Typically, solutions are created using "happy-day" data and some limited data that supports "negative" scenarios (to test error conditions). With complex, multi-system integrations becoming the norm, it's getting more likely that data quality issues will affect the success of the solution. Utilizing this approach, problems aren't identified until the end of the sprint or in the the next testing sprint.
Here's an alternative – quickly perform standardized data profiling activities using a data profiling tool to identify basic data quality issues and issues with data service designs (e.g. join criteria). Investing ½ day in a 10 day sprint can have a significant impact on the quality of the integration solution.
Identifying data quality problems early is key to minimizing risk with data being integrated from disparate systems – part of our ConnectedBlueprint where complex system integration risk is mitigated using best practices from our deep engineering experience.
Here are some related videos:
- [Video] Typical Agile approach to Data Service development
- [Video] Sample Scenario Background
- [Video] Typical Agile Approach Sample Scenario Part 1
- [Video] Typical Agile Approach Sample Scenario Part 2
- [Video] Typical Agile Approach Sample Scenario Part 3
- [Video] Typical Agile Approach Sample Scenario Findings
- [Video] Typlical Agile Approach Sample Scenario Next Steps
- [Video] QTA Approach -A typical day in the life
- [Video] QTA Approach - Sample Scenario
- [Video] QTA Quick Observations
- [Video] QTA Action Plan
- [Video] Hands on Session with Talend Studio for Data Quality (Setup)
- [Video] Hands on Session using TOS-DQ (Conventional Analysis)
- [Video] Hands on Session using TOS-DQ (QTA Approach) Part 1
- [Video] Hands on Session using TOS-DQ (QTA Approach) Part 2
- [Video] Hands on Session using TOS-DQ Multi-Column Analysis
- [Video] TOS-DQ- Advance Features
- [Video] QTA Closing Thoughts
Interested in reading the original blog? Please click below: