• Pre-course call: Tuesday, May 7th, 2024 12-1 pm ET
• Starts: Tuesday, May 14, 2024 (Pre-course call Tuesday, May 7^th)
• Ends: Thursday, June 13, 2024
• Frequency: Varies by week on Tuesdays and Thursdays, starting at 12PM ET
1. Instructor led sessions will run from 12-1PM ET
2. Office Hours (by registration) will be offered from 12-2PM ET on Thursdays in 30-minute increments
• Duration: 5 Weeks
• Language: The sessions will be run in English with live translation to Spanish and/or Portuguese as required.
  
  

Improving Data Quality

This sprint is designed to emphasize the importance of data quality in local government operations. The curriculum will cover best practices for maintaining data inventories, explore practical applications of high-quality data, and provide insights into the consequences of utilizing ‘bad’ data. Through a comprehensive approach, participants will be equipped with strategies, tools, and resources to help them develop a quality improvement plan for their city.

Who should attend?

This sprint is designed to equip city leaders and staff with a comprehensive understanding of data quality issues and provide the knowledge and tools needed to implement data standards and quality improvements within their respective roles and departments.

What will I learn/achieve during this 5-week sprint?

At the end of the Sprint, participants will be able to:

• Data provenance and quality management framework
1. Create or improve processes to identify, document, and audit the provenance, collection methodology, and known quality issues of available datasets. (DM 4.1, 4.2, 4,3, and 4.5)
• Documented internal data standards (DM 4.1, 4.2, and 4.3)

At the end of the Sprint, participants will be ready to:

• Define and identify three types of data standards (DM 4.1)
• Differentiate between internal and external data standards. (DM 4.1)
• Identify common data quality issues (DM 4.1, 4.2, and 4.5)
• Describe the value of improving data quality for your organization (DM 4.1, 4.2, and 4.5)
• Explain the role of data governance in data quality improvement (DM 4.1, 4.2, and 4.5)
• Explore how to approach the implementation of data quality improvement (DM 4.1, 4.2, and 4.3)

Within a year, participants could:

• Create and document standards for internal data in order to improve internal interoperability (DM 4.1, 4.2, and 4.3)
• Solicit community feedback to identify data quality issues & improve the quality of datasets determined to be inaccurate, incomplete, or suffer from fundamental limitations or bias (DM 4.5)

This course will put you on the path to achieving the following WWC Criteria:

DM4. Data Quality Improvement: Your local government has & carries out documented policies or practices to improve data quality. (Must meet 4 of 5)

• DM4.1: Your local government implements processes to document the provenance, collection methodology &/or known quality issues with datasets & to improve identified data quality problems (ie., conducting regular data quality audits, establishing feedback channels for data users, etc.).
• DM4.2: Your local government documents standards for internal data in order to improve internal interoperability, so that certain common fields (e.g., street address) are consistent across the organization.
• DM4.3: Your local government has implemented & published datasets that conform with at least one civic data standard (e.g., GTFS) in order to improve external interoperability.
• DM4.4: Your local government produces high-quality & complete metadata for both protected and openly published datasets.
• DM4.5 Your local government solicits community feedback to identify data quality issues & improve the quality of datasets determined to be inaccurate, incomplete, or suffer from fundamental limitations or bias.