Download to read offline
More Related Content
![Data Analytics Life Cycle [EMC² - Data Science and Big data analytics]](https://cdn.slidesharecdn.com/ss_thumbnails/bdalifecycle-slideshare-211028070344-thumbnail.jpg?width=640&height=640&fit=bounds)
Similar to Data Quality at the Speed of Work
Data Quality at the Speed of Work
- 1. T5 Test Data Management 5/11/17 9:45 Data Quality at the Speed of Work Presented by: Shauna Ayers Catherine Cruz Agosto Availity Brought to you by: 350 Corporate Way, Suite 400, Orange Park, FL 32073 888-‐-‐-‐268-‐-‐-‐8770 ·∙·∙ 904-‐-‐-‐278-‐-‐-‐0524 -‐ info@techwell.com -‐ http://www.starwest.techwell.com/
- 2. Shauna Ayers Shauna Ayers has been untangling the Gordian knots of IT systems for more than seventeen years, analyzing data systems and testing both software and data quality in the manufacturing, medical device, and healthcare industries. Shauna found her passion in developing creative solutions for the analysis and testing of sensitive and highly regulated data sets at industry leaders such as Blue Cross Blue Shield of Florida (now Florida Blue), Vistakon (a subsidiary of Johnson & Johnson), and Availity. Catherine Cruz Agosto Catherine Cruz Agosto found her software engineering experience at Baxter Healthcare and Boeing-‐subsidiary Insitu provided an excellent foundation for finding more effective and user-‐friendly approaches to complex technical problems. Catherine has developed more efficient and innovative data quality testing solutions at healthcare intermediary Availity, expanding their automated data quality testing processes to accommodate diverse and dissimilar data sources, thus facilitating analysis, testing, and controls for data integration, analytics, and healthcare data reporting.
- 3. Data Quality at the Speed of Work By Shauna Ayers and Catherine Cruz Agosto
- 4. Overview • Definitions • Why is this important? • What strategies can we use? • What benefits do these activities bring us? • What tools do we use? • Case Studies • Communication • Conclusion
- 5. Defini+ons ● Dataquality (DQ) is data's fitness and usability for its intended purpose. ● Data quality assurance is the monitoring and analysis of data sets and the processes that create or manipulate data, in order to ensure the data’s quality meets the company's needs. ● DQ Issue: Incorrect or unexpected behavior from the data as a result of unknown data scenario, upstream change, flaw in logic, missing requirements, etc. ○ Timing Issue: A type of issue/defect in which the root cause stems from the timing between two or more components of the system that depend on each other.
- 6. Why is this important? • Consumers expect data to be instantly available • Consumers expect near-zero downtime • Automation and algorithmic transactions cause a small data issue to snowball quickly • If consumers don’t feel they can trust your data, they won’t be your customers for long
- 7. What strategies can we use? ● Types of Testing ○ Exploratory ○ Manual ○ Automated ● Continuous Regression ○ Production Monitoring vs Monitoring Lower Environments ● Continuous Data Profiling
- 8. What strategies can we use? (con6nued) ● Types of Checks and how to use them to identify timing issues ○ Business Rule Validations: Type of test that verifies all of the acceptance criteria by comparing the source data to the target data. ■ This type of check catches any discrepancies or deviations from the acceptance criteria. ○ Null Checks: Type of test that verifies key fields are not null ■ Verify that fields that are expected to be populated are done so from the initial write, instead of as an update later on. ○ Duplicate Checks: Type of test that checks for any unexpected duplication of records, typically by use of alternate key. ■ Can be used to spot duplications that are created over time.
