If you’re a chief information officer or data director at a school district, chances are the past year and a half has been a whirlwind. You’ve been accustomed to working in the background, quietly providing data to school leaders, but now, your work is front and center. Staff across the organization are using data you generate and maintain—attendance, proficiency, and engagement data points—to make major decisions with long-term, and often very public, impact.
Having your work be front and center can be incredibly exciting, but it’s also challenging. Quantitative information about vulnerable populations can be sensitive, and some stakeholders are not only suspicious of data but aren’t trained in how to assess it and use it effectively.
The fact is, pushback against data happens all of the time in education, from all angles—school boards and administrators, parents, lobbyists and politicians, and student groups. And resistance to data can arise for any number of reasons: teachers believe that data dehumanizes the students they work so hard to support, the data exposes something stakeholders aren’t ready to hear, or they believe that their district’s circumstances are unique, so comparative data isn’t relevant.
School data experts are now working in an utterly transformed landscape—one in which reliable numbers are needed to make decisions about online learning and to address newly exposed equity issues at the district, state, and national levels. The stakes are high. If you’re a district CIO or data director, chances are you need strategies to make data compelling and accessible to audiences that can historically resist it.
SCRUB AND HUMANIZE THE DATA
Likely one of your main tasks these days is to take demographic and course enrollment data (e.g., gender, race, and who is taking which courses) and marry it to your LMS and internal quantitative and qualitative data sets (e.g., test scores, survey results, formative assessment, and SEL social-emotional learning data), so you’re generating precise reports on data sets that can be as granular as “survey results of Hispanic males in AP Chemistry who log on to Google Classroom more than twice a day.” I call this “demographic overlay”—it’s very different from conventional data analysis, where you may have looked at assessment results in isolation from demographics, qualitative SEL/survey input, and engagement metrics.
A dynamic “demographic overlay” can prompt compelling next steps for your school, but it can also increase the amount and complexity of information for stakeholders to take in, so make sure that the data you share is scrubbed, tidy, and formatted in a way that ensures that they don’t struggle to make sense of overly granular details or straightforward points such as what the X-axis represents. They need to focus on analysis and decision-making. Use simple visuals, like bar graphs, to transform information into single data point graphics or sentences, and know that building data literacy skills among stakeholders is part of your job. (Assume nothing! Many stakeholders are newcomers when it comes to data.)
Also, in education, stakeholders can become resistant to data if it doesn’t reflect the humans behind it. Your job is to humanize summaries of data, and the best way to do that is with data storytelling that is fastened to stories in the school community that are already a priority (e.g., the experience of Asian students and families in the district) and surfaces stories that maybe no one has looked at yet (e.g., suspension rates in a particular population).
Finally, take every opportunity to tell the story in different ways and multiple ways. (In marketing lingo, these opportunities would be called “touchpoints” along a “customer journey.) For example, in addition to sending PDFs of reports once a year, craft quarterly emails that tell the same story in a slightly different way, perhaps with teacher perspective interwoven.
When data tells a compelling story, it incites emotion, and in education, emotions can range from glee to pride to resentment, differ across groups, and even change among individuals depending upon the timing and the data presented.
Just as data is valid, so is the emotion it generates. Plan for emotional space for stakeholders who are analyzing and digesting the data. For example, I recently presented data that drilled into accelerated class placement for a district to district administrators, and the differences among scoring profiles of different racial and ethnic groups moved them to the point of despair. I was taken aback and had to think quickly; I asked them to turn off their cameras so they could take a moment, privately, and think about sharing how the data made them feel before turning the cameras back on. I learned a powerful lesson that day: While I want stakeholders to look at data critically, I also need to anticipate their emotional reactions and honor them.