Principles and Good Practices

Table of contents

Some Common Problems in Data Systems

Problem 1: Lack of Understanding by Data Collectors

Those who collect data cannot visualise why it is being collected and how it will influence their work. It is seen as a monitoring device to assess their work. This leads to a lack of ownership and a lack of motivation to collect data.

Those collecting data need to be engaged in a visioning exercise, aligning with Principle 1. By involving stakeholders in understanding the purpose and outcomes of data collection, it transforms from a mere monitoring device to a valuable tool that influences their work. This shift in perception fosters a sense of ownership and purpose, ensuring that data is collected with a clear understanding of its impact.

Problem 2: Dependency on External Programs

Usually the stakeholders from outside the government education system have a predefined programme including kits, tests with identified learning outcomes to be achieved by the state. The state allows these teacher development programmes but they stop happening once the programmes are stopped.

Addressing the issue of predefined teacher development programs, Principle 6 comes into play. Establishing a data stewardship body at the state or department level can ensure continuous support for programs even after their formal conclusion. This body can define policies, standards, and strategies, preventing a cessation of initiatives once external programs end, and maintaining a cohesive and sustainable approach.

Problem 3: Ambiguity in Achievement Survey Data

Achievement Survey data sparks an urgency to attain learning outcomes for students. The goal to be attained is specified but how to get there is not clear.

To clarify the path towards achieving learning outcomes from Achievement Survey data, Principle 7 is crucial. Adopting a ‘single source of truth’ approach ensures that the specified goals are supported by accurate and reliable data. This principle emphasizes collecting data once and making it available across all necessary datasets, providing clarity on the ground situation and facilitating informed decision-making for achieving learning outcomes.

Problem 4: Underutilization of Performance Data

Performance data, when collected, is used for monitoring. This could be a resource for knowing the situation on ground and planning specific interventions at all echelons i.e., teachers, CRCs, BRC, DIETs.

The solution to underutilized performance data lies in Principle 2 (User-Centric Data Collection). Shifting the focus from mere monitoring to using performance data as a resource for planning interventions aligns with the idea that data collectors should also be users. This user-centric approach ensures that data is not only collected efficiently but is actively employed at various levels, empowering teachers, CRCs, BRCs, and DIETs to make informed decisions based on ground-level insights.

Problem 5: Underutilization of Data Beyond Monitoring

Data, particularly from achievement surveys and performance metrics, tends to be primarily used for monitoring purposes. There is a lack of consistent strategies for leveraging this data to inform proactive interventions. Opportunities for using data to drive targeted improvements at the teacher, CRC, BRC, and DIET levels are underutilized due to a focus on retrospective monitoring rather than forward-looking planning.

The challenge of primarily using data for monitoring purposes and missing opportunities for proactive interventions can be addressed by aligning with Principle 8 and Principle 4. Conducting a top-to-bottom evaluation of data verification practices ensures the accuracy and reliability of the collected data. Subsequently, by allocating dedicated human resources at various levels—teachers, CRCs, BRCs, and DIETs—specifically tasked with interpreting and utilizing the data for forward-looking planning, we bridge the gap between retrospective monitoring and proactive intervention. This involves creating frameworks for real-time decision-making, encouraging data sharing, and defining flexible strategies to drive targeted improvements. The emphasis is on transforming data from a retrospective tool into a proactive driver for positive change at every educational echelon.


Below are a set of principles that can be used to guide the development of data systems for education. These principles are not exhaustive and are meant to be used as a starting point for thinking about data systems. The applicability of these principles spans beyond just the education system and can be used to guide the development of data systems in other sectors as well.

Principle 1: Set a Vision and Goals Comprehensively

• Planning for multiple challenges simultaneously.
• Contextualizing data locally.
• Determining the top-down or participatory nature of decision-making.
• Conduct a visioning exercise with stakeholders to identify participation levels, relationships, processes, and desired outcomes.

Principle 2: Make Data Collection User-Friendly

• Overworked data collectors lacking a mandate for daily data use.
• Lack of ownership, treating data collection as a burdensome task.
• Ensure data collectors also serve as users, fostering a better understanding of data value and potentially improving performance.

Principle 3: Improve Data Compatibility and Integration

• Independent and siloed datasets.
• Limited usability due to datasets not interacting with each other.
• Align block and master codes for interoperability.
• Integrate datasets to reduce workload and enable joint analysis.

Principle 4: Allocate Human Resources Strategically

• Teachers facing challenges in timely data upload due to combined workload.
• Insufficient time at block and district levels to process data and meet deadlines.
• Assign dedicated human resources at school, block, and district levels.
• Ensure staff can process and derive insights from data, improving data quality and freeing up teachers for teaching.

Principle 5: Streamline Data Collection Online

• Data collected through both online and offline means.
• Perception of paper reliability leading to parallel systems, negating the time-saving aspect of online collection.
• Implement an official order from senior officials mandating the exclusive use of online applications.
• Ensure adequate infrastructure (phones, computers, networks) supports online data collection.
• Address cultural shifts by providing technological training and support.

Principle 6: Prioritize Responsible Data Management

• Departments and sections operate in silos, lacking unified strategies and standards.
• Ad-hoc addition of data tools leads to non-interoperable datasets.
• Establish a state or department-level data stewardship body.
• Define governance policies, data standards, and automation strategies.
• Strengthen data collection and verification processes.
• Analyze department needs and create frameworks supporting innovation.

Principle 7: Adopt a ‘Single Source of Truth’ Approach

• Same data collected from multiple sources leading to errors.
• Inconsistencies in information, akin to different spellings in official documents.
• Adopt the principle of a ‘single source of truth.’
•Collect each data point only once and make it available in all necessary datasets.
• Streamline data verification processes for accuracy.

Principle 8: Revise and Enhance Data Verification Practices

• Inappropriate data verification practices and pressure to show positive results.
• Gaps in the process or lack of human resources hinder accurate data representation.
• Conduct a comprehensive evaluation of verification practices.
• Develop a framework for appropriate data entry and checks.
• Foster a supportive culture to discourage misrepresentation.
• Involve external organizations for thorough checks.
• Allocate adequate human resources for data accuracy.

Principle 9: Consistently Build Skills at All Levels

• Capacity gaps in using software for data generation, verification, and utilization.
Utilize best practices and principles from other states and countries for capacity building.
• Conduct orientation programs and retraining using online tools and courses.
• Assess state-specific needs and gaps through a mapping exercise.

Principle 10: Boost Infrastructure for Better Data Entry

• Insufficient infrastructure for data entry, slowing down processes and diverting attention from teaching and administration.
• Include infrastructure indicators in the state or union database.
• Allocate resources to ensure adequate infrastructure, especially at the frontline.

Principle 11: Involve Stakeholders in Software Development

• Third-party creation of data portals and software without consulting end-users.
• Lack of consideration for user needs and capacities, impacting data quality.
• Engage stakeholders at all levels, from the frontline to state level, in the creation of data portals and software.
• Ensure that language and functionality align with user familiarity and requirements.