Data based decision making

Data based decision making or data driven decision making refers to educator’s ongoing process of collecting and analyzing different types of data, including demographic, student achievement test, satisfaction, process data to guide decisions towards improvement of educational process. DDDM becomes more important in education since federal and state test-based accountability policies. No Child Left Behind Act opens broader opportunities and incentives in using data by educational organizations by requiring schools and districts to analyze additional components of data, as well as pressing them to increase student test scores. Information makes schools accountable for year by year improvement various student groups. DDDM helps to recognize the problem and who is affected by the problem.
Purpose
The purpose of DDDM is to help educators, schools, districts, and states to use information they have to actionable knowledge to improve student outcomes. DDDM requires high-quality data and possibly technical assistance; otherwise, data can misinform and lead to unreliable inferences. Data management techniques can improve teaching and learning in schools. Test scores are used by many principals to identify “bubble kids”, students whose results are just below proficiency level in reading and mathematics.
Types of data used in education
There are 4 major types of data used in education: demographics data, perceptions data, student learning data, and school processes data.
1. Demographics data in educational organizations answers the question, "Who are we?". Demographics show the current context of the school and shows the trends. Trends help to predict and plan for the future, along with seeing measures where leaders work towards continuous school improvement. Thorough demographic data explains the structure of school, system, and the leadership. In education demographic data to the next items: number of students in the school, number of students with special needs, number of English learners, age or grade of students in cohorts, socio-economical status of students, attendance rates, ethnicity/race/religious beliefs, graduation rates, dropout rates, experience information of teachers, information about parents of students.
For example, in a rural area educators tried to understand why a particular subset of students were struggling academically. Data analysts collected students performance data, medical records, behavioral data, attendance, and other data less qualitative information. After not finding direct correlation between collected data and student outcomes they decided to include transportation data into the research. As result, educators found that students who had longer way from houses to the school were struggling the most. According to the finding administrators modified transportation arrangements to make the way shorter for students as well as installing Internet access in buses so students could concentrate on doing homework. DDDM in this particular case helped to improve student results.

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