Core Implementation Components

Data analysis, or data analytics, involves identifying and combining the data collected and processed in the EMIS so that stakeholders can examine it and draw conclusions about education system performance. There are four possible purposes for data analysis: descriptive, causal, predictive, and prescriptive.

Within the EMIS ecosystem, data analysis is defined as:

The process of identifying, combining, and analysing data stored in the EMIS and linked systems to enable monitoring of the education system according to performance indicators.

Explore these essential data analysis resources:

1. Education Management Information Systems (EMIS) A Guide for Young Managers – This guide provides a clear outline of data management in an EMIS and is useful for understanding data analysis.
2. An overview of key data sets in education in South Africa – This paper provides an overview of data sets and management for education in South Africa but is a useful resource for anyone interested in data management including analysis.
3. Data Analytics and Decision-Making in Education: Towards the Educational Data Scientist as a Key Actor in Schools and Higher Education Institutions – The authors of this book chapter illustrate how traditional data analyses are becoming gradually substituted by more sophisticated forms of analytics. They provide a classification for recent movements (such as learning analytics, academic analytics and educational data mining). This resource is useful for anyone interested in understanding growing trends in data analytics in education.

Steps to effective data analysis in EMIS

• Articulate performance indicators for the education system: Clear performance indicators should be established for all levels and aspects of the education system that require monitoring.
• Check that performance indicators are suitable for system levels: Indicators should be detailed at the school/institutional level and summarised at the national level.
• Ensure that data analysis processes serve the full range of reporting purposes: Decision-makers and planners may need data presented in different ways for a range of purposes, such as statistics to identify trends and indicators, research results, and/or specialized reports that support projections and simulation models.
• Ensure that software applications allow for data manipulation by users. In digital systems, users and stakeholders should be able to use the software to generate different types of reports from data sets (e.g. graphs, and statistical tables) to support their various analytical purposes.
• Use software to conduct analysis and generate specific types of reports: Users play an active role in data analysis by using software apps to generate specific reports and to perform analysis of data.