Quality Assurance, Driven by Data: 10 Steps To Open A School Series

It is important to evaluate a school’s performance on an ongoing basis, but we need to ensure it is done authentically, using data-driven decision-making. With strategic and authentic support, organisations transform and improve when coached, mentored, and led, with a focus on key performance indicators. Ideally, decisions should be data-driven. Although some areas of school performance are easily monitored and measured, e.g., Financial Performance, others are tougher to review, identify, and improve. There are also traps and misleading data that can confuse you.

2026 Perspective: Why Quality Assurance is a Strategic Priority

In today’s rapidly changing education environment, quality assurance is no longer a compliance exercise – it is a strategic priority. School leaders, parents, regulators, and investors all increasingly rely on data, evidence, and continuous improvement systems to assess institutional performance.

Effective quality assurance systems help schools anticipate change, align operational execution with vision, and demonstrate measurable impact. This makes data-driven quality assurance integral not just to academic outcomes, but to reputation, sustainability, and long-term success.

Michael Fullan argues for meaningful systems of data collection in his book, Putting Faces on the Data. We need to “build the bridge from data collection to improved instruction.” Students are people – not data. Assessment data can bury you or provide focused guidance on how to reach every student. The same applies to data that informs the business side of running schools. It is often more than what the data first presents to understand the full picture.

This article aims to unpack this often complicated topic.

What is Data and Data-Driven Decision Making?

Of course, data will include very real numbers. Financial reports are the obvious. But there are other forms of data that are less concrete but equally significant.

School Opinion data represented by parents, students and the community, including how they might describe the school to others, is invaluable. The concrete numbers are easy to find, but the soft data is also important. How people behave, how they feel, and what is common practice. Perceptions, mood, school culture, values and beliefs in practice. What are the behaviours, and what proof exists of the demonstrative actions by all team members? These things are important.

Leadership plays a critical role in effective quality assurance. It is leadership that sets expectations, ensures accountability, and fosters a culture where evidence-based decision-making becomes embedded in everyday practice rather than treated as an administrative requirement.

What data is collected? What data is acted upon? What data is shared? What data is analysed?

Financial Data

We have discussed financial data in previous articles, and it is probably well covered. In particular, a school’s financial position provides a clear picture of its current status. There are some traps, and data can be skewed in areas like EBITDA, but in most cases, the financial picture is pretty clear. Valuation becomes subjective when we realise the data is only telling part of the story and does not reflect potential. What is most often not clear is what to do next. It is the other areas that will significantly impact the bottom line when we don’t understand the full picture and all that this encompasses.

When is Data Not Real data?

At first glance, data often tells a story, but without understanding the full picture that underpins that story, you may not be so sure of the data’s validity. Take student achievement data. In 2019, a school may have excellent overall data, as evidenced by high literacy averages. In 2020, that same average may drop significantly. Pressure mounts on the school administration and teachers about why results deteriorated so quickly. What if that same school had high enrolment turnover? What if the data were not measuring the same students, year by year? This example is a common one in international schools with relatively high student turnover rates. We need to be clear about what we are measuring and what the measurement represents. We need to look out for data that may be skewed. Data-driven decision-making needs to be accurate.

What do you do with the Data?

Ideally, all data should serve a purpose. It should inform you about the past, but, more importantly, it should inform future behaviours. There is no point in collecting data unless it will lead to a change in practice. A big stick reporting approach does not change behaviour. A culture of self- and peer-reflection needs to be part of a cycle of review and change. We believe strongly in creating a community of peer learning and support. A model of organisational culture that values reflection, openness and judges data as helpful information to learn and grow from.

Why Quality Assurance Matters

Quality assurance is more than monitoring outcomes – it provides a structured framework for alignment, improvement, and accountability.

Schools that embed data-driven quality systems:

✔ Support leadership decision-making
✔ Strengthen staff performance systems
✔ Improve teaching and learning outcomes
✔ Build stakeholder confidence
✔ Demonstrate accountability to parents and regulators

This makes quality assurance foundational to both educational excellence and institutional sustainability.

Data-Driven Decision-Making in Teaching and Learning

In today’s international education context, data-driven quality assurance is supported by digital dashboards, analytics platforms, and integrated information systems – not just manual spreadsheets.

Schools are inundated with information that, in this context, we might call data. It becomes very difficult for teachers and leaders to know which data are useful or how to use them effectively. Teachers also get quite cynical if management collects data just for the sake of it. It can create extra layers of work without any real benefit. Schools need to know how to select data that can be useful for improving student learning, and also how to present and make the data accessible to teachers for analysis. Where possible, schools should use already existing processes. The information is most often already freely available; it just requires a system to capture it.

