As School Data Systems Specialist at the Girls’ Day School Trust, Marian leads 26 schools in their use of data systems and MIS. This role encompasses the development of best practice, organisation data standards, supporting the introduction of new systems, analytics, and the development and delivery of training to empower the schools in use of systems and data.
Marian’s passion for MIS systems and data comes from many years working in the Education sector, within Local Authorities, as well as schools. Her experience includes over a decade working for a Local Support Unit, supporting around 800 schools in their use of the SIMS MIS, with a particular focus on the use of systems and data to support school improvement and consultancy work advising SLT on exploiting MIS technology to its fullest potential.
1. What best practice processes would you recommend for structuring and storing data outside an MIS?
Having clear and consistent naming conventions and capturing meaningful data is essential. It’s about not making assumptions that everyone understands what you’re talking about. You need to have a good foundation for a common understanding of data, so that it’s valuable to everyone.
We’re doing a lot of work at the Girl’s Day School Trust (GDST) on our Data Cloud project. We’ve built a data warehouse in Microsoft Fabric, and we’re using Power BI to visualise it. We’re sourcing data from lots of different places, such as financial systems, HR systems, and MIS.
Part of this work is around creating a data dictionary so that you give users a clear explanation of what a particular field means. If you’ve got calculated data you need to explain the formula; the data dictionary will help you clearly define the calculations. It’s something I’ve noticed in SIMS Next Gen, where there are definitions provided for the calculations around attendance data.
That’s one of the most important pieces – because if you don’t get that right in terms of labelling and clear understanding – then you’re going to be in danger of making that data meaningless because it’s possible that the data and analysis is open to interpretation. People may draw conclusions that aren’t necessarily correct, and therefore, your decision-making can become flawed. So that’s essential.
We’re using cloud storage for our source data files, which is the most cost-effective way for us and also means that it’s scalable and accessible when we need it.
Clear use of metadata also ensures that we’re descriptive about our data, can categorise it clearly, and that it is searchable.
These are just some of the main things in terms of good practice around structuring and storing your data outside of your MIS which will give you a good foundation.
2. How do you maintain data quality?
This is a key part of the data governance piece. Within my remit in the GDST, I’m ensuring that we have good quality data because if we don’t, we’re basing decisions on data which could be incomplete. At the GDST, we have multiple schools, meaning it becomes much more critical to have data standards which allow us to clearly identify specific data items that we see as key to our decision-making and also ensure that we understand exactly what they mean and how they’re recorded across all our schools. This could include things like the frequency of data recording. I think this applies equally to a single school. It’s about having clear documentation and setting standards around specific data, which also would include the intent. So ask yourself, ‘How will we analyse it [the data]? Why is that data important?’
There are lots of tools that we’re using, within SIMS itself and using Power BI, to look at things like ensuring our data is complete, because you don’t want to base decisions on data where you think you’ve got your entire cohort included, but actually only 25% of them have data. So, looking where data is missing is key.
We’re also looking at the validity of data. That includes data fields that enforce a choice, a lookup in other words. So, it could be a choice of yes, no, or maybe, and at some point in time, we’ve decided not to record ‘maybes’ anymore. We need to understand whether the data we have is still valid, so we can identify potentially invalid data items. You get that a lot, particularly in schools, with some of the key data fields that the DfE collects, for example, ethnicity and languages. They will have defined a list of values that everyone should be recording against, and then the DfE may well change that list of values at some point. So that can mean that historic, as well as current data, is potentially invalid, and you might need to consider whether you’re actually going to use that in your data set or whether you need to update it.
The other side of the coin with data validity, is ensuring that data is sense-checked. We all use lots of systems now where we’re collecting data from different people directly – where parents, pupils, or staff are actually filling the data in rather than us seeing it first. It’s just going straight into our systems, and because of that, we need to be careful. Although the values they’re recording may be valid, it doesn’t mean that the data is necessarily correct. One simple example could be recording sex as male or female, and also recording someone’s title: Mr, Mrs, etc. Cross-checking those two fields against each other can highlight if somebody could have selected Mr for the title, when their sex is female. It can significantly affect your overall analysis if you don’t think to look at the quality of data you’re working with.
