Mastering the GMAT Data Insights Section
- Goalisb
- Jun 20
- 6 min read
Mastering the GMAT Data Insights Section: Your Guide to Multi-Source Reasoning
As the business world becomes increasingly data-driven, the ability to interpret, analyze, and synthesize information is essential for success. Recognizing this need, GMAC has revamped the former Integrated Reasoning section on the GMAT exam into the Data Insights section. This section is designed to test critical data interpretation and decision-making skills that are vital for MBA candidates and future business leaders.
In this guide, we’ll cover what the Data Insights section is all about, why it matters, and take a deep dive into one of its most complex question types: Multi-Source Reasoning.
Why the GMAT Data Insights Section Matters
The Data Insights section assesses skills that today’s business environment demands:
Data Interpretation: In the workplace, information often comes from various sources and formats. Business professionals must know how to navigate these, identify key trends, and pull out the most relevant insights.
Multi-Source Integration: Data isn’t always clear-cut. To make informed decisions, leaders often need to evaluate multiple data types—graphs, tables, and written notes—simultaneously.
Analytical Decision-Making: In addition to simply analyzing data, leaders must make quick, informed decisions based on their findings. The Data Insights section simulates this process, requiring you to draw insights efficiently and accurately.
Together, these skills allow MBA candidates to handle complex, real-world business challenges, setting them up for success in data-driven environments.
Data Insights Question Types: An Overview
The Data Insights section includes four key question types:
Multi-Source Reasoning: Tests your ability to cross-reference information across multiple sources, such as tables, graphs, and text passages.
Table Analysis: Assesses your skills in sorting and analyzing data in a table format, often with conditions or filters.
Graphics Interpretation: Involves reading and interpreting various types of charts, such as line graphs, bar charts, and scatter plots.
Two-Part Analysis: Requires solving complex problems by answering two related questions based on the same information.
In this post, we’ll focus on Multi-Source Reasoning questions, which test your ability to gather, cross-reference, and interpret data from different tabs—skills you’ll regularly use in business when dealing with complex projects.
Multi-Source Reasoning: What It Is and How to Approach It
Multi-Source Reasoning questions are designed to simulate real-life business situations where you might have access to multiple pieces of data—financial statements, market research reports, customer feedback, etc.—all presented in separate sources. The goal is to interpret and integrate data from these sources to make an informed decision.
In the GMAT, Multi-Source Reasoning questions provide you with two or three tabs of information. These tabs often contain:
Tables or Graphs: Numeric or visual data requiring quick interpretation.
Text Descriptions: Information about processes, business contexts, or rules.
Additional Data Sources: Such as a second table or a graph, adding another layer of complexity.
You’ll need to switch between tabs to answer questions, making sure to cross-reference details accurately. This mirrors tasks in the business world, like evaluating multiple reports or aligning team objectives with financial data.
Example Multi-Source Reasoning Problem
Let’s walk through a sample problem to understand how to approach these questions.
Example Scenario: Imagine you’re a consultant analyzing the performance of different product lines for a client. You’re given three tabs of data:
Tab 1: Revenue Data (Table)
This tab contains quarterly revenue data for each product line over the past two years, displayed in thousands of dollars. You can sort the table by product line, quarter, or revenue amount.
Tab 2: Marketing Campaigns (Text)
This tab provides descriptions of recent marketing campaigns, each linked to specific product lines and timeframes. Each campaign includes the objectives, budget, and the product lines involved.
Tab 3: Customer Satisfaction Scores (Graph)
This tab contains a bar chart showing average customer satisfaction scores by product line, measured each quarter over the last two years.
detailed example with the actual Revenue Data (Table), Marketing Campaigns (Text), and Customer Satisfaction Scores (Graph). This will give readers the full context and a realistic practice experience:
Sample Multi-Source Reasoning Question with Tables
Imagine you’re given three tabs of data as follows:
Tab 1: Revenue Data (Table)
Quarter | Product Line | Revenue (in thousands) |
Q1 2023 | Alpha | 250 |
Q2 2023 | Alpha | 280 |
Q3 2023 | Alpha | 310 |
Q4 2023 | Alpha | 400 |
Q1 2023 | Beta | 150 |
Q2 2023 | Beta | 190 |
Q3 2023 | Beta | 210 |
Q4 2023 | Beta | 300 |
Q1 2023 | Gamma | 350 |
Q2 2023 | Gamma | 360 |
Q3 2023 | Gamma | 400 |
Q4 2023 | Gamma | 450 |
Tab 2: Marketing Campaigns (Text)
Campaign Alpha: Ran in Q2 and Q3 of 2023 for Product Line Alpha with a focus on expanding customer reach through digital advertising.
