This article will be presented in 5 sections, with each new section sent out weekly. The focus is on 5 critical factors to have “right” when preparing to make data informed decisions: getting the “right people” the “right data” in the “right format” within the “right time” using the “right process” in order to make well informed decisions for divisions, departments, schools, staff, classrooms, students and families.
Introduction:
In our last segment, the importance of the right data was highlighted to ensure the right information was available for teams to engage in data driven decision making. Click here to review this previously published piece. In this segment, the focus will extend to the importance of format when sharing data.
Right Format
Now that the team has clearly defined which data and data systems are necessary to ensure they have access to a complete and accurate picture, the team will need to consider if the data is available in a meaningful way. How data is presented to team members can shape what a team notices or how they respond. Imagine you are at a grade level meeting reviewing universal screening data and someone shares a spreadsheet with a number next to each student’s name. Is this format helpful for teams to know what value that score holds as it relates to the student’s proficiency, or would team members need to spend additional time searching for benchmark scores to interpret its meaning? Additionally, does this format allow teams to easily compare student performance or conceptualize the total number of students at, above, or below expected proficiency rates? Would the team need to further manipulate the data in order to make this determination? Similarly, have you been in meetings where discipline data is shared for the current month and you found yourself wondering how this compares to discipline data last month or this time last year? Have there been instances where data is shared and you found yourself wondering what you were looking at, or what it meant? These scenarios all tap into the critical need for data to be presented in the right format.
The right format for sharing data requires several key factors. First, data needs to be presented in a user-friendly way. Once teams determine which data is necessary to answer the questions they have, their next step is to determine how they want to look at it. Formal data systems can support this by providing visual representations of specific data sets. Most systems can convert raw data into color coded charts, graphs, tables, etc. that provide great detail and context with little time and effort from the user, provided adequate training and access to the system has occurred. Even when formal data systems are not available, data can be converted through charts, pivot tables, graphs, etc. by team members with the skill set and software to create these documents. Visualization can have a huge impact and can make an incredible difference in the amount of effort and time teams need to really dig into problem solving. Furthermore, it is important to highlight, when formatting data in a “user friendly “ way, consistency is important for progress monitoring. Additionally, it is important to analyze relevant disaggregated data next to aggregate data points which can help to explore inequitable outcomes as a part of problem solving and progress monitoring.
Knowing who will be responsible for collecting and sharing the data is also an important part of ensuring the right format. As indicated in the first segment of this article, team composition is critical to the data driven decision making process. In this regard, the role of data analyst should be clearly defined and included as part of the team’s composition. This role is essential to ensuring the right data is shared in an accessible and meaningful way that aligns with the team’s identified question or goal. Examples of the responsibilities of the data analyst(s) could include:
- Describes potential new problems with precision (What, Who, Where, When, Why)
- Provides data (e.g. Custom Reports, Risk Ratios, SWIS Big 5) concerning the frequency/rate of precisely-defined new problems
- Provides updates on previously-defined problems (i.e., precise problem statement, goal & timeline, frequency/rate for most recently-completed calendar month, direction of change in rate since last report, relationship to change goal)
- Distributes user friendly reports to team members
- Supports capacity building for data literacy skills
- Leads discussion of potential new problems
- Responds to team members’ questions concerning content of the Data Analyst’s Report; produces additional data on request (e.g., additional Custom Reports)
While the data analyst holds a critical role in sharing the data in a meaningful way, data literacy is an additional factor that can promote or hinder data driven decision making. Schildkamp et al. (2017) suggest that user characteristics of teachers such as knowledge and skills, as well as perceptions about the importance of data, can impact data use for decision making. Similarly, Mandinach (2012) indicates that for “the benefits of the technology solutions to be actualized, educators at both the state and local levels must know how to use data to inform practice”. For educators, this means the ability to transfer the information into evidence based practices and instructional strategies that meet the needs of the identified students. Building the capacity to interpret and apply data, often referred to as data fluency, is therefore essential to the team’s ability to function effectively. For this reason, it is incumbent on school leaders, data analysts, and coaches to create a data culture that emphasizes the value, practice, and use of data to improve decision making. Providing data in a clear and concise format is one way to promote and support a strong data culture.
Tip: Consider these questions when planning to format data for decision making:
- Where is the data stored (which system)
- Who has access?
- How often is this collected/updated?
- Who inputs the data?
- What does this data tell you about your students/school?
- How can this data be generated in a user friendly way that supports the questions the team wants to address?
References
Ellen B. Mandinach (2012): A Perfect Time for Data Use: Using Data-Driven Decision Making to Inform Practice, Educational Psychologist, 47:2, 71-85
Kim Schildkamp, Cindy Poortman, Hans Luyten & Johanna Ebbeler (2017) Factors promoting and hindering data-based decision making in schools, School Effectiveness and School Improvement, 28:2, 242-258, DOI: 10.1080/09243453.2016.1256901
