Data Foundation Audit: 30-Day Assessment
Comprehensive step-by-step guide to audit your data infrastructure and prepare for AI implementation
Data Foundation Audit: 30-Day Assessment
Introduction
Auditing your organization's data foundation is a critical first step toward implementing AI solutions. This guide outlines a structured 30-day process to evaluate your data infrastructure, identify gaps, and ensure that your data is ready for advanced analytics and AI initiatives.
Key benefits include:
- Improved data quality and reliability
- Clear understanding of infrastructure gaps
- A roadmap for AI readiness
This guide is designed for Data Engineers, IT Managers, and Business Analysts with intermediate skills in SQL and data concepts.
Expected Outcomes
- Comprehensive data quality audit
- Infrastructure gap analysis report
- Governance framework for sustainable data management
- Actionable roadmap for AI implementation
Key Insights
- Data Quality Assessment: Ensuring data is accurate, complete, and consistent.
- Infrastructure Evaluation: Identifying readiness for AI and analytics.
- 30-Day Audit Timeline: A structured, achievable timeline.
- Implementation Roadmap: Clear next steps for AI readiness.
Implementation Overview
The 30-day audit is broken into four key phases:
- Planning and Preparation: Define objectives, stakeholders, and resources.
- Assessment and Analysis: Profile data and evaluate infrastructure.
- Roadmap Development: Develop actionable recommendations.
- Review and Finalization: Present findings and align on next steps.
Technologies required include Python, SQL, Azure Data Factory, and Power BI. Templates and tools, such as data quality templates and audit checklists, are provided to streamline the process.
Tools and Templates
- Data quality templates for profiling and metric calculations
- Audit checklists to ensure comprehensive coverage
- Assessment tools for infrastructure mapping
Success Metrics
- Percentage of data quality improvement (e.g., accuracy, completeness)
- Identification and closure of infrastructure gaps
- Governance framework adoption rate
- Alignment on the AI readiness roadmap
- Comprehensive data quality assessment
- Evaluation of existing infrastructure readiness
- 30-day structured audit timeline
- Development of a clear implementation roadmap for AI readiness
- 1Conduct data profiling and assess current quality
- 2Define and measure key data quality metrics
- 3Map data infrastructure and identify gaps
- 4Establish a governance framework for data management
- Data Engineers
- IT Managers
- Business Analysts
- Basic SQL knowledge
- Understanding of data concepts