Course overview
Operations generate a constant stream of data, yet many decisions are still made on gut feel or last month's report. Data-driven decision-making closes that gap: using analytics to understand what happened, why, what is likely next, and what to do about it. This course gives operations professionals a structured command of the analytics types and decision tools that turn data into better, faster operational choices.
Participants move from the fundamentals of evidence-based decisions through descriptive and diagnostic analytics, predictive forecasting, and prescriptive optimization. The course covers building meaningful KPIs and dashboards, structured decision-making models, and the cultural change needed to make analytics stick, using cross-industry cases throughout.
Why this matters in operations
Decisions made without data are slower to correct and easier to bias, and small errors compound across an operation. Organizations that build analytics into daily decisions respond faster, waste less, and spot problems earlier. The barrier is rarely the technology; it is knowing which analytics to use and building a culture that trusts them, which is where these skills complement the Data Analytics for Operations and Performance Monitoring course.
What you will be able to do afterwards
By the end of the course, participants will be able to:
- Apply evidence-based decision-making principles to operations.
- Use descriptive and diagnostic analytics to find trends and root causes.
- Apply predictive and prescriptive analytics to anticipate and optimize.
- Design KPI frameworks and dashboards linked to strategy.
- Foster a data-driven culture and overcome resistance to analytics.
Course outline
Unit 1: Fundamentals of data-driven decisions
The unit sets out the case for evidence-based operations.
- Principles of evidence-based decision-making.
- The role of analytics in operations.
- Business benefits and challenges.
- Case examples across industries.
Unit 2: Descriptive and diagnostic analytics in operations
Participants examine understanding what happened and why.
- Tools for descriptive analysis.
- Identifying performance trends and patterns.
- Diagnostic methods for root-cause analysis.
- Case studies in operational analytics.
Unit 3: Predictive analytics for decision making
The unit covers anticipating what is likely next.
- Forecasting models and applications.
- Anticipating risks and opportunities.
- Predictive maintenance and demand forecasting.
- Worked forecasting examples.
Unit 4: Prescriptive analytics and optimization
Participants study deciding what to do.
- Optimization frameworks for operations.
- Resource allocation and scheduling.
- AI and machine learning for prescriptive insights.
- Real-world case studies of prescriptive decisions.
Unit 5: KPI frameworks and dashboards
The unit turns data into visible performance signals.
- Designing relevant KPIs for operations.
- Linking KPIs to strategic goals.
- Building dashboards for real-time decisions.
- Benchmarking best practices.
Unit 6: Decision-making models and tools
Participants examine structured ways to decide.
- Structured decision-making frameworks.
- Scenario analysis and what-if modeling.
- Decision support systems.
- Aligning analytics with managerial judgment.
Unit 7: Building a data-driven culture
The closing unit addresses making analytics stick.
- Leadership's role in promoting data-driven thinking.
- Overcoming resistance to analytics adoption.
- Training and upskilling teams.
- A roadmap for data-driven transformation.
How the course is delivered
The course combines structured teaching with cross-industry case studies and guided analysis of operational data. Participants work through descriptive, predictive, and prescriptive analytics on realistic examples, so the methods and the judgment to apply them transfer to their own operations. The emphasis is on applied reasoning rather than statistical theory.
Who should attend
The course suits operations and production managers, business and operations analysts, continuous-improvement and performance staff, and team leaders who want to base decisions on data. A basic comfort with numbers is helpful; advanced statistics are not required.
About EuroQuest International Training
EuroQuest International Training is an international training provider founded in 2015, with a catalog of more than 1,000 courses delivered to over 15,000 participants. Headquartered in Bratislava, EuroQuest runs courses across a network of European and regional training hubs and focuses on practical, current, and professionally relevant content.
Frequently asked questions
Do I need to know statistics or coding for this course?
No. The course focuses on which analytics to use and how to act on them, explaining the concepts without requiring statistical or programming skills. Those with a technical background will still gain the operational framing.
Does the course cover the difference between predictive and prescriptive analytics?
Yes. It treats descriptive, diagnostic, predictive, and prescriptive analytics as distinct stages, showing what each answers and when to use it, so participants can match the right analytics to the decision.
Is the course specific to one industry?
No. It uses cross-industry examples because the decision-making methods apply across manufacturing, services, logistics, and more. Participants apply the approach to their own operational context.
Related courses
- AI-Driven Decision Making in Operations
- Big Data Analytics and Predictive Modeling
- Business Intelligence Tools and Applications
- Statistical Analysis for Data-Driven Decision Making
Register for this course
To reserve a place or request an in-house session for your team, contact EuroQuest International Training and our team will help you confirm dates and details.
All Course Dates & Locations
25 dates · 17 cities · Oct 2026 – Jun 2027