Reading Data Without Going Blank: the Questions to Ask
You open a chart or a table and your mind goes empty. You stare. Nothing comes. This chapter fixes that. The problem is almost never that you are "bad at numbers" — it is that you have no routine for attacking data. This chapter gives you a fixed list of questions to ask every single time, so the blank screen turns into a short, calm conversation with the data.
Why your mind goes blank (and why it's not your fault)
Your working memory — the small mental space that holds what you are thinking about right now — can only juggle a few things at once. Cognitive scientist George Miller famously estimated it at around seven items; later work suggests it is closer to four. A chart throws dozens of things at you at once: numbers, labels, colours, axes, a title. Your working memory overloads, and overload feels like blankness.
The fix is not "be smarter". The fix is to look at one thing at a time, in a fixed order. A checklist does the remembering for you, so your brain is free to think. Pilots and surgeons use checklists for exactly this reason — not because they are dumb, but because checklists beat memory under pressure.
The six-question checklist
Ask these in order, out loud or on paper, every time you face a chart, table, or report. Do not skip ahead. Each question should produce one short sentence.
| # | Question | What you're really finding out |
|---|---|---|
| 1 | What am I looking at? | The units, the time period, the source. (Dollars or counts? This month or this year? Who made it?) |
| 2 | What's the big picture? | The overall level. Is the number big or small? Healthy or worrying? |
| 3 | What's changing? | The trend. Going up, down, or flat over time? |
| 4 | Compared to what? | The baseline or benchmark. Up versus last month? Better or worse than a target? |
| 5 | What's surprising? | The thing that breaks the pattern — a spike, a dip, an odd row. |
| 6 | What's missing? | What the data does NOT show but you'd want to know. |
Let me define the trickier words plainly before we use them.
- Units
- What each number measures — rupees, people, percent, orders. A "5" means nothing until you know it is 5 what.
- Trend
- The direction over time. Up, down, or flat.
- Baseline
- A starting point you measure against — last week, last year, or the day before a change.
- Benchmark
- An outside standard you compare to — a target, an industry average, a competitor.
- Base rate
- How common something normally is. "10 complaints" sounds bad — until you learn the base rate is 10,000 orders, so it is 0.1%.
The three layers: describe, compare, then explain
Beginners try to jump straight to "why did this happen?" and freeze, because why is the hardest layer. Climb the ladder in order instead. This mirrors how analysts actually think.
Layer 3 CAUSAL "Why? What caused it?" (hardest)
^
Layer 2 COMPARATIVE "Bigger/smaller than what?"
^
Layer 1 DESCRIPTIVE "What does it literally say?" (easiest)
- Descriptive
- Just read it back in plain words. "Tuesday had 120 orders." No opinion yet.
- Comparative
- Put two numbers side by side. "Tuesday's 120 is double Monday's 60."
- Causal
- Offer a possible reason — and label it as a guess. "Maybe the Tuesday discount drove it." A guess to test, never a fact.
Reading a chart, step by step
For any chart, do this slow walk before you let yourself "feel" anything about it:
- Read the title. It usually tells you the whole point in one line.
- Read the bottom axis (the X axis). Usually time or categories. "What is each bar/point?"
- Read the side axis (the Y axis). The units and the scale. Does it start at zero? (If not, small changes can look huge.)
- Find the highest and lowest points. Your eye now has anchors.
- Trace the shape left to right. Up, down, flat, bumpy? Say it in one sentence.
- Now run the six questions.
A tiny worked example
| Day | Orders |
|---|---|
| Mon | 60 |
| Tue | 62 |
| Wed | 58 |
| Thu | 61 |
| Fri | 120 |
- What am I looking at? Daily order counts (units = orders) for one week (time period), Mon–Fri.
- Big picture? Most days sit around 60 orders — a steady, modest level.
- What's changing? Mon–Thu is flat. Friday jumps.
- Compared to what? Friday's 120 is roughly double the ~60 baseline of the other days.
- What's surprising? The Friday spike — it breaks an otherwise flat week.
- What's missing? I can't see why Friday spiked (no column for promotions, holidays, or order size), and I have only one week, so I can't tell if Friday is always big.
See how the layers showed up? Steps 1–3 were descriptive, step 4 was comparative, and the "why" I deliberately held back as a causal question to investigate. That restraint is what separates careful thinking from jumping to conclusions.
Turn data into an opportunity, not just a fact
The whole point of reading data is to find something you can act on. The two most useful questions for spotting opportunities are baked into the checklist: "What's surprising?" and "What's missing?" A surprise is where the easy money or the easy fix usually hides — the Friday spike could be a pattern worth repeating. A gap tells you what to measure next. Every good insight ends with a sentence that starts "So we should…" or "So let's find out…".
Practice
- Checklist reps. Over the next three days, run the six questions on three different charts or tables (one per day). Keep them in a note. Speed and ease come from repetition — this is spaced practice, spreading reps over days, which research shows beats one long cramming session.
- Layer-labelling. Take any sentence you've said or read about data and label it Descriptive, Comparative, or Causal. If it's Causal, rewrite it as a guess: add "this might be because…".
- Base-rate catch. Find one scary-sounding number in the news ("X complaints!", "Y% rise!"). Ask: out of how many? Compared to when? Write the number you'd need to judge it fairly.
- The "so what" finisher. For one table you read this week, write a single action sentence beginning "So we should…". Turning a fact into a next step is the skill that makes data valuable.