Demographic	Processes	Perceptions	Student learning
– Purpose, mission, and vision – Leadership policies – Curriculum – Staff assignments – Professional learning, planning, collaboration – Program offerings and access to them – Instructional strategies and materials	– Purpose, mission,n and vision – Leadership policies – Curriculum – Staff assignments – Professional learning, planning, collaboration – Program offerings and access to them – Instructional strategies and materials	– District expectations and evaluation procedures – Student attitudes – Teacher attitudes – Parent, community attitudes – School/district climate – Perceptions, preconceived notions/expectations – Learning style preferences	– Classroom summative assessment results – Formative strategies – Teacher evaluations – Benchmark (interim) test results – Standardized test results – Common district assessment results – Common school-wide assessment results

Digging Deeper

Usually, data processes focus on generalities. “Averages” and “trends” are important, but these stories often don’t reveal the underlying details. An “average” blends microdata together. What if we want to identify the effect of professional development strategies on improving critical thinking? We would need to investigate and capture data about student performance in areas that reflect “critical thinking.” Overall grades may generally reflect this, but not specifically. What if we want to analyse the number of students who moved from a ‘C’ to a ‘B’ in Mathematics over a set time period? Surely, when we believe that all children are important, this data represents the distance travelled for large groups of students. A’s are important and somewhat prestigious, but some may argue that many bright children may do well, almost in spite of the quality of teaching or programs. Our first layer of data may provoke us to look even further. It may tell us what we really need to look for.

Systems Approach to Gathering Data

Technology is obviously a very helpful tool to achieve this; however, the goal should be “for data to be touched just once.” What we mean is that if information comes into your workplace in some form, then find a way to capture it just once and make it easily accessible. Let’s look at an “old school” example. A teacher teaches students daily, including observing their learning progress. eg Questioning, observation of work completed. (Data point 1). The teacher tests students, and their grade is marked on the paper (Data point 2). The teacher transfers that grade into a paper mark book (Data point 3). The teacher transfers that data into a central database (Data point 4). The teacher creates a report card (Data point 5). The teacher conducts a parent-teacher interview (Data point 6).

The teacher knows the students’ ability at data point 1. Of course, we may need to validate the data, capture it in different ways and then present it in different formats; however, the above example shows how cumbersome the process can be. There are some shortcuts. (A longer discussion for another time)

Another thing to consider is shaping processes so you can monitor data systematically. In another example, let’s consider a request by management to monitor the degree to which all subject departments are focused on literacy. We want to capture this information monthly, as our annual strategic plan states: “The school will ensure all subject departments focus on and improve literacy within their subject contexts.” A simple way to do this is to create a standardised minutes template for all subject meetings that includes a section on literacy. If the minutes of every subject meeting are recorded consistently, management has quick access to the reported discussion of literacy in each subject. Of course, beyond that, we can review lesson/unit plans, conduct teacher observations, test the students, speak to students, etc. However, we should, wherever possible, systemise data capture to make the process smoother.

In schools with a strong focus on data-driven decision-making, they also foster a data-friendly culture in which teachers and administrators seek reliable data to inform decisions about curriculum and instruction. Fostering such an atmosphere is a gradual process, but much more effective than top-down approaches.

Accreditaton

Most international accreditation systems depend on data. In truth, the review teams are trying to quickly and succinctly review practice to ensure it is aligned with standards. Furthermore, they are looking for a demonstrated commitment to changing and improving practice, including data-gathering processes. Our advice to schools that will one day seek accreditation is to implement systems from the beginning. When you design systems and procedures to embrace these principles, your job is half done. When meeting minutes include systematic recording of conversations, a quick review shows an external team what you are focused on. When data is captured and reviewed for internal purposes, it is easily shared with others. Data Driven decision making is an important part of accreditation processes.

Data Driven Decision Making

The real point here, however, is to ensure we make well-informed, effective decisions. The challenge for the data exploration team is to turn this momentum into action. The next steps in the process are to set concrete goals, develop strategies for achieving those goals, determine how to evaluate progress, and roll out the plan. Too often, we make assumptions about small pieces of information, and we don’t have the complete picture. Ideally, data should drive great decisions and improved performance.

Frequently Asked Questions About Quality Assurance in Schools

What is quality assurance in education?
Quality assurance refers to the systems and processes that evaluate and improve teaching, learning, leadership performance, and organisational outcomes.

How is data used in quality assurance?
Data helps schools measure performance, identify gaps, inform strategic planning, and track progress over time.

Who is responsible for quality assurance?
Leadership teams, governing boards, and academic leaders share responsibility for embedding and sustaining quality systems.

Why is quality assurance linked to sustainability?
Schools with strong quality assurance systems build stakeholder trust, demonstrate accountability, and are better positioned for stable growth and investment.