Data Integration Assessment
Data Integration Assessment Requirements
Data Integration Assessment Approach
- Incomplete, inconsistent, duplicate data
- Do not have reliable, 360-degree or complete view of a customer/supplier
- Incomplete view undervalues high-value relationships
- Not able to make relevant cross-sell/up-sell recommendations
- Lack of data foundation or governance to go digital
- Too expensive to identify and attract new customers/markets
- Match / Merge Analysis
- Decide High Level Technical Architecture
- Design Conceptual Data Model
Data Discovery Assessment Approach
- Understand current ETL architecture and usage.
- Interviews /Questionnares for ETL developers.
- Identify key findings.
- Compare current situation with best practices
- Apply Health Checks
- Define standards.
- Calculating Assessment Score
- Create an assessment report
- Current situation
- Recommendations etc…
Assess Capabilities – Scoring
- Understand current ETL architecture
- What are the environments such as Dev, Test and Prod and their Resources?
- What are source and target systems?
- Technical Architecture?
- How is deployment working?
- User Management, Groups and Roles
- Interview/Questionnaire with Developers
- Challenges in ETL development
- Identify Developer behaviour
- Measure Server Performance
- Identify ETL Jobs with low performance
- Defining ETL Standards for Naming, Development, Deployment
- Checklist for ETL Standards
- Assess and Strategies to Improve Performance
- ETL Jobs with low performance (Small Workshop to check results)
- Server Performance
- Comparing current situation with best practices
- Repository & Domain Management
- Team Based Development and Versioning
- Security and User Management
- Assess Deployment problems
- Creating an Assessment Report
- Current Situation
- Data Integration Assessment Score
- Architecture Recommendations
- Performance Findings & Recommendations
- Standards Best Practices
- HA/DR Recommendations
- Deployment Recommendations
- Presentation for Assessment Results