that:


  • Some teachers may feel as though their teacher efficacy is being measured

  • Some may worry that supervisors may used this data as part of evaluation

  • Deflection of data does not fit with current thinking


We need to continuously address the area of psychological safety around data with the faculty so that we are not having discussions of disregarding the data rather allowing the data to lead to productive inquisitive conversations and develop ways for us to do even better for our students.

Strategies for developing a culture of psychological safety


  • Defining a purpose with all stakeholders

  • Developing norms and protocols in the inclusion of data visualizations

  • Institutional knowledge of ladder of inference

  • Student centered conversations

  • Avoiding dangerous data

Schools need to continuously define what their purposes for looking at data are, why we are looking at data, and what are some possible outcomes. Once a clear purpose is identified and shared and possibly developed with key stakeholders, it can then be used to drive productive data meetings. In a highly effective school the primary reason for looking at data is to identify and explore how we might do ever better for our students. If there are secondary purposes related to changing school wide curriculum, student placement, and influence support services they will need to be clear and transparent.


  • Primary Purposes: Data is used formatively by teachers and students to inform next steps in learning. Data is not separate from instruction & learning but part of learning.

    • Example: MAP data results are shared with students during 1:1 mentor meetings to identify where they are in their learning and to set year long goals.

  • Secondary Purposes: Data has potential to inform programmatic decisions.

    • Example: Common assessment data can inform student needs and identify what lessons or units teachers need to spend more or less time on.


Common outcomes of looking at data:

To ensure that the purpose of looking at data is transparent and clear, you may decide to come up with some specific possible outcomes so that teachers know where they are going such as:

  • Influence 1:1 meetings and student mentoring

  • Influence future planning during curriculum review

  • Collect data on progress toward year long goals, and use the data with teachers to celebrate success and inform next steps

  • A list of questions are generated that will translate into actions or next steps to dig deeper


Using questions to drive purpose

We are often provided with data sets that we are not sure what we should do with. One of the best ways to define purpose and outcomes is to brainstorm questions that we want the data to answer. If we know what questions we want the data to answer it is easier for teachers to stay on topic and avoid derailing the conversation. Have a look at this list of questions for data team leaders.

Developing agreed upon protocols and norms help teachers frame the conversation around data and avoid the common issue of jumping to conclusions.


Common Data Norms:

  1. Take an inquiry stance

    1. By taking an inquiry stance we can wonder and ask questions that will lead to further inquiries, find out more information, and dig deeper.

  2. This is my best thinking at this time, my ideas can change and grow (Assume positive intent)

    1. We are looking at data to do even better for our students. We can suspend judgment on each other by taking on a growth mindset and allow us to question our assumptions and grow our thinking.

  3. Ground statements in evidence

    1. Rather than allowing false assumptions to lead to the wrong action, we can make sure we focus solely on what we notice in the data and go back to norm #1 and ask questions.


Common data dialogue protocols

Data dialogue protocols are a great way to engage a group of teachers and/or school leaders in a productive data driven conversation. These protocols allow all participants to slow down, make sure everyone understands the data, and that all voices are heard.


Common protocols generally include 3 steps:

  1. Predictions of what the data will tell us. Uncovers assumptions that can be pushed aside.

  2. Explorations of the data that strictly only focused on facts and what we notice. Avoid explaining why the data looks as though it does.

  3. Questions/Actions to explore and possible actions to take/next steps.

Two resources for teachers facilitating data meetings:

This video that further explains the idea of the Ladder Inference. Essentially our 5 senses are taking in data all the time and we generally select some of that “data” or information, provide our brains with assumptions, that lead conclusions, that lead to our beliefs, that will eventually lead to our human actions.


Building institutional knowledge and understanding around the ladder of inference enables us to rethink our norms when looking at data and ensuring we don’t go up the ladder too quickly. This allows us to ask more questions and approach the data with a more inquisitive mindset so we can go further in our investigations.

If we go too fast up the ladder we may draw false assumptions and conclusions which could lead us to take the wrong action. It is always better to stay lower on the ladder, take an inquiry stance, ask more questions, gather more data so that we can find out what is really going on.


The concept of the ladder allows us to shift our thinking when looking at data and not let data define us, rather allow it to provide us with opportunities to ask more questions and identify the true areas of improvement that help us lead a positive data culture.


Knowing about the process of how we take in data helps us shift our mindset and to show care for each other by:

  • staying low on the ladder,

  • take an inquiry stance,

  • ask new questions,

  • and discover more data.

When looking at data, teachers may think that we will be talking about their professional efficacy, but what we need to make sure we do is talk about how students are performing and how we can do even better for them. This shifts the focus away from measuring teacher's abilities and practices that don’t lead to a positive data culture.


One way to keep the focus on data is to talk about individual students. For example below, we might talk about each student one by one and use the data to inform our conversations. We may look at student 124, see a low wellness score but high in academics and discuss ways we can do even better for that student.

It is further vital to identify data that might be potentially “dangerous” and steering clear of it to avoid counterproductive discussions that can lead to negative perceptions towards the development of our shared data mindset.

As you can see in the image to the right, many teachers may jump up the ladder of inference and assume that teachers in subject B are all easy graders or that Mr. Caldwell is a hard grader. These could all be false assumptions, that fail engender a positive data culture. A general rule of thumb is that if one introduces a negative assumption others may follow suit reducing the level psychologically safety for all.


One issue is that leaders want more consistency around grading practices and may want to use this data to prove a point. The best data to help support consistency is going to be student work. We might be able to see what standards or strands students are not performing as well as that will lead teachers to choose students and calibrate how we see low and high performers.