Data Analytics for Business Decision-Making

The programme

Programme overview

Organisations increasingly expect managers to make decisions backed by data, not instinct — but most managers were never trained to read, interpret, or question data critically. This Data Analytics for Business Decision-Making course builds practical data literacy and analytical thinking for non-specialists, focused on using data, including AI-assisted analytics tools, to make better business decisions, not on becoming a data scientist.

Through practical, case-study driven sessions using realistic business dashboards and datasets, you’ll work through chart interpretation, basic statistical reasoning, and framing genuine business questions with data. You’ll also learn to work effectively with AI-assisted analytics tools and evaluate their outputs critically, before presenting your findings clearly and persuasively to stakeholders. No coding, spreadsheet formulas, or statistical background required.

Who should attend

Who is this programme for?

This Data Analytics for Business Decision-Making course is designed for:

• Managers and Department Heads Who Use Data and Reports to Make Decisions
• Professionals Responsible for Interpreting Performance Dashboards or KPIs
• Non-Technical Staff Who Want to Build Genuine Data Literacy
• Anyone Preparing to Work More Closely with AI-Powered Analytics Tools

Learning outcomes

What you will learn

This Data Analytics for Business Decision-Making course equips you to read, question, and act on data with genuine confidence, and to communicate data-driven recommendations that stakeholders can act on.

By the end of this Data Analytics for Business Decision-Making course, you’ll be able to:

• Interpret data, charts, and dashboards accurately
• Identify common misleading data presentations and question them confidently
• Apply basic statistical reasoning to avoid common decision-making errors
• Distinguish correlation from causation and recognise other common analytical traps
• Use data to frame and test business questions rather than confirm existing assumptions
• Work effectively with AI-assisted analytics tools and interpret their outputs critically
• Recognise the limitations of AI-generated analysis and know when to seek deeper verification
• Present data-driven recommendations clearly and persuasively to stakeholders
• Read Data With Confidence: Interpret charts, dashboards, and reports accurately, without a technical background.
• Spot the Spin: Identify misleading data presentations before they lead you to the wrong decision.
• Fewer Decision-Making Errors: Apply basic statistical reasoning that catches common traps like correlation-causation confusion.
• AI-Analytics Ready: Work effectively with AI-assisted analytics tools and know when to question their output.
• Recommendations That Land: Present data-driven findings clearly enough to move a room, not just fill a slide.

Explore the programme

Detailed programme outline

Module 1: Data Literacy Foundations

• Reading charts, dashboards, and reports accurately
• Understanding common chart types and what they’re best used for
• Identifying the story a dataset is actually telling versus what it seems to say
• Building the habit of questioning data before acting on it

Practical Exercise: Interpret a sample business dashboard and summarise its key findings accurately.

Module 2: Spotting Misleading Data Presentations

• Common techniques that make data look more or less significant than it is
• Misleading axis scales, cherry-picked timeframes, and selective comparisons
• Questions to ask when a chart or report doesn’t feel right
• Building healthy scepticism without becoming a cynic

Practical Exercise: Identify the misleading elements in a set of deliberately manipulated sample charts.

Module 3: Basic Statistical Reasoning for Managers

• Averages, medians, and when each is the better measure
• Understanding trends and avoiding overreaction to normal variation
• Correlation versus causation, and why the difference matters for decisions
• Common statistical pitfalls that lead managers astray

Practical Exercise: Evaluate a sample business claim and determine whether it demonstrates correlation or causation.

Module 4: Framing Business Questions with Data

• Moving from confirming assumptions to genuine data-driven inquiry
• Structuring a business question so data can actually answer it
• Avoiding confirmation bias when interpreting results
• Knowing when the data available can’t answer the question being asked

Practical Exercise: Reframe a business assumption as a testable, data-driven question.

Module 5: Working with AI-Assisted Analytics Tools

• What AI-assisted analytics tools can and can’t do for business users
• Structuring queries to get useful, relevant output from analytics tools
• Common capabilities across today’s AI-powered dashboard and reporting tools
• Where AI-assisted analytics adds genuine time savings

Practical Exercise: Use an AI-assisted analytics tool to interpret a sample dataset and summarise the output.

Module 6: Evaluating AI Analytics Outputs Critically

• Common failure modes in AI-generated analysis and summaries
• Verifying AI-generated figures and claims before acting on them
• Knowing when AI output needs deeper human verification
• Building a critical-review habit into AI-assisted analytics workflows

Practical Exercise: Review a set of AI-generated analytics outputs and identify errors or unsupported claims.

Module 7: Communicating Data-Driven Recommendations

• Structuring a data-driven recommendation for maximum clarity
• Choosing the right visual for the message you’re delivering
• Anticipating stakeholder questions and objections
• Presenting findings persuasively without overstating what the data shows

Practical Exercise: Build and present a one-page data-driven recommendation for a sample business scenario.

Learning approach

How you will learn

Our diverse instructional approaches ensure effective learning:

– Lectures & Presentations: Engage with expert-driven, stimulating content.– Course Material: Access well-crafted supporting resources.– Group Work: Collaborate on discussions and case studies for practical insights.– Workshops & Role-Play: Participate in immersive, scenario-based activities.– Practical Application: Focus on applying theoretical knowledge in real situations.– Post-Training Support: Receive extensive support after training for skill implementation.

Discuss your requirements

Dates, duration & arrangements

Contact Ratomac to confirm dates, duration, delivery format, entry requirements and any applicable assessment or certification before booking.