What is data interoperability and why is it important in education?

The use and usefulness of data depends upon how easily it can be shared across devices and digital systems. While discrete data packets can be easily circulated within a digital system, different devices, platforms and systems must be able to connect with each other in order for that data to be shared. This ability for different systems to ‘talk’ with each other is known as interoperability. As education becomes more data-driven and data-intensive, interoperability is an increasingly important aspect of how schools are able to function.

Interoperability issues are a surprisingly common impediment to the use of digital data in education. To give a simple example, interoperability issues might lie behind the regular frustration of a teacher having to manually enter students’ attendance data into their online progress reports because the school’s student information system expects to see dates in an Australian DD/MM/YYYY format while the reporting system does not. For an individual teacher this is clearly an inconvenience – across a whole school system this renders the data unusable. In both instances, digital data is clearly not ‘working’ as it should. 

Defining data interoperability

Interoperability refers not just to data sharing but “the ability of different information systems, devices or applications to connect, in a coordinated manner, within and across organizational boundaries to access, exchange and cooperatively use data amongst stakeholders” (HIMSS, 2019). Some technology users might be aware of data formats and protocols such as XML and SQL that offer ‘syntactic interoperability’ (i.e. the ability to exchange information). More difficult to establish is the capacity to automatically interpret and process data between systems (i.e. ‘semantic interoperability’). This requires systems to be using a common, unambiguously defined ‘information exchange reference model’.

While data interoperability involves considerable technical challenges, it is primarily a challenge of establishing consensus and compromise between a range of different data actors.  Establishing a commonly agreed-upon set of standards and protocols for data systems to adopt involves establishing a consensus amongst many different developers, vendors, regulators and end-users. If a ‘common standard’ is agreed upon, then it can be released for public adoption and use. This is referred to as an ‘open standard’ which can be adhered to by existing and future products as a guarantee of inter-operability.

In practice, achieving the establishment of common and open standards can be a fraught process. For example, in some domains standards are set in a de facto manner by the dominance of one particular platform, system or product (for example, the dominance of Microsoft Excel in terms of spreadsheet use). In this instance, other products will often find themselves forced to ensure interoperability with the dominant product. Given the commercial tensions and competing interests in many areas of data and software use, the push for establishing interoperability in particular markets and sectors often falls to industry-wide groups and community interests who act as catalysts for awareness-raising, campaigning and eventual brokering of agreed standards.

Mechanisms for establishing data interoperability in education

Although a little delayed in comparison to sectors such as health and e-government, the drive to establish data interoperability in education is now well underway. Similar to well-established Standard Interoperability Frameworks (SIFs) that exist in the US and the UK, the Australian National Schools Interoperability Program(NSIP) works to promote an ‘industry supported standard used to link together data systems within the school sector’. The development of a ‘standard’ for interoperability is promoted as a basis for enabling school systems to “interact and share data efficiently, securely and cost effectively, regardless of the application and technology platform’. These national programs are complemented by organisations such as Project Unicorn– a not for profit organisation focused on promoting data interoperability in the US education system. The existence of not-for-profit agencies and frameworks such as NSIP, SIF and Project Unicorn reflects the fact that interoperability is a complex process to attempt to establish in education, requiring cooperation between multiple stakeholders.

Opportunities of establishing data interoperability in education

There are many benefits to increasing interoperability in education – not least the increased efficiency of education systems and the increased (re)use of data between and within schools. If school data systems can ‘speak to’ each other without a human intermediary then time and money are saved. This can enable greater fluency of data across the various domains of a student’s schooling experience – from enrolment and behavioural information to learning and reporting. Interoperability has the potential to streamline the sharing of information in ways that impact upon all aspects of student success and wellbeing.

Interoperability also allows schools and teachers to reclaim and reuse information related to technology use. While large amounts of data are now generated and collected through schools, much of it remains underutilised in central databases. More effective interoperability can therefore allow teachers and schools to access this data and put it to new uses. As education systems have become increasingly centralised, schools have lost power and autonomy to make decisions that best suit the specific local needs of their students and school communities. In this way, providing greater access to data generated within a school might help schools to make more informed decisions and govern their school communities with confidence, thereby becoming less reliant on the pre-processed data reporting and feedback from centralised bureaucracies.

While there are clear technical and infrastructural efficiencies implicit in establishing common forms of data, the need for data interoperability in education is most often expressed in terms of the optimisation of learning. In the US, for example, the system-wide (re)use of data is seen as a ‘critical tool’ to support student success – particularly as a means of enabling learning to be personalised  (see the Data Quality Campaign). Project Unicorn claims that interoperability is ‘critical to giving teachers the information they need to do their jobs and to enabling innovation in classroom practices’. Indeed, the ability to share data efficiently is seen as integral to planning, monitoring, supporting and reporting on student learning and therefore an issue that has a bearing on effects teachers, students, parents and school districts.

Challenges to establishing data interoperability in education

Notwithstanding these potential benefits, as the general lack of data standards across school systems suggests, interoperability is not easy to establish. Indeed, establishing a standardized framework is costly, technically challenging and requires the hiring of specialist employees and contractors if it is to be established across a whole school district. As Marisa Kaplan notes, these are costs that are ultimately borne by schools and school districts rather than the developers and suppliers of the data systems. The notion of interoperability can also be resisted as an imposition on the market – with software developers feeling constrained in how they can develop and extend their products without infringing the open standards. There might also be commercial reluctance to share product details and potentially compromise intellectual property advantages.

At the same time, increasing data interoperability introduces new problems associated with broader data sharing and reuse. First, with more systems and devices sharing information, there are greater risks associated with securing data and ensuring that it remains private. Given that much of the information being circulated within school systems is potentially identifiable and sensitive (especially in relation to children), the stakes are high. This also raises questions with regards to the rights of children and young people. For example, if students do not wish their data to be shared beyond particular boundaries, then what rights do they have to redress this given they are legally dependent on adults (i.e. parents and teachers) to make decisions for them? Alternately, to what extent are individuals able to give informed consent for the reuse of their data if new forms of reuse are being continually made possible through improved interoperability?

Second, while getting rid of human mediators between systems might save time and money, how do we ensure teachers and schools remain in control of choosing and overseeing processes that become automated in education systems? Anxieties around the slow creep toward automation can only be dispelled if teachers and principals are consulted about the steps involved in interoperability. Just because systems canconnect, and data can beshared, does not mean that they should be. Indeed, the fact that many commercial EdTech companies now take a leading role in managing school data systems is a good reason to be cautious. Administrators need to ask themselves who is benefiting from this devolved governance and at what cost?

Finally, there needs to be careful consideration over the understandings that arise from data that is shared across systems and devices. Much has been written about the importance of data being used in context (see Loukissas, 2019: Boyd & Crawford, 2012), with the overriding argument being all data need to be read in their local settings. If data are shared without consideration of the social, cultural and political conditions under which they were originally generated and given meaning, then this increases the chance of misinterpretation and/or misuse. In the current climate of high stakes testing and school accountability, the erroneous analysis of data shared through a ‘no holds barred’ approach interoperability could have real world consequences.





Body, D., & Crawford, K. (2012). Critical questions for big data. Information, Communication & Society, 15(5), 662-679.

Loukissas, Y. (2019). All Data Are Local: Thinking Critically in a Data-Driven Society. Cambridge, MA: The MIT Press.