We also regularly perform data cleansing activities, looking for duplicates and clearing down historical data, both of which could introduce bias and make your analysis less meaningful. If we have data going back 20 years or so, it may no longer be something that we want to include within the data set that we’re analysing.
Data standards are about having a clear purpose for why we’re collecting the data, how we are processing it, what we’re planning to do with it, and what decisions that involves. I think it’s crucial that you have that well laid out, to ensure that you get good-quality data.
One of the big things that we are actively always working on is understanding the ownership of data systems or key areas of data. For example, you might have your Deputy Head (Pastoral) being responsible for all attendance, welfare data, etc. So, from that perspective, they should be leading the effort to share understanding with the people who may be working with that data on a day-to-day basis, to ensure that they understand why it’s important to have good-quality data and thereby enforcing data standards.
One key piece in all of this is ensuring that you have the skills, knowledge, and resources to support your data strategy. For example, if you’re considering working with an MIS, you need to ensure that you have adequately skilled people who know what they’re doing with the system. Person specifications in job descriptions should reflect the skills required to effectively carry out the role and these should be reviewed on a regular basis. Skills audits and other similar activities all feed into overall data quality. This is not about barriers to recruitment, but about ensuring staff are appropriately empowered and training is put in place where necessary.
We also need to ensure that when we examine our data strategy, including how we collect, process, and analyse data, we are sufficiently resourcing it and ensuring the intended outcomes are achievable. If we’re not, then again, that’s likely to impact the quality of data and the insights that we gain from it. We should be doing things like regularly reviewing job descriptions and skills audits, identifying where there are gaps so that we’re doing effective planning around data; not only for what we’ve got now but also what we’re thinking of implementing going forward. This means we’re always in a good position and can be confident that the foundations of our data insights are reliable. I think it’s a critical piece, and it’s something that people don’t always think about.
We are seeing such a big shift now in education generally to being much more data-focused, and making decisions based on it. Therefore, we need to have a data-literate workforce, that understands the analysis and dashboards presented to them.
One of the critical pieces we are working on within the GDST is around ensuring that when we make dashboards available to different groups we’re supporting those groups in using those insights and providing tailored training sessions. We can do all of this wonderful work and have this great data; it can be lovely, clean, and of good quality, and we can create amazing insights from it – but if the people who are making the decisions at the end of the day aren’t confident and don’t have the right skill sets to work with the data, that’s a real problem. There’s a whole piece of work in education about understanding who our audiences are and tailoring analysis appropriately. All of this feeds into getting good-quality data insights.
3. From your perspective, why is data so crucial for leaders in today’s education landscape?
I see the School Improvement Plan at the heart of the planning and strategic activity that school leaders will be doing. A key part of that work is around effective self-evaluation and understanding what’s going on in school now, which obviously then informs how they determine what actions they take. Having good data in place will allow people to plan effectively, and the decisions will be based on strong evidence. Not only are leaders then able to plan, but they should also be able to identify their key performance indicators to effectively monitor and track progress and review where they’re going. That may mean reviewing their planning, as they are evaluating their school improvement activities consistently.
This is a big piece now for schools: the ability to examine the whole picture of the school. It’s one of the things I used to talk a lot about with schools when I was a SIMS Consultant: avoid looking at any particular set of data in isolation. You can’t just look at attendance and determine that attendance is not great without looking at the other factors contributing to that. You can’t look at attainment and make the judgement that all these students aren’t doing very well in English, without considering the whole picture. You have to be looking at all of the factors that contribute to outcomes and obviously the MIS can play a big part in that.
The contextual data, the things that are influencing outcomes, are crucial, and that can really support school improvement. If you’re able to identify if there are particular groups of students that may need a different type of support or a different approach, this allows schools to influence outcomes and support improving teaching and learning, which is ultimately what we’re there for.
One of the key messages I have always talked about is that [data] doesn’t give you the answers, but it helps you identify the questions to ask to actually make a difference supporting school improvement.
Data is crucial in areas such as safeguarding to establish an early warning system to protect students and identify issues early on – whether through conduct, attendance, or a sudden change in attainment for a particular student. Data analysis can be used to identify where there could be a potential safeguarding issue.