- 9. What strategies can we use? (con6nued) ● More types of Checks and how to use them ○ Environment Checks: Type of test that verifies if the process run is within tolerance. ■ Can be used to identify if and when process is running behind, which can explain any data issues with downstream processes. ○ Count Checks: Type of tests that compares the count of records in the source to the count of records in the target. ■ Timing issue could be a potential cause for count mismatch. ○ Compare Checks: Type of tests that compares the alternate key of records in the source to the alternate key of records in the target. ■ A mismatch in data could indicate potential timing issue ■ Can use compare check to get the details on a count check discrepancy
- 10. What strategies can we use? (con6nued) ● Even more types of checks and how to use them ○ Domain Integrity Checks: Type of test that verifies the values used in specified field exist in the corresponding code set. ■ Could indicate discrepancy between timing of value added to code set and use of code value. ○ System Version Checks: Type of test that checks when there are changes to the version the system is running on. ■ Changes and/or updates to system versions can cause unexpected issues such as difference in process behavior, difference in system clocks, etc.
- 11. What benefits do these ac+vi+es bring us? • Opportunity to fix issues before the customer sees or reports them • Faster localization of root causes • Better visibility of chronic issues rooted in timing and environment • Better visibility of changes in input profiles • Cleaner integration with existing operational support
- 12. What tools do we use? ● Buying DQ testing software o Common tools: Informatica Data Quality, Datamartist, Microsoft Data Profiling Task o All tools have some sort of limitations o Can get expensive ● Creating custom test harnesses o Seems more time consuming up-front o More control/ less limitations compared to pre-bought ● Machine cannot replace a human
- 13. Case Studies: Data Integra+on Timing ● Definition: The timing of ETL processes in relation to each other and the supporting systems they depend on. Risks affect execution order, dependencies, and load rule boundaries across processes. ● Useful Checks: o Count/ Compare checks o Tolerance/Threshold checks (includes cycle time checks) o Environment checks o Business Rule Validations ● Case Studies o Hybrid systems – the velocity/dependency trap o Clock syncs sink ships o Who watches the watchmen? o Surge Protection
- 14. Case Studies: Opera+onal Dependencies ● Definition: Two or more processes of a system or components of a process that rely on each other. ● Useful Checks: ○ Codesets ○ BRV ○ Null Checks ○ System Version Checks ○ Count/ Compare checks ○ Environment Checks ● Case Studies ○ Rocket Failure ○ Data Warehousing ○ UI to Backend
- 15. Case Studies: Reference Data Management ● Definition: Reference values are used to drive categorization, routing and filtering, and may provide part of the focus for dimensional data. They are normally controlled data sets. Some ● Useful Checks: o Domain checks o Tolerance/Threshold checks o Consistency checks ● Case Studies o Point-of-Use Domain Checks o Rate of Dimensional Growth (runaway conditions in the content) o Process violations
- 16. Case Studies: Data Integrity ● Definition: The correctness of the data in or outputted from the system ● Useful Checks: o BRV o Null checks o Domain Checks o Null Checks o Duplicate Checks o Count/ Compare Checks o Environment Checks ● Case Studies o Transaction Processing o Reporting
- 17. Communica6on: Proac+ve No+fica+on Alerts • Automated no+fica+on mechanisms can be integrated easily with exis+ng opera+onal alert mechanisms (e.g., pager duty) • No+fica+ons and alerts can be tailored to support and reinforce data stewardship
- 18. Communica6on: Business Intelligence Dashboards ● External Dashboards ○ Potential Users: Customers, Production Support, Customer Service, Business ● Internal Dashboards ○ Display more granular data regarding processes and/ or tests ○ Drill-through
- 19. Communica6on: Trends Analysis ● Performance and tolerance checks over time reveal cyclic impacts from maintenance activities or correlation of surges in quality issues to specific business activities. These drive preventive measures, capacity planning and performance tuning.
- 20. Conclusion ● Proactivedata quality saves an organization time and money. ● Data is the fastest changing element of an organization; there is no cookie cutter way of monitoring or testing, but there are known strategies that can be used to help maneuver the course. ● Metadata about data quality testing can be used to communicate issues faster, more easily target the correct parties, and provide insights as to the health of the systems that drive the organization.
- 21.