Our Commitment to Data Interoperability

Posted by Shivani StumpfO

September 11, 2018

As software tools gain an increasingly prominent place in classrooms and district offices alike, data interoperability has never been more important.


Stakeholders in nearly every industry have been attempting to solve the problem of data integration since the dawn of the digital age. As software applications have proliferated, however, it has become increasingly difficult for organizations to ensure that their systems are able to seamlessly exchange critical data.


In education — where school districts spend up to $250 per student per year on software — this problem is exacerbated by the use of instructional and administrative systems that are seldom designed with interoperability in mind. From student information systems (SIS) to assessment software to intervention programs, it’s not unusual for a single school to be running dozens of systems simultaneously. This infrastructural complexity makes comprehensive data integration an extraordinarily time- and resource-intensive process.


Inefficient data integration can have a range of adverse effects inside and outside the classroom, as it often precludes educators’ timely access to the data they need to make effective, well-informed decisions. This issue will only become more pressing as the popularity of edtech continues to snowball, which is why organizations from Ed-Fi and Project Unicorn to IMS Global and the Michael & Susan Dell Foundation are actively working toward a comprehensive solution to education’s data interoperability problem.


Difficult as it may be, school districts, state educational agencies, third-party organizations, and edtech providers have a joint responsibility to facilitate seamless data integration in order to ensure that teachers — and, by extension, their students — are able to take advantage of the latest, greatest software tools.


The Complexity of Comprehensive Data Integration


The best way to fully appreciate the value of interoperable systems is to understand the nuances of the problem they’re designed to solve.


Consider, for example, a dataset as essential as roster information. Every district maintains records of basic student information (and, often to a lesser extent, its staff), usually in its SIS. When a district introduces a specialized system to manage, say, assessments, IEPs, transportation, or responses to intervention, it must export the roster information that lives in its SIS to the new system (or vice versa). This wouldn’t be an issue if every piece of software was built using the same interfaces and protocols, but this is far from the case.


Not only does every edtech vendor have a unique data model and overlaying Application Programming Interfaces (APIs), but every district and state educational agency has its own set of data processes to which all of its operations must adhere. The way that an assessment software system formats and transmits student names, grade levels, and test scores may be completely unintelligible to a district’s existing SIS, meaning aligning these systems’ operations requires an immense amount of manual intervention.


In addition to data conformance, unintegrated software systems raise concerns about data quality and data security, as well. Edtech vendors  can impose extensive technical rules across all of their systems — for instance, that a “date of birth” column should be formatted as a date field, and that a subsequent “age” column should be calculated based on the data logged in the DOB field and stored as an integer — but if the right validation and operancy mechanisms aren’t in place, there’s no guarantee that the logical correlation between these two data fields will be accurate. In other words, this may result in a situation where the DOB is logged as 09/08/2010 but the age may be stored as 10, resulting in inaccurate data even though both fields adhered to the right data format.

Further, access control and permissioning is a key part of every district’s data architecture. A superintendent needs to be able to access a much broader subset  of data than a bus driver, and while many software systems feature some sort of permissioning settings, these settings are rarely exportable to other systems. This forces administrators to redefine their access controls every time they introduce a new system, which requires a monumental amount of time and expense.


Ultimately, overcoming these challenges benefits districts in three ways: it lowers costs, reduces demands on educators’ (especially administrators’) time, and drives efficiencies across the entire process of data analysis.


Laying the Groundwork for an Interoperable Data Ecosystem


At Hoonuit, facilitating data interoperability has always been among our foremost priorities. We built our platform to be system-agnostic, recognizing that every district utilizes varying systems and tools and have data analysis and reporting requirements that may be unique to them.


The Hoonuit platform is equipped with an assortment of prebuilt connectors and universal data loaders that enable it to extract data from any SISs, assessment platforms, HR and finance systems, transportation and food systems, third party sources like juvenile justice, foster care and National Student Clearinghouse platforms (and more) and export it into our longitudinal data warehouse. Our comprehensive PK-12 data model is a superset of the Common Education Data Standards (CEDS) which is also the foundation of Ed-Fi. Hoonuit continues to invest heavily in building standard interfaces to Ed-Fi to further support our promise of interoperability as well as the Project Unicorn pledge. This focus has allowed us to build a platform in which critical datasets from all of a district’s systems can be seamlessly exchanged and aligned.


Educational data is only valuable insofar as it produces meaningful insights, which is yet another reason why scattered, dis-integrated data is such a significant problem. When a district’s data is drawn together in a Hoonuit-powered interoperable ecosystem, however, educators are able to run descriptive and predictive analyses on their entire data library — and view the results as visualizations that are as intuitive and interactive. These analyses — and the ability to understand their practical implications — help educators make better decisions inside and outside the classroom and, as a result, drive better student outcomes.


As an edtech provider, our ultimate goal is simple: improve student outcomes by getting actionable, timely data-driven insights into every teacher’s hands. Expensive, time-consuming data integration projects represent a significant impediment to this mission, which is why we’ve placed data interoperability at the heart of everything we do.

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