Also, schools must be compliant and monitor activities against statutory and regulatory requirements, whether this is attendance or for immigration purposes, where we have international students in independent schools, in particular. Data really helps support the school’s operational business as much as it does the teaching and safeguarding.
4. What types of data would be most valuable for informing school leadership decisions?
A big part of this is the contextual and demographic data, because your answers will tend to come from understanding what might be influencing something. For schools, academic attainment data is a big part of student information and how they are progressing towards targets. Data analysis allows us to think about reviewing those targets and ensuring we’re always challenging students to the best of their abilities.
Conduct data, in particular, is important because, again, it can raise many flags about safeguarding. Negative behaviour could be ‘not doing homework’, but it could also be something like coming into school and being really tired, hungry, or emotional. These are potential red flags for safeguarding issues. Looking at the positive side of things, lots of schools have reward systems where students get points for positive behaviours. Exploring the impacts of the conduct policy and understanding how positive rewards support a reduction in negative behaviours is key, and that data supports agendas like safeguarding and well-being in particular.
There are key, or what I would call ‘vulnerable’ groups, for want of a better description, where the data is so important: these include special educational needs, deprivation indicators such as free school meals, or looked-after children. Grouping students by ethnicity and languages can help paint a picture of what’s going on in terms of individual student performance or for a group of students, for example. Again, particularly with international students, understanding things like nationality, ethnicity, and languages is important. We want to celebrate the culture of our students and ensure that everyone feels part of the community. Our schools may work on bringing together students who speak the same home language and share their culture. The GDST also challenges students to take qualifications that the school may not always offer as standard examination courses, because they already speak a different language fluently. There’s lots of key elements of contextual data that are important in understanding the school picture.
Also, schools should look at things like national baselines and understand what the bigger picture looks like. As we know, there are differences between different types of schools, but it’s useful to understand the national trends and what’s going on, as well as the work that’s being done more widely in the education sector.
Staff data is also critical for evaluating recruitment and retention, which is a key issue now in education. We must understand how we work towards improving that and the quality of working experience within our organisation’s teaching staff.
Also, grouping staff together in different ways can encourage them to learn and share good practice. This is an area where the GDST excels. We have good communities of teaching staff that come together across our schools and work has been done with these communities to explore how to get the best from data.
Financial data is obviously important for us as independent schools, where funding is a completely different model from the maintained sector. I understand obviously that it's going to be important to all schools, but because we’re much more of a ‘business’, being a group of independent schools, financial data is critical for us.
5. How do you ensure that school leaders and staff are equipped to interpret and act on the data insights?
Have clear data standards. Ensure that we have purposeful use behind our data systems. Given limited resources, we need to really question what we’re doing in terms of collecting and processing data and whether there is value to it.
Establish clear KPIs around expectations for monitoring and evaluation, whatever the area, whether financial, operational, attainment, etc.
Data literacy is crucial. Make sure that people understand how to use data effectively. For example, we develop dashboards centrally for schools to use where there’s a common need. Not only do we launch the dashboard, but we will also run inset sessions for the appropriate audience within schools to talk through the dashboard, what it’s delivering, or how they might want to use it. So, we’re trying to ensure that we really package that effectively.
I think it’s also critical to have a clear calendar for your data for two reasons: it should be clear at what points in time you intend to analyse, review, and target set so that it doesn’t become too onerous. This feeds into things like the frequency of collection, how often we need to update it, and how often we need to review the analysis based on whatever that data set is. For example, if you think of budgetary data, you’re going to be looking at specific points in time – financial quarters. A key part of the data strategy is having a clear calendar around your data activities. This also includes things like housekeeping and data cleansing activities that you might need to do on a fairly frequent basis to ensure that you’re in a good place.
6. What strategies have you found most effective for translating complex data into actionable steps?
We’ve been doing lots of work around inset for teaching staff with dashboards, but we’ve also been thinking about relationships between national, whole school, and classroom data and where they’re appropriate. Comparisons with different levels of data and different groupings of data should be done to ensure that the dashboards are going to be actionable and appropriate.
