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.

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Who Benefits?

TEACHERS

Teachers know the routine: One app to track behavior and attendance. Another to plan lessons and manage student assignments.

SCHOOL DISTRICTS

School districts sit atop millions of data points. Now, district administrators, academic officers and superintendents can make fully-informed decisions

STUDENTS & PARENTS

Students, parents, school administrators and educators are empowered with a complete picture of student progress at their fingertips in real time.

STATE AGENCIES

CEPI, MDE and other state agencies can collaborate with districts on data solutions, while maintaining district control of data.

VENDORS

MiDataHub allows the biggest ed-tech providers down to the most cutting-edge startups to have a scalable entry point into the market.

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News & Events

MiDataHub YouTube Channel In the last few months the MiDataHub team members have been working proactively to create a YouTube channel providing end users throughout the state a quick, one stop resource to review videos on utilizing different functionalities that MiDataHub provides. Videos are organized by topic and cover areas in general Data Hub knowledge (Overview), Data Hub Cockpit tutorials, and areas within navigating reports and creating custom exports, both of which provide a high level of validating the quality of data passed from the Student Information System (SIS). These videos are updated every other week. We are continually exploring how to expand and provide the most benefit to users of MiDataHub and would appreciate any ideas you might have on videos to create in the future by following this link (Ideas) and submitting those ideas to us directly. Youtube Channel

Now Live - EduPaths Courses The first of many courses to be published in EduPaths for the Data Hubs is now available for your viewing pleasure. This first course, MiDataHub 101, is designed to provide both new and current users of MiDataHub, the skills needed to get started with basic and advanced features including how the process works to e-sign agreements, managing user accounts, validating data against pre-built reports, creating integrations for educational systems such as NWEA, M-STEP, etc, and how to create dashboard builds. www.edupaths.org

Weekly Newsletter

January 17, 2022

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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 network of 5 data hub hosting locations that securely host district data
A cockpit application that provides for easy data management
A quick and reliable way to connect data systems
A platform for common tools and dashboards
A data framework that emphasizes local control and stewardship of data
A method to promote and sure data quality

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.

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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.

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