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. 

WATCH VIDEO

MiDataHub Newsletter

Subscribe to RSS Feed for MiDataHub Newsletter

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:

  • A cockpit application that provides for easy data management
  • A quick and reliable way to connect data systems
  • A platform for common tools
  • A data framework that emphasizes local control and stewardship of data
  • A  method to promote and sure data quality

     

    EdFi Logo

ROI Study/Survey

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.

MI Data Hub ROI Study

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.