How Hoonuit Works: Interoperability

Interoperability

An education ecosystem with data interoperability at its foundation is well-positioned to tackle both today’s challenges and the unknown challenges of tomorrow. Data interoperability seamlessly connects and integrates data sources and provides a complete view of all data. Without it, educators would be ill-equipped to offer the solutions our school district partners urgently require.

Educational data is only valuable insofar as it produces meaningful insights. Unfortunately, scattered, dis-integrated data is a significant problem for education agencies across the country. When a district’s data is drawn together in a Hoonuit-powered interoperable ecosystem, educators can run descriptive and predictive analyses across their entire data library. Now they are positioned to view the results as visualizations and make better decisions and drive better student outcomes.

Data interoperability also opens doors toward greater student equity. Without question, education is the cornerstone to ending systemic inequities and data interoperability is a key part of that work. Data tells a story. With the analysis of data, equity and achievement gaps can be identified. In order to make informed decisions and drive effective action plans, you need information.

Data interoperability enables both comprehensive longitudinal reporting critical to the examination of data to identify equity and achievement gaps, as well as predictive analytics which allows educators to react faster and apply intervention or other strategies for improvement. When educators are given the tools to analyze data on student performance and behavior, inequities come into sharper focus and can be addressed head on.

Connecting Data

Hoonuit eliminates data silos and connects disparate data to generate actionable, holistic student views that promote impactful decision making. Administrators, teachers, and staff rely on Hoonuit to quickly analyze data and trends from the highest level down to groups or individual students.

The Hoonuit data management solution imports data from a variety of sources, including Student Information Systems, financial systems, human resource systems, survey results, and assessments. Hoonuit can also scale to meet changing needs, such as the need to take to in Learning Management System data to measure student engagement during distance learning.

Data Acquisition and Transformation

Data warehouse migration requires thoughtful consideration and a sound strategy. Once Hoonuit brings in your data, we apply a transformations process to get it in a format that's consistent for dashboards and visualizations. This step ensures consistency in areas such as attendance codes, discipline codes, and even birthdates.

Does your system identify a student’s birthdate as October 1, 2010 or 10-1-2010? Do student absences include a half day of attendance? These details are critical, and every school handles them differently. If coded incorrectly or inconsistently, your dashboards and reporting will be wrong.

Our team of experts will go through an extensive 'Map and Gap’ process for each table and field from your source systems to support a seamless transition without any disruption to day-to-day operations. While performing a detailed profiling and auditing of source data, Hoonuit documents business rules and logical associations while conducting a thorough analysis of data to uncover any errors or issues prior to data migration. The mapping process allows us to understand the meaning of each field in the source system to ensure it has a proper home. In addition, we create transformation rules to ensure data will continue to be interpreted correctly after migration.

Data Quality

Data quality is the most common barrier to successful data migration. Without data accuracy, the visualizations and analysis that follow are rendered useless. At Hoonuit, data validation occurs at an early stage to ensure a smooth implementation. Moreover, our data quality processes our ongoing as new data is added to the data warehouse.

Hoonuit performs a machine learning algorithm that continually gets smarter and understands how to accurately match incomplete variances of information. We call this process ‘Fuzzy Matching.’ As an example, it is not uncommon for different source systems to use different identifiers for the same student. Imagine an SIS that lists Ron Johnson, an assessment sources with Ronald Jeffrey Johnson, and an LMS system showing Ronald J. Johnson. The Hoonuit system automatically identifies and connects the underlying data for this student so that you don’t have to.

Hoonuit’s advanced data quality monitoring system can be configured to your business rules and requirements. Take advantage of key tools such as:

The ability to choose from pre-built data quality measures or your own defined business rules
Error definitions, where to locate the errors in the source application, and exactly which records are causing the errors
Dashboards that can be viewed at the education agency, principal, or teacher level — showing their specific data quality issues
Dashboards showing every source data refresh to show progress towards fixing data quality issues

What We Do