Business Analytics for Non-Analysts
Gain practical insights into data-driven decision-making by learning key business analytics tools and techniques - no prior analytics experience required.
Duration: 1 Day
Hours: 2 Hours
Training Level: All Levels
Batch One
Monday, January 05, 2026
12:00 PM - 02:00 PM (Eastern Time)
Batch Two
Monday, February 02, 2026
12:00 PM - 02:00 PM (Eastern Time)
Batch Three
Monday, March 02, 2026
12:00 PM - 02:00 PM (Eastern Time)
About the course:
In today’s data-driven world, every professional, not just analysts, needs to understand how to interpret data, derive insights, and make informed decisions. This workshop provides a practical, jargon-free introduction to business analytics, helping participants learn how to think analytically, ask better questions, and turn raw data into actionable insights. Designed for non-technical professionals, it focuses on decision-making, storytelling, and collaboration with analytics teams.
Course Objectives
By the end of this course, participants will be able to:
- Understand the core concepts of business analytics and data-driven decision-making.
- Identify and define key business questions that can be answered with data.
- Interpret dashboards, KPIs, and metrics with confidence.
- Differentiate between descriptive, diagnostic, predictive, and prescriptive analytics.
- Translate business goals into measurable outcomes.
- Communicate with data teams using clear problem statements.
- Turn analysis into action through storytelling and visual insights.
- Avoid common data interpretation and reporting pitfalls.
Who is the Target Audience?
- Product, Project, and Operations Managers.
- HR, Marketing, Sales, and Finance professionals.
- Consultants, Business Partners, and Team Leads.
- Anyone who wants to use data for smarter, faster decision-making.
Basic Knowledge:
- Ideal for professionals who rely on data but don’t have a formal analytics background.
Curriculum
Total Duration: 2 Hours
Kickoff: Why Analytics Matters
- From intuition to insight-driven decision-making
- Real-world examples of data transforming business outcomes
- What analytics is (and isn’t) - no coding required
Understanding the Analytics Spectrum
- Descriptive: What happened?
- Diagnostic: Why did it happen?
- Predictive: What’s likely to happen next?
- Prescriptive: What should we do about it?
Business Questions that Drive Analytics
- Framing a good question: measurable, specific, relevant
- The difference between correlation and causation
- Turning vague goals into data-ready problems
Metrics, KPIs & Dashboards
- Identifying the right KPIs for your role
- Leading vs. lagging indicators
- Reading and interpreting charts, graphs, and dashboards
- How to spot misleading visualizations
Making Sense of Data
- Mean, median, variance - without the math overload
- Trends, seasonality, and anomalies
- Understanding percentages, growth rates, and comparisons
Data-Driven Storytelling
- The “data → insight → action” flow
- Framing insights in plain language
- Using visuals to support decisions (not overwhelm)
- Tailoring messages to executives vs. peers
Working with Analysts & Data Teams
- How to write a clear data request
- What to expect from analysis deliverables
- Collaborating on metrics, dashboards, and hypotheses
- Avoiding the “analysis paralysis” trap
Common Pitfalls in Analytics
- Data without context
- Cherry-picking or confirmation bias
- Over-relying on vanity metrics
- Ignoring external or qualitative factors
Tools Overview (No-Code Focus)
- Common business analytics tools: Excel, Google Sheets, Power BI, Tableau, Looker
- AI-assisted analytics (e.g., ChatGPT, Power BI Copilot, Google Duet)
- Quick wins with natural language queries
Applying Analytics to Your Work
- Use cases by function (Marketing, HR, Ops, Product, Finance)
- Turning recurring reports into insight-driven reviews
- How to build an action plan from analytics findings