Action Overview: Develop tools and processes to identify, collect, analyze and respond to data

For organizations to successfully utilize a decision support data system, the data itself must be high quality. This means it must be accurate, secure, useful, and timely. In addition, in order to use data as a part of continuous improvement, processes and tools must be in place for:

· Identification of high-quality data that will support making decisions and answering questions about implementation

· Collection of data in a systematic, timely way

· Preparing data so that it is useful for analysis

· Analyzing and using the data to make decisions and answer questions about implementation in order to adjust and improve practices

Developing processes and tools to guide continuous improvement will help teams:

· Use data to make decisions and answer questions

· Ensure that data being used is useful and timely

· Avoid barriers to accessing, understanding, and using relevant data

Suggested Activities

How do Portal resources support the development of a shared understanding of a decision-support data system?

· Activity Directions: Planning for a Decision-Support Data System guides teams to inventory current data use, then identify and plan for the collection and use of data to make decisions and answer questions about implementation.

· Activity Directions: Data Displays guides teams to explore best practices in displaying data so that data is usable for making decisions.

· Activity Directions: Processes for Analyzing and Responding to Data helps teams build a shared understanding of applications of technical and adaptive decision processes, then provides two optional processes for analyzing and responding to data for continuous improvement.

Related Content

For resources to create an action plan for engaging community and building hospitable environments, visit the Active Implementation Hub (AI Hub). https://implementation.fpg.unc.edu/

Sources

The Active Implementation Hub, Handout 28: Decision Support Data System, retrieved 8/9/19 from https://nirn.fpg.unc.edu/sites/nirn.fpg.unc.edu/files/resources/AIHub-Handout28-DriversEd-DSDS.pdf

Geier, R., Smith, S., (2012). District and School Data Team Toolkit. Everett, WA: Washington Office of Superintendent of Public Instruction, Washington School Information Processing Cooperative, and Public Consulting Group.

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