Turning Data Schools Have Into Information Schools Use
Our vision is to streamline the use of educational information statewide, through common data and common solutions allowing districts
to turn data you have into information you can use.
Teachers know the routine: One app to track behavior and attendance. Another to plan lessons and manage student assignments.
School districts sit atop millions of data points. Now, district administrators, academic officers and superintendents can make fully-informed decisions
Students, parents, school administrators and educators are empowered with a complete picture of student progress at their fingertips in real time.
CEPI, MDE and other state agencies can collaborate with districts on data solutions, while maintaining district control of data.
MiDataHub allows the biggest ed-tech providers down to the most cutting-edge startups to have a scalable entry point into the market.
News & Events
Fiscal Agent: Kalamazoo RESA
The Michigan Data Hub is a collaborative, statewide effort to address challenges in managing and using school data. The work of this initiative has centered around creating an ecosystem where information is exchanged between the large number of disconnected data systems used by schools in the state based on pre-defined standards. The initiative has leveraged the data standards developed by the Ed-Fi Alliance. The results of this work include:
The Michigan Data Hub has completed a Return on Investment (ROI) and Potential Cost Savings Study. Our belief is that the data hubs, when fully implemented, will save districts a significant amount of time and money in managing data. The study estimates that MiDataHub can save districts up to $56 million per year by eliminating duplicate integrations, integrating systems that aren’t currently connected, and by streamlining and partially automating the state reporting process. This information serves as a goal for MiDataHub to attain and exceed.
What are the Benefits?
- Schools will spend far less time and money establishing and maintaining their own data bridging services. What used to be many programming chores of many hours each will become an implementation task to connect local systems to programming written by others.
- Data quality can improve significantly. Every connector will include error checking with feedback to the data originator. Quality control measures can be focused on one source for each piece of data with the results of that propagated to other systems in a controlled, error-checked manner.