Campaign Beta: Ran in Q3 and Q4 of 2023 for Product Line Beta, aiming to improve brand loyalty through loyalty programs and special promotions.
Campaign Gamma: Ran in Q4 of 2023 for Product Line Gamma, focusing on boosting sales through holiday discounts and email marketing.
Tab 3: Customer Satisfaction Scores (Graph)
Quarter | Product Line | Customer Satisfaction Score (out of 100) |
Q1 2023 | Alpha | 78 |
Q2 2023 | Alpha | 80 |
Q3 2023 | Alpha | 85 |
Q4 2023 | Alpha | 82 |
Q1 2023 | Beta | 65 |
Q2 2023 | Beta | 70 |
Q3 2023 | Beta | 88 |
Q4 2023 | Beta | 90 |
Q1 2023 | Gamma | 76 |
Q2 2023 | Gamma | 80 |
Q3 2023 | Gamma | 85 |
Q4 2023 | Gamma | 89 |
Sample Question
Question: “For the product line with the highest average customer satisfaction score in the most recent quarter (Q4 2023), which marketing campaign generated the largest revenue increase?”
(A) Campaign Alpha
(B) Campaign Beta
(C) Campaign Gamma
(D) No campaign generated a significant revenue increase
Solution Explanation
Identify the Product Line with the Highest Customer Satisfaction Score in Q4 2023:
According to Tab 3, Product Line Beta has the highest customer satisfaction score in Q4 2023, with a score of 90.
Check Revenue Data for Product Line Beta:
In Tab 1, Product Line Beta’s revenue increased from 210 in Q3 2023 to 300 in Q4 2023, showing a significant revenue jump.
Cross-Reference with Marketing Campaigns:
Tab 2 shows that Campaign Beta was active for Product Line Beta during Q3 and Q4 of 2023, aligning with the period of increased revenue.
Correct Answer: (B) Campaign Beta
Sample Question:
“For the product line with the highest average customer satisfaction score in the most recent quarter, which marketing campaign generated the largest revenue increase?”
Options:
(A) Campaign Alpha
(B) Campaign Beta
(C) Campaign Gamma
(D) No campaign generated a significant revenue increase
Step-by-Step Solution
Identify the Product Line with the Highest Customer Satisfaction Score:
Go to Tab 3 (Customer Satisfaction Scores) and check the graph for the most recent quarter.
Identify the product line with the highest satisfaction score in that quarter.
Check Revenue Data for This Product Line:
Move to Tab 1 (Revenue Data) and filter for this product line.
Look for revenue increases over time, paying close attention to the latest quarters where the marketing campaigns were active.
Cross-Reference with Marketing Campaigns:
Finally, go to Tab 2 (Marketing Campaigns) and identify which campaigns were running during the period with the highest revenue increase.
Ensure that the campaign matches both the timeframe and the product line with the highest customer satisfaction.
Correct Answer: Let’s say it’s (B) Campaign Beta.
In this example, you’ve used all three data sources to arrive at an answer, demonstrating the critical cross-referencing skills necessary for Multi-Source Reasoning questions.
Top Strategies for Tackling Multi-Source Reasoning
Skim for Key Data: At the start, quickly identify the most relevant information in each tab without getting bogged down. Look for keywords or figures that relate to the question.
Efficient Navigation: These questions require flipping between tabs. Stay organized—some test-takers like to jot down quick notes or underline key points (if allowed), keeping track of what’s relevant as they move between tabs.
Ignore Irrelevant Information: GMAT questions often include distractors—details that are unnecessary or don’t directly impact the question. Be mindful of what you need, and ignore extraneous information to save time.
Practice Visualization: If you find it challenging to hold multiple data points in mind, try visualizing or sketching a quick layout. This can help you remember key points as you switch between tabs.
Final Thoughts on Multi-Source Reasoning
The GMAT’s Data Insights section, especially Multi-Source Reasoning, tests how well you handle complex information under time constraints. These are real-world skills that MBA programs—and future employers—will expect you to have. By mastering Multi-Source Reasoning questions, you’ll be better prepared for the demands of a business environment where data-driven decision-making is essential.
Looking for more practice? Keep an eye on this blog series for more GMAT Data Insights question types and sample problems, or connect with GOALisB for one on GMAT Prep.