Qlik – AI analytics platforms
- Duration: 7 hours
- Exam Voucher: Yes
- Language: English
- Course Delivery : E - Learning Access
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Course Overview
The Qlik / Sisense / ThoughtSpot – AI Analytics Platforms course equips professionals with hands-on skills to design and operate modern analytics experiences powered by AI-assisted exploration. Learners gain practical experience building governed dashboards, enabling self-service analysis, and using natural-language insights to reduce time-to-answer—while maintaining security, consistency, and stakeholder trust. This course is designed for organisations that want analytics to be a daily habit, not a monthly report: clean metrics, fast discovery, embedded experiences, and adoption playbooks that stick.
Course Details
- Understand how AI-assisted analytics works across leading BI platforms and why governance matters
- Build reliable dashboards and KPI layers that drive consistent decision-making
- Use AI search/NLQ to speed up analysis and reduce dependency on analysts for routine questions
- Enable embedded analytics for product and internal workflows (especially relevant for Sisense-style use cases)
- Improve data trust with semantic definitions, access control, and versioned metrics
- Strengthen adoption with rollout patterns, enablement assets, and usage measurement
- No prior AI analytics platform experience required (beginner-friendly)
- Helpful (not mandatory): basic understanding of data concepts (tables, joins, KPIs) and business reporting
- Best suited for: BI analysts, data analysts, product analytics teams, business stakeholders, RevOps/FinOps teams, and platform owners
- Understand the operating model of modern BI + AI analytics platforms
- Build dashboards and KPI layers with consistent definitions and governance controls
- Use natural-language querying and AI exploration to accelerate insights
- Design analytics experiences for different audiences (executives, managers, frontline users)
- Implement embedded analytics patterns for internal tools or customer-facing products
- Apply security, data access, and governance best practices for production rollout
- Measure adoption and improve usage through feedback loops and enablement workflows
- AI analytics platform landscape: Qlik vs Sisense vs ThoughtSpot—strengths and fit by use case
- Core BI foundations: measures, dimensions, filters, drill-downs, and dashboard storytelling
- Semantic layer & KPI governance: definitions, consistency, and trusted metrics
- AI-assisted exploration: natural-language queries, search-driven analytics, and insight surfacing
- Self-service analytics: reducing bottlenecks while maintaining guardrails
- Embedded analytics patterns: in-app dashboards, role-based experiences, and multi-tenant considerations
- Data connectivity & refresh: sources, schedules, performance basics, and reliability mindset
- Collaboration & sharing: narratives, annotations, subscriptions, and stakeholder alignment
- Security & governance: permissioning, row-level access patterns, auditability, and responsible usage
- Adoption playbooks: onboarding, templates, usage tracking, and continuous improvement
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