GMAT Focus: Data Insights

GMAT Focus Edition’s Data Insights section is designed to measure how well you can make decisions with imperfect, compressed, and sometimes messy information. In 45 minutes, you are asked to interpret charts and tables, synthesize details from multiple sources, and decide what can be concluded from partial data. The section is not about advanced mathematics. It is about disciplined reasoning under time pressure.

Data Insights includes several question types that feel familiar if you have worked in business, analytics, consulting, or operations. What makes it challenging is the pace and the precision required. Small reading mistakes, untested assumptions, and weak organization quickly turn into wrong answers.

What Data Insights Actually Tests

At its core, Data Insights tests three abilities:

1. Interpretation: extracting meaning from a table, chart, or short scenario without overreading it.
2. Logical sufficiency: deciding whether the information you have is enough to answer a question, even if you cannot compute a final numeric value.
3. Integration: combining details across sources while keeping track of constraints, definitions, and units.

You are being evaluated on how you think, not just what you calculate. Many questions reward restraint. The best test-takers learn to stop the moment they have enough information, rather than chasing a full solution out of habit.

The Question Types You’ll See

Data Insights typically draws from four major task families: data sufficiency, multi-source reasoning, graphics interpretation, and table analysis. Each requires a slightly different workflow, but the same discipline applies throughout: define what is being asked, identify what information is relevant, and verify conclusions before selecting an answer.

Data Sufficiency: The Signature Challenge

Data sufficiency is uniquely difficult because it asks a different kind of question. Instead of “What is the value?” it asks “Do you have enough to determine the value?” Two statements are provided, and you must evaluate whether each statement alone is sufficient, and whether both together are sufficient.

This format tempts people into doing too much math. Often, the fastest path is to reason about what would make an answer determinate.

A practical way to think about sufficiency

Before touching the statements, write a clean target:

• “Need: determine $x$”
• “Need: determine whether $x>0$”
• “Need: determine the percentage change”

Then test sufficiency by looking for uniqueness. If multiple values still fit the information, the statement is insufficient.

For example, if the question asks for a specific value of $x$, and a statement only gives a relationship like $x+y=10$, you immediately know it cannot be sufficient by itself because infinitely many pairs satisfy it. If the question instead asks whether $x+y=10$, that same statement could be sufficient. The target matters.

Common traps in data sufficiency

• Assuming properties not stated: “integer,” “positive,” “nonzero,” and “distinct” are common missing assumptions.
• Confusing “can compute” with “can determine”: you might be able to calculate many possibilities, but not a single outcome.
• Testing only one example: sufficiency requires ruling out all alternate possibilities, not finding one that works.

A reliable habit is to test for at least two distinct cases that satisfy the statement but lead to different answers. If you can produce two valid cases quickly, the statement is insufficient.

Multi-Source Reasoning: Synthesis Under Constraints

Multi-source reasoning presents information split across tabs, panels, or short documents. The challenge is not computation but organization. You need to locate relevant details, reconcile definitions, and avoid mixing conditions from different contexts.

Strong performance here depends on how you read:

• Scan all sources first to learn what each contains.
• Identify which source defines key terms, categories, or time periods.
• Return to the question and pull only what is needed.

Practical workflow

1. Map the sources: “Tab A is definitions, Tab B is performance metrics, Tab C is exceptions.”
2. Anchor on the question: what variable is being asked about, and for what time frame or segment?
3. Cross-check units and scope: monthly vs quarterly, gross vs net, region vs global totals.

Errors often come from subtle mismatches. A table may show percentages, while a narrative uses counts. A policy may apply only after a certain date. Multi-source reasoning rewards test-takers who slow down for ten seconds to confirm scope.

Graphics Interpretation: Seeing What the Chart Really Says

Graphics interpretation asks you to draw conclusions from charts such as bar graphs, line graphs, scatterplots, and stacked displays. The math is usually light. The real difficulty is reading exactly what the chart encodes.

What to check before answering

• Axes labels and units: dollars vs thousands of dollars, percent vs percentage points.
• Scale and baseline: a truncated axis can exaggerate changes visually.
• Legends and categories: confirm which color or marker corresponds to which group.
• Time direction: left-to-right is typical, but not guaranteed.

A common trap is treating visual impressions as facts. If the question depends on “largest change” or “highest rate,” you often need to compare values carefully, not rely on shape.

Table Analysis: Fast Accuracy With Dense Data

Table analysis questions present a table with multiple variables, where you may sort or filter to answer. The challenge is efficiently isolating the right rows and columns without losing track of conditions.

Skills that matter most

• Filtering logically: apply constraints one at a time to avoid excluding the wrong entries.
• Tracking multiple conditions: for example, “Region = West, Year = 2024, Margin > 10%.”
• Reading definitions: tables often include computed fields like “growth rate” that may be defined in a specific way.

When tables are dense, speed comes from structure. Decide what you need first, then narrow. Avoid scanning the entire table repeatedly.

Managing the 45-Minute Clock

Time pressure in Data Insights is real because each problem can include multiple moving parts. The goal is not to rush, but to use predictable processes so you do not waste time reorienting.

Pacing principles that work

• Start with clarity: spend a few seconds confirming what is being asked. This prevents minutes of wrong-direction work.
• Stop early when sufficient: especially in data sufficiency, stop as soon as you know whether information is enough.
• Cut losses: if you are stuck in complex arithmetic, look for a reasoning shortcut or move on.

In many Data Insights problems, the highest-value move is recognizing that the question can be answered without full computation. For example, if comparing two quantities, you may only need to compare their components or determine which must be larger given constraints.

Practical Preparation: What to Practice and How

Preparation should mirror the skills the section actually demands.

Build a repeatable data sufficiency method

Practice writing a “need” statement before looking at the information. Train yourself to test sufficiency with counterexamples, not hope. Over time, you get faster at spotting when a statement cannot possibly pin down an answer.

Improve chart and table discipline

Do timed sets where your only goal is perfect reading accuracy:

• call out units
• check baselines
• verify what each column represents
• note whether figures are totals, averages, or rates

These habits feel slow at first, but they prevent the most expensive mistakes.

Strengthen multi-source organization

When working with multiple panels, practice summarizing each source in a single line before answering. You are training the same skill you use in real work: knowing where to look and what each document is for.

What High Scorers Do Differently

High scorers treat Data Insights as a reasoning section, not a calculation section. They stay skeptical of assumptions, keep definitions and scope front and center, and use structure to manage time.

If you want one guiding principle for this part of GMAT Focus, it is this: be precise about what you know, and equally precise about what you do not. In Data Insights, that precision is the difference between guessing and deciding.