Approximately 650 registrants from around the world

Approximately 650 registrants from around the world

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Agenda A word from Directions Magazine Nora Parker Importance of data quality Jay Cary Automating data review Jay Cary / Michelle Johnson Facilitating manual data review Jay Cary / Michelle Johnson Managing and reporting the quality control process Jay Cary / Michelle Johnson Q&A

Poll #1 Have you had an experience where you thought you were working with good data, but it turned out not to be the case? (choose all that apply) a. Yes and it affected my project deliverables b. Yes and it affected my data s / program s credibility c. Yes and it caused an expense for which we hadn t planned d. Yes and we didn t win follow-on work e. No we have a quality control process already in place

Importance of data quality Jay Cary Product Manager Esri Michelle Johnson Product Specialist Esri

Defining Quality What is good quality? Spatial Accuracy Thematic Accuracy Completeness Logical Consistency Temporal quality Usability Requirements are defined: Intended purpose and usage Technical specifications Client requirements (ISO-19157 (DRAFT), 2010)

Data Quality Matters Perspectives on quality data management Executive Confidently make decision Reduce financial risk Optimize organizational performance Manager Effective data stewardship Maximize GIS productivity Drive increased GIS usage Data Consumers Increased efficiencies Confidence in GIS Ability to provide feedback

What is ArcGIS Data Reviewer? Data quality management for ArcGIS Provides Rule based workflows Interactive tools Track errors For individuals and enterprise Saves time/money Less rework Review Enterprise Workflow Standard extension ArcGIS for Desktop ArcGIS for Server New Verify Correct Trusted data through improved quality management

Quality Control Workflow Visual Review Automated Review Server Adhoc/Scheduled Data Workspace(s) Connected Users Quality Reporting Dashboard Reporting Desktop Adhoc/Scheduled Reviewer Workspace

Types of Quality Control Automated Review Visual Review Fast As subjective as needed Consistent and repeatable Objective 100% coverage Better for finding patterns and missing elements

Managing Quality Control Scalable framework for managing QC life cycle REVIEW Find & Record Errors

Managing Quality Control Scalable framework for managing QC life cycle REVIEW CORRECT Find & Record Errors Perform Edits or Note Exceptions

Managing Quality Control Scalable framework for managing QC life cycle REVIEW CORRECT VERIFY Find & Record Errors Perform Edits or Note Exceptions Acceptable or Unacceptable

Poll #2 How does your organization handle QA/QC? a. Manually b. We use third-party products for data validation c. We use custom scripts for validation d. We don t have processes defined for QA/QC

Visual Review Automated Review Server Adhoc/Scheduled Connected Users Quality Reporting Dashboard Reporting Data Workspace(s) Desktop Adhoc/Scheduled Reviewer Workspace AUTOMATING DATA VALIDATION

Automating Data Validation Implement Quality Requirements as Rule-Based Data Reviewer Checks 42 data checks Configurable Scriptable/Additive Ad-hoc validation Batch validation Multiple execution methods www.esri.com/datareviewer

Batch Validation Encapsulate workflow / process Designed once and executed many times Complete specifications check Subject Matter Experts Industry Standards / Specifications Training & Experience Quality Assurance Plans Configure as Rule-based Checks Batch Job

Methods for Executing Data Validation Within ArcMap From ArcToolbox Within a model/python script As a step in ArcGIS Workflow Manager As a service of ArcGIS for Server

Automated Data Review Demo

Quality-focused Management Executive Leadership Governance IT Services Business Line Management Division Division Division Division QA Lead Branch Branch Branch Branch Branch Subject Matter Expert Subject Matter Expert

Visual Review Automated Review Server Adhoc/Scheduled Connected Users Quality Reporting Dashboard Reporting Data Workspace(s) Desktop Adhoc/Scheduled Reviewer Workspace FACILITATING VISUAL DATA VALIDATION

Visual Data Validation Visual inspection of the data Data missing or in incorrect location Compare between data sources Level of judgment/discretion Strong knowledge of data & quality requirements

Visual QC with Data Reviewer Facilitating manual data validation Visual QC review across ArcGIS platform Simple to use tools for error identification Systematic process Random Sampling

Visual Data Review Demo

Quality-focused Management Executive Leadership Governance IT Services Business Line Management Division Division Division Division QA Lead Branch Branch Branch Branch Branch Subject Matter Expert

Visual Review Automated Review Server Adhoc/Scheduled Connected Users Quality Reporting Dashboard Reporting Data Workspace(s) Desktop Adhoc/Scheduled Reviewer Workspace MEASURING AND REPORTING DATA QUALITY

Data Quality Reporting Enabling transparency in data quality Better decision making by communicating data quality across stakeholders Open quality reporting Shared across ArcGIS system Tools and methods to communicate quality

Managing Errors and Reporting Data Quality Demo

Quality-focused Management Executive Leadership Governance IT Services Business Line Management Program Analyst Division Division Division Division Branch Branch Branch Branch Branch

ArcGIS Data Reviewer Automate, Simplify, and Improve your Quality Control Process Extension to Desktop/Server Supports complete QC process Provides Rule based workflows Interactive tools Track errors For individuals and enterprise Saves time/money Less rework

Resources Product page www.esri.com/datareviewer 60-day Free Evaluation Data Reviewer Checks poster Customer Success Stories Demos Training www.esri.com/training Data Reviewer Resource Center http://resources.arcgis.com/ Email questions and comments datareviewer@esri.com