The many angles of Data Quality in Kansas Data Quality Showcase The many angles of Data Quality in Kansas Data Quality Showcase: The many angles of Data Quality in Kansas Achieving data quality is not as straightforward as it may appear on the surface – and to be effective, efforts to achieve quality need to be approached from many angles.  In Kansas, we have found that data quality requires a systemic approach that addresses common quality issues on a variety of fronts, both technical and nontechnical. In this session, you will hear how Kansas has approached data quality systemically through data quality audits, training, dashboard development, master data management practices, and software application edits and standards. Kathy Gosa, Director, KSDE Kelly Holder, Data Analyst, KSDE Kateri Grillot, Senior Trainer, KSDE 25th Annual STATS-DC 2012 Data Conference July 11, 2012

Kansas Education Landscape 286 school districts - over 1500 schools Approximately 520,000 (Pre K-12) students Combination of rural vs. urban About 37,000 educators Issue approximately 20,000 licenses annually Local control – approximately 17 different student information systems Elected Board – appointed commissioner

Achieving Data Quality Input Training Communication Common User Experience Use Reports Dashboards Quality Management Governance Master Data Management SW Development Standards Data Audits

Input: Training New user training Communication Common User Experience Input: Training New user training In-person Online On-demand Training before and during collections Data Quality Certification Program

DQC Program Overview Data Entry Source System Data Stewardship, Data Calendar, Audit Checklists, Error Prevention Data Coordinator All of Data Entry Curriculum Plus: Submission Practices, Error Correction Procedures Administrator Sign Off Process, AYP, Funding, Hiring Procedures, Information Sharing Among Staff Assessments & Accountability State Assessment Data, Reports, AYP Building a Culture of Quality Data, Student Privacy, Resource Navigation Data Ethics, Annual Re-Certification Assessments & Accountability Data Career & Technical Education Data Enrollment & Transportation Data Special Education Data Migrant Student Data Graduation & Dropout Data

Input: Communication Listserv Conference calls HelpDesk Training Communication Common User Experience Input: Communication Listserv Deadlines/reminders Question of the Week Conference calls HelpDesk Tech support via phone Reporting guidance via email

Input: User Experience Standards Training Communication Common User Experience Input: User Experience Standards Single sign-on via Common Authentication System

Input: User Experience Standards Training Communication Common User Experience Input: User Experience Standards

Input Training Communication Common User Experience KIDS New Staff Training Pre-Collection Workshops Data Coordinator DQC Track Weekly KIDSINFO Listserv KIDS HelpDesk Planned Changes KIDS Collection v8.0 Training Communication Common User Experience

Management: Governance Master Data Management SW Development Standards Data Audits Management: Governance KSDE implemented a Data Governance Program in 2005. This program includes definition and responsibilities for data ownership and data stewardship, outlines the goals and objectives for the Board, the Data Steward Workgroup, and the Data Request Review Board.

Management: Governance Master Data Management SW Development Standards Data Audits Management: Governance Data Quality is a guiding principle of the Data Governance Board. The Data Governance Board is focused on policy considerations for all data across the agency. This includes: source data collections; deadlines; KSDE Enterprise Data Warehouse; communication regarding data and policy; reporting access including federal and legislative, local, and research requests; establishing certification requirements; and master data management. security of data; data verification;

Management: Master Data Management Governance Master Data Management SW Development Standards Data Audits Management: Master Data Management KSDE’s Enterprise Data System includes integration of data through implementation of Master Data Management processes to ensure core data are captured, defined, and used consistently across the enterprise.

Management: Master Data Management Governance Master Data Management SW Development Standards Data Audits Management: Master Data Management Identify those data elements collected and stored by the agency that should be considered the master source for specified purposes. Document policies and procedures related to Master Data Management so expectations related to this are clear and can be enforced.

Management: Master Data Management Governance Master Data Management SW Development Standards Data Audits Management: Master Data Management Ensure that whenever a system managed by the agency includes information consistent with a defined master data element, the system will use the master data element rather than collecting the data element separately. This is to ensure: No redundant data collections occur. Data are used consistently and appropriately. Data systems can be connected allowing sharing of data across the agency.

Management: SW Development Standards Governance Master Data Management SW Development Standards Data Audits Management: SW Development Standards Software Development standards not only lead to efficiencies but also contribute to data quality through data integration & communication. Consistent technologies (e.g., Microsoft .Net, SQL Server) Technical Design standards Naming Conventions Harvest Logic Code Reusability

Management: Data Audits Governance Master Data Management SW Development Standards Data Audits Management: Data Audits Data Audit Framework Purpose: Implement a systemic process for using data audits to improve data quality. Process Ownership: Data Governance Board. Process Design: Builds on other initiatives (KIDS, DQC, EDS).

Management: Data Audits Governance Master Data Management SW Development Standards Data Audits Management: Data Audits Data Audit Cycle: Identification Evaluation Analysis Use

Management: Data Audits Governance Master Data Management SW Development Standards Data Audits Management: Data Audits Data Audit Reports Identify: Data Quantity and complexity — critical for project scoping Active data — data that should be in scope for the project Inactive data — data that needs to be properly flagged in the source system Source data issues and problems that can cause load errors

Management: Data Audits Governance Master Data Management SW Development Standards Data Audits Management: Data Audits Data Audit Reports Identify: Missing, incorrect, or unusual values Duplicate records within a source and/or across sources Distribution frequencies of common configurations (i.e., AYP School, Funding School, AYP Student Subgroup)

Management: Data Audits Governance Master Data Management SW Development Standards Data Audits Management: Data Audits Data Audits provide recommendations for: Level of Detail Data Cleansing Configuration Considerations Opportunities for Professional Development

Management: Data Audits Governance Master Data Management SW Development Standards Data Audits Management: Data Audits Completed Data Audits KIDS (core student data collection) Educator Assessment Migrant Planned Data Audits Career and Tech Ed Special Education

Management Governance Master Data Management SW Development Standards DGB reviews annual planned changes & DRRB monitors release of KIDS data KIDS web services used to populate core student data in all student data collection and reporting systems (e.g., SPED, CTE, Migrant, CNW, School Finance, Assessments) KIDS Data Audits are used internally and externally to improve quality Governance Master Data Management SW Development Standards Data Audits

Use Reports Dashboards Use: Reports Fictitious Data

Use Reports Dashboards Use: Dashboards Fictitious Data

Use Reports Dashboards Approximately 40 different user reports in 8 different categories Dashboards are the subject of required homework exercises in the DQC Program Reports Dashboards Fictitious Data

How do you know when its impacting data quality at the SEA? The trend of repeated data audits shows decrease in issues. State’s fiscal auditors have observed a reduction in reporting errors. Data owners and data stewards comment that the data are becoming more reliable. There is a reduction in AYP Helpdesk call volume.

How do you know when its impacting data quality in the LEA? “Our school district set a new record for low percentage of errors on our 9/20 data.”

Questions? Kathy Gosa, kgosa@ksde.org Kelly Holder, kholder@ksde.org Kateri Grillot, kgrillot@ksde.org