Management and Leadership

Problem Solving & Data-Driven Decision Making for Analysts

• Traditional Classroom: 2-day
• Virtual Instructor-led: Four 3-hour sessions

This two-day interactive workshop is designed to help analysts and senior analysts move from a reactive reporting mindset to becoming proactive, strategic problem-solvers. Participants will learn a structured approach to problem-solving that integrates data-driven decision-making, with a strong emphasis on root cause analysis. Through hands-on activities and realistic applicable scenarios, attendees will practice identifying the right problems to solve, analyzing data effectively, and developing insightful recommendations that drive business impact.

Unlike technical Excel or analytics classes, this course focuses primarily on how to think critically about problems and how to connect analysis to meaningful business decisions. It equips participants with the tools and frameworks to transform data into actionable insights, communicate findings clearly, and influence decisions across the organization.

Target Audience

This course is ideal for:

• Various analysts who regularly work with data but want to increase their problem-solving and decision-making skills
• Senior analysts seeking to become more proactive advisors to leadership teams
• Teams looking to improve collaboration and consistency in addressing complex problems and other issues

Learning Objectives

• Apply a structured, step-by-step problem-solving approach to general, financial and operational challenges (with real-world discussions, scenarios, examples and activities).
• Distinguish between symptoms and root causes using practical tools such as 5 Whys and cause-and-effect diagrams.
• Use data effectively to generate insights that inform decision-making.
• Prioritize solutions using decision matrices and impact-effort tools.
• Communicate insights and recommendations clearly to stakeholders and leadership.
• Develop proactive habits to anticipate and address recurring business problems.
• Use the prioritized solutions to assist in making sound and effective data-driven decisions.

Course Outline

Module 1: Getting Started

• About this Course
• Administration and Logistics
• Workshop Objectives
• Action Plan

Module 2: The Problem-Solving Method

• What is a Problem?
• What is Creative Problem-Solving?
• What are the Steps in the Creative Solving Process?
  • Why Problem-Solving Matters (for Analyst)
  • Address Common Challenges for Analysts: Reactive Fire-Fighting, “Data Dumping,” Lack of Action Orientation
  • Address the Gap Between Being “Data Busy” Vs. “Data Smart”

Module 3: Information Gathering

• Understanding Types of Information
• Identifying Key Questions
• Methods of Gathering Information

Module 4: Problem Definition

• Defining the Problem
• Determining Where the Problem Originated
• Defining the Present State and the Desired State
• Stating and Restating the Problem
• Analyzing the Problem
• Root Cause Analysis
  • Role of data in verifying causes.
  • Writing the Problem Statement

Module 5: Preparing for Brainstorming

• Identifying and Removing Mental Blocks
• Stimulating Creativity

Module 6: Generating Solutions (I)

• Brainstorming Basics: Brainwriting and Mind Mapping
• Other Techniques for Brainstorming and Root Cause Analysis

Module 7: Generating Solutions (II)

• Overview of Popular Solution Generating Techniques
• Techniques Applicable to Financial and Other Analysts

Module 8: Analyzing Solutions

• Developing Criteria
• Analyzing Wants and Needs
• Using Cost/Benefit Analysis and other Financial Techniques

Module 9: Selecting a Solution

• Developing a Final Analysis/Paired Comparison Analysis
• Analyzing Potential Problems

Module 10: Planning Your Next Steps

• Introducing Data-Driven Decision-Making Using Analytics
• Applying Solutions to Data-Driven Decisions
  • Help Analysts Think Beyond Data Extraction Toward Insight Generation
  • Moving from “Data Retrieval” to “Data Storytelling”
  • Key Questions Before Pulling Data:
    • What Decision Will This Data Support?
    • What Assumptions Are We Testing?
  • Common Pitfalls: Correlation vs. Causation
  • Using Excel for Quick Pattern Recognition (Pivot Tables, Conditional Formatting)

Module 11: Recording Lessons Learned

• Planning the Follow-up Meeting
• Celebrate Successes
• Identifying Improvements

Module 12: Wrapping Up

• Supplemental Material and Handouts/Next Steps

MDV1017 Course Code