Understanding the audience and intent behind any data analysis is crucial because if you don’t have a clear picture around that, it’s going to be difficult. I use the word ‘KPIs’ a lot – it’s a key part of our data analysis because it helps us dig into what’s appropriate and to be able to track. We need to have clear KPIs, but we can’t have too many of them because, otherwise, it becomes something that you can’t action as you’re trying to look at too many different things. It depends on the intent behind the analysis – that’s key to all of this – having a clear purpose throughout.
At the school level, there should be a clear mapping back to the school improvement plan. We shouldn’t be doing any activities around data that does not feed in and support that – if there is something else that we’re doing, we need to be clear about why. We need to ask ourselves: ‘Is there value to it? Can we make better use of our resource elsewhere?’
Understanding the audience to which you’re presenting data is also critical, and I think it’s worth looking at your strategy from that perspective. When you present data analysis to different types of people, you need to understand what they need. For example, if you present data to governors or trustees, then the data is likely to be in a different shape than it would be, perhaps for a head of a department.
From one data set, potentially, it’s possible to present it in many different ways for different purposes, so you’re getting lots more value back from your data. But there must be clear communication around intent, particularly in terms of required actions when you’re sharing your data; it’s not good to launch a data dashboard, or provide somebody with a report with a lot of data analysis, if you’re not clear around why you’ve given it to them and what you expect them to do with it. Is it just for information? Are they supposed to be going off and saying, ‘OK, I need to look at this and take some action’? This piece is easily missed and can make a big difference in terms of effective use of data.
7. Can you share an example of where data has made a measurable difference for people outcomes or school performance?
Schools need to have a clear analysis of academic baselines and attainment over time. Looking at progress can make a significant difference in understanding where interventions for individual students are required. I used to talk a lot with schools [in a previous role] about this when looking at assessment data.
For example, back in the days when there were national curriculum levels, it was easier for everyone to understand what was meant by a particular grade. So, a child at the end of year six should be this [given] level and at the end of year 11 should be that [given] level. Recording academic data at various points between enables progress tracking and understanding if targets will be met.
However, that doesn’t mean we shouldn’t try to challenge those targets. What we don’t want to see is that higher-level [attaining] students are coasting. Therefore, analysing assessment data effectively allows you to see where those things might be happening over time. We often tend to focus on lower-ability students, supporting them and trying to get them up. It’s also important to keep in mind when looking at data, whether the higher-ability students are not performing as expected or not making the same level of progress. You can make good use of data to look at a whole group of students from across that spectrum of ability to ensure that you can have significant impact on pupil outcomes.
At the GDST, we look at what value-added measures we can use, such as the Centre for Evaluation and Monitoring (CEM) system for target setting. For our students, at particular points through their school career, we will gather baseline information from computer-based testing, but also for our A-level students, we’ll be looking at GCSE outcomes. This is used to generate targets for the appropriate endpoints, such as Key Stage 2, GCSE, or A Level. We’re always focused on having positive value added at key assessment points so that we’re not taking outcomes for granted and saying, ‘Oh well, they’ve hit target, that’s fine then, tick.’ We’re always trying to go beyond, so we do lots of work around constantly looking at that value-added measure as students go through school, so that we’re always looking to challenge and support where we need to. It’s not about just being focused on what might be lower- or higher-ability students, but a key part is the piece around contextual data and making sure that you are understand the entire picture of students so you can understand what is influencing their attainment.
In my experience from working in a Pupil Referral unit, one of the key lessons I learned was that education wasn’t second place, of course, but there were so many other things that were going on with students, like home influences, that were impacting on why they may not be performing well academically. So, it’s critical to be able to always look at that entire piece. That’s where you can make significant impacts on outcomes, by ensuring you’re looking at everything that could be influencing a child’s attainment.
8. What role does data play in supporting pupil safeguarding and well-being?
Dashboards and automated alerting are and will be so important in making a significant impact in terms of alerting and early warnings – being able to prevent a child from becoming a safeguarding concern – the ability to combine data across different systems from different sources and from different foci to really inform what is going on with children, not only identifying children that are classified as being vulnerable, but being able to look at trends. Gaining early warning around potential areas of concern and being able to put support in place and monitor and track that it has been effective, before it becomes a huge issue for a child, that’s what I think is significant.
