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Data Integration (Data Hubs)

Animated Vision of the Michigan Data Hub

 

Please click anywhere in the graphic below for a quick journey animating the vision of the Michigan Data Hub. Please note, some of the features referenced in this video are still under development.  

 

 

DATA INTEGRATION

 

 

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

 

A major takeaway from a recent ROI Study on the Michigan Data Hub is that Michigan districts can save a significant amount of money by utilizing the Data Hub.  The chart below, excerpted from the study, shows that Michigan districts currently spend about $61M on data quality and data management, $64M on data connections and $38M on state and federal compliance reporting for a total of $163M annually.  When fully implemented, the study estimates that the Michigan Data Hub can save districts $56M annually, reducing total costs to $107M.  That leaves a lot of extra money that can be put towards educating students.

 

THE MICHIGAN DATA HUB:  A STRATEGIC ALIGNMENT AND ROI STUDY

 CLICK HERE TO DOWNLOAD THE ROI STUDY PDF

 

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.

                                                    

Vision

To streamline the use of educational information statewide 

Mission

To develop and implement a Standards-Based Enterprise Data Architecture that facilitates the exchange of information among the stakeholders in Michigan who work to improve student achievement. 

About Data Integration

A longstanding issue in Michigan schools has been the inconsistent ways in which student data is managed and handled. Each school district or PSA makes its own set of choices of which systems to use for student information, food services, transportation, data warehousing, Special Education data, library systems and more. All of those systems use student data, and schools are left to solve on their own or in small groups the problem of how to share data among those systems. The result is a massive duplication of effort as the same problem is solved over and over in the many school systems around the state. And often those solutions are less than ideal, with some combination of:

  • Home-grown processes to share data among applications, costing a lot in staff time and often being very incomplete due to limitations on that time

  • The purchase of expensive data bridging services

  • Redundant data entry, which is the result when not enough resources can be devoted to establishing and maintaining effective data bridging

The Data Integration Project seeks to solve this inefficiency by establishing regional "Data Hubs" to handle the bridging of student data among applications. This approach will centralize data bridging processes so that a connector written for a product will work for all users of that product. Having the Datahub at the center of the process means that data will be standardized on its way into the new system. This makes a connector out to another system work no matter which system originated the data.

To see this concept in action, consider the example of a food service system that works with five different student information systems. If they have provided standard connectors then there still needs to be five different ones to handle the different systems. If they have not, then many users may have written their own, resulting in far more than five versions of connectors being written and maintained. With a Datahub, one connector will do the job for all customers from all five systems and more since the data is passing through and being standardized by the Datahub. When you consider the dozens of different student information systems and many dozens of other systems in use in the hundreds of school systems in the state, the massive potential for efficiency savings becomes apparent.

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