Most Organizations Focus on AI Readiness
Organizations are investing heavily in AI tools, automation, prompts, productivity systems, and workflow improvements.
Those things matter. But they are not where readiness begins.
AI Readiness is the visible outcome of Data Readiness and Analytical Readiness.
Before people can use AI effectively, they need to understand the information AI consumes, transforms, analyzes, and presents.
The Three Readiness Layers
The Data Competency Path is built around a simple progression. Analytical Readiness creates the foundation for Data Readiness. Data Readiness creates the foundation for AI Readiness.
Analytical Readiness
Before asking AI for answers, people need to understand the questions.
- What problem are we solving?
- What decision are we supporting?
- What does this number mean?
- What business rule produced this result?
- How do we know this is correct?
Data Readiness
Once the question is understood, the next challenge is determining whether the information can be trusted.
- Complete information
- Accurate information
- Current information
- Consistent information
- Structured information
- Missing information awareness
- Assumption identification
AI Readiness
AI becomes more valuable when the analytical and data foundations already exist.
- Microsoft Copilot
- ChatGPT
- Claude
- Prompt development
- AI-assisted analysis
- Workflow automation
- Decision support
Understanding the Journey of Information from Source to Decision
The Data Competency Path™ teaches learners how information moves from source systems, through analysis, into reporting, and ultimately into business decisions.
SQL: Understanding the Source
Learn where information originates and how it is retrieved, filtered, joined, validated, and prepared for analysis.
- Tables and relationships
- Queries and joins
- Data extraction
- Business rules
- Information quality
Excel: Understanding the Information
Learn how to organize, explore, validate, and communicate information.
- Workbook design
- Data analysis
- Excel formulas
- PivotTables
- Decision support
Power BI: Understanding the Decision
Learn how information becomes metrics, dashboards, reports, insights, and decisions.
- Power Query
- Data modeling
- DAX measures
- Visualization
- Executive dashboards
Flexible Learning Options
Learners may study SQL, Excel, or Power BI as standalone courses.
They may also complete the full Data Competency Path™ and understand how information flows across all stages of the analytics process.
The goal is not simply learning software. The goal is developing the ability to understand information well enough to make better decisions.
About
The Data Competency Path™ was developed from a simple observation: most jobs are information jobs.
People are expected to make decisions from information every day, yet training often treats SQL, Excel, Power BI, SharePoint, and AI as separate topics rather than connected ideas.
The Data Competency Path brings these ideas together by focusing on information quality, business rules, analytical thinking, and decision-making before technology.
Roger Steinbronn is an instructor, curriculum developer, and consultant with decades of experience helping organizations use SQL, Excel, Power BI, SharePoint, and AI to solve information problems.
His work focuses on information quality, business rules, analytical thinking, and decision-making.
The Data Competency Path was developed to help learners understand not just software tools, but the information concepts that connect them.The goal is not simply learning software. The goal is developing analytical thinking and the ability to understand information well enough to make better decisions.