Schools historically have done lots of work with data, with academic data in particular, maybe not using the greatest approaches, even perhaps paper-based. They’ve been collecting, processing, and analysing data for a long time, but I think we could see a big game-change around safeguarding and well-being data analysis.
9. What trends do you see shaping the future of data use in schools?
We’re seeing it already. It’s not necessarily completely embedded or totally widespread, but things like the use of data dashboards are going to grow exponentially, where real-time analytics really come into play and make a massive difference – having live dashboards informing you about Key Indicators around things like safeguarding and well-being, etc. For example, a student is not in a particular lesson when they have been in school all day – a vulnerable child that’s perhaps gone off somewhere, left the school.
All of that is going to have a significant impact, and part of that is the use of AI.
It will be significant because, you can ask questions that aren’t data-framed but are in plain language, for example, ‘How is Johnny doing today?’ It will be a game-changer for schools because they’ll be able to get to the information that they need to inform decisions and take actions quickly.
One of the barriers, particularly with SLT, is having to dig into an MIS or other system, which may not be something that SLT use on a daily basis, but is the only source of that information. Being able to have AI dashboards at their fingertips with alerting features coming in means that they’re pushing information out to those people who need to know about it in a timely fashion. That’s the big game-changer.
10. How do you think advancements in technology, such as AI and predictive analytics, will change how schools use data?
Push notifications and alerts can and will make such a significant difference. Obviously, things like automation and streamlining tasks will potentially save resources for teachers, and things like report writing can become easier than they currently are. That’s a significant workload (checking and proofing report writing). I’ve spent many hours going through teacher reports and having to correct things – that will make a significant difference.
What we do need to be careful of though, when using AI, and it comes back full circle to where we started, is that if we don’t have good-quality data, we’re likely to introduce bias. So we need to think about what needs to be built into how schools use AI going forward when it comes to analysing their data.
I think we’re going to see a much more significant uptick in schools adopting data analytics to support their decision-making. We’re still quite early on, and the main focus for AI is on teaching at the moment.
There’s work to do within trusts and schools. We’ll certainly be looking at how we exploit that technology in the GDST. However, if we’re going to benefit from the use of AI and predictive analytics, then we need to clearly understand our data landscape.
11. What are the biggest challenges schools face when collecting and using their data effectively?
Again, this goes back to the quality of data. It’s that bigger piece around data governance. So, it’s not just the quality of data in terms of whether it is valid or complete – it is that piece around whether everyone understands what we mean and whether we have standards around ways of working. When we’re working with our data, is it appropriately resourced? Are we using the right systems? That’s a critical question, and in education in particular, this is a key piece: the system strategy itself. It's something that we are embarking on across the GDST: looking at all of the systems we have, and how they interact and fit into our data strategy. We have to ask ourselves if they are actually appropriate or just cobbled together, which is often what happens. That’s key, as in schools we often have legacy systems. Are they really giving us what we need? Looking at interoperability across systems is key.
In terms of looking at pupils and understanding the whole picture, you need to be able to combine data from different sources, such as your MIS, HR system, payroll system or finance system. No data should stand in isolation of factors that can influence it.
Data standards should have clear frameworks as well around how we are assessing whatever it might be – whether it’s academic attainment or something else – there should be clear frameworks and measures in place for schools.
There is huge potential in terms of the technology that is becoming available and coming down the line. You can’t keep up with it. It changes so fast now, but I think schools do not need to get too excited about it. They should take a step back and be considered in their approach to using AI and predictive analytics to ensure that it will give them the outcomes that they’re expecting. It will make a significant positive difference to school improvement overall because it will make data and insights from data so much more accessible.
12. What advice do you have for leaders to make navigating data easier?
Clear definitions and a common understanding are key. Ensure that there is a shared purpose, intent, and understanding of how we’re going to benefit from data and what we’re going to use it for. Also, ensure that there is adequate learning and upskilling of people, where appropriate, to support data usage.
Data governance is so important. Having your data catalogue – for example, having clear standards around how data should be recorded, what the processes are, and understanding your landscape – will make data navigation easier.
Marian’s Linkedin profile: https://www.linkedin.com/in/marian-wheeler/
The GDST website: https://www.gdst.net/about-us/about-the-gdst/