Specialised Technical Course
AI-Driven Business Intelligence and Decision Making
Develop an evidence-based approach to AI-assisted business intelligence by reviewing data quality, analytical assumptions and the interpretation of decision recommendations.
Course Overview
Examines AI-assisted business intelligence and decision support. Participants review data quality, analytical outputs and the assumptions that influence recommendations and business interpretation.
Who This Course Is For
- Business intelligence and data analysts
- Managers reviewing analytical decision support
- Data specialists assessing AI-generated insights
- Professionals interpreting enterprise performance information
What Participants Will Learn
Assess data quality for business analysis
Review analytical outputs and their assumptions
Connect insights with relevant business context
Evaluate evidence behind AI-assisted decision recommendations
Explain limitations in a proposed business decision
Key topics
- Data quality
- Analytical outputs
- Decision assumptions
- Result interpretation
Course Modules
Back to Course OverviewBusiness Intelligence and AI Context
Business Intelligence and AI Context
Module overview
Review data quality, analytical outputs, assumptions and interpretation within an AI-assisted decision support workflow.
Key topics
Data quality · Analytical outputs · Decision assumptions · Result interpretation
Learning outcome
Identify the evidence and assumptions involved in an AI-assisted insight.
Learning format
Technical explanation · worked examples · evidence review
Data Quality and Business Context
Data Quality and Business Context
Module overview
Assess data sources and the business context needed to interpret measurements and analytical results.
Key topics
Data sources · Quality checks · Business context · Measurement boundaries
Learning outcome
Identify data quality concerns and contextual information needed for analysis.
Learning format
Technical explanation · worked examples · evidence review
Analytical Outputs and Assumptions
Analytical Outputs and Assumptions
Module overview
Review AI-assisted analytical outputs and identify assumptions affecting their reliability and relevance.
Key topics
Analytical outputs · Model assumptions · Result consistency · Evidence limitations
Learning outcome
Explain an analytical result and identify assumptions requiring further review.
Learning format
Technical explanation · worked examples · evidence review
Decision Support and Interpretation
Decision Support and Interpretation
Module overview
Connect analytical evidence with a proposed business decision and distinguish observed findings from recommendations.
Key topics
Decision assumptions · Result interpretation · Alternative explanations · Recommendation review
Learning outcome
Assess whether a recommendation follows from the available analytical evidence.
Learning format
Technical explanation · worked examples · evidence review
Business Intelligence Decision Case
Business Intelligence Decision Case
Module overview
Review an AI-generated insight and explain its evidence, assumptions and implications for a business decision.
Key topics
Analytical dataset · Decision case study · Insight assessment · Evidence explanation
Learning outcome
Participants can assess an AI-generated insight and explain the evidence behind it.
Learning format
Lectures, analytical datasets and decision case studies
Programme Information
Course duration, location, practical components, training-centre information and applicable certification arrangements depend on the selected programme.
Learning format: Lectures, analytical datasets and decision case studies
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