Data-Driven Decision Making in Education

Daphne Heflin
Daphne Heflin
Elementary School Principal; Ed.S. in Educational Leadership and Administration
Group of people at a table analyzing data on spreadsheets and reports.

Data are all around us yet the term itself, “data,” often creates fear or anxiety. Really, it’s simple; data are pieces of information. The number of eggs in the refrigerator, the expiration date of the milk, the cost of the monthly electric bill, the attendance rate of employees are all examples of data. Big data in education can be used to make effective decisions that lead to different forms of success.

Success is a must for school districts because schools within them educate children, and children are our future. Data, when used to make effective, informed decisions, will help move schools from their current reality, whether good or bad, to a place of improved achievement. Many types of data within school districts can be used for the improvement of schools.

What Types of Data are Available

Types of useful, available data in school districts can be categorized as follows:

  1. academic achievement data
  2. non-academic data
  3. program and systems data
  4. perception data

Academic Achievement Data

Academic achievement data include information related to the achievement of and progress toward students’ academic goals. Examples of academic achievement data include:

  • benchmark assessments
  • diagnostic assessments
  • formative and summative assessments
  • common grade-level assessments
  • students’ class averages
  • progress monitoring data
  • student work samples
  • portfolios
  • performance tasks

Non-academic Data

Non-academic data are information or factors that impact students’ academic achievement in some way, but are not direct measures of the learning outcomes. Examples include:

  • student attendance
  • teacher attendance
  • office discipline referrals
  • special needs
  • socio-economic status
  • mobility patterns

Program and Systems Data

Program and systems data are information related to the structure of the work itself, which impacts achievement and success. Examples include:

  • learning standards
  • instructional expectations
  • curricular resources
  • observation data
  • schedules
  • new teacher mentoring information
  • meeting agendas and minutes
  • professional development plans
  • behavior management plans
  • student support systems information

Perception Data

Perception data are information related to culture which impacts success. Perspective and perception affect people’s behavior and decisions. Examples of perception data include:

The lists of data in this article are not exhaustive but are plentiful, reinforcing the fact there are data all around us. With all of this data available, what is next? How do schools go from having data to having success?

How Schools can Use Data

The following steps can be followed to effectively use data:

  1. question
  2. determine
  3. gather and analyze
  4. create and implement
  5. review and revise


Using data effectively begins with asking questions. What information is needed to make informed decisions related to the school goals? Questions that immediately come to mind, when the goal is to improve school achievement and overall success, are:

  • What are the year-to-year and month-to-month trends in student performance at each grade level, for each teacher’s class, and each subject area?
  • Which students have learning gaps? In what areas are those gaps? Which skills are needed to meet the needs?
  • What are teachers’ instructional strengths and areas for growth?
  • What do teachers, parents, and students believe about what is and is not working in the school?
  • Which systems and which programs have been fully implemented, partially implemented and not implemented at all?
  • What resources are being used and are they aligned with students’ learning standards?


To answer questions initially asked, determine which type of data is needed. Answering questions about student performance trends and learning gaps, for example, requires the use of student achievement data. Answering questions about teacher performance requires the use of student achievement data, non-academic data, and program/systems data. Perception data is needed to answer questions about the beliefs of different people groups. Questions about resources, alignment, and implementation require program and systems data.

Gather and Analyze

District data systems, state assessment reports, assessment programs, grading systems, documented conversations, artifacts, and anecdotal records are all examples of places or means of gathering data. After the data is gathered and formatted for legibility of the group analyzing the data, analysis meetings occur.

During analysis, teams look for and discuss answers to their questions. The answers are then used to determine action steps to close gaps, meet needs, and change the direction of negative trends.

Create and Implement

Creating well-defined action steps removes unknowns and assumptions; so establishing who, what, where, when, and how for each action step is critical for the fidelity of implementation. Next steps may be implemented school-wide or may be implemented with specific groups of people or individuals or in specific subject areas, curricular areas, programs, or systems.

Review and Revise

Finally, reviewing results of the action steps and measuring their impact provide more data to be used for making adjustments or changes. The cycle then continues, as new questions arise from the review.

Everyone wants success. From paying bills and restocking cabinets in one’s home to leading a school and a district to the highest levels of success, the effective use of data is necessary. For schools and districts, using data analytics in education effectively creates success for children and thus success for our future.

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