7  Making a Workbook Someone Else Can Read

Module 1 was about getting Excel to compute the right answer. This chapter is about the other half of the job: handing the file to someone else. A workbook that only you can follow is not finished work, and the difference between the two is mostly decisions about layout and presentation rather than anything technical.

Learning Objectives

By the end of this chapter, you will be able to:

  • Decide how much polish a workbook needs, based on who will open it
  • Lay a workbook out so a reader can find their way around it
  • Use shading, borders and labels to make a table readable without making it harder to analyse
  • Recognize the trade-off between a workbook that looks good and one that is easy to compute with

7.1 Who Is Going to Open This?

A workbook is one Excel file; it holds one or more worksheets (“sheets” or “tabs”). A habit worth building early is deciding deliberately how to split work across sheets — and that starts with one question you should ask every time: who is the audience for this workbook? Three answers, each shaping how you build it:

  • Just you. A scratchpad — quick numbers for today’s decision, or exploratory work. You can get away with fewer sheets, terser labels, less formatting. But be honest: “just for me” files have a habit of becoming “I need to send this to my boss tomorrow” files, so if there is any chance it will be shared, build it as if it will be.
  • Internal colleagues. A file for a co-worker, your boss, or the TA. Now you want a README, clear labels, consistent structure, and enough formatting to navigate without asking you questions. Aim for “clean and self-explanatory,” not “pretty.”
  • External audiences. A file for clients, regulators, or the public. Here aesthetics genuinely matter — consistent formatting, polished tables, charts that stand on their own, often with the working sheets hidden. Every element reflects on you or your organization.

There is no single “right” level of polish — it depends on who opens the file. The mistake is not picking the wrong level; it is not thinking about it at all. Match the polish to the audience.

A complex workbook should open with a README or contents sheet. This explains what the workbook is, who made it and when, where the inputs came from, and what each of the other sheets contains. It can also hold links to those sheets, so a reader can jump straight to what they need.

If you are doing a lot of data cleaning, it is also good practice to store raw data in a separate sheet from the cleaned data. This way you can always come back to the raw data and see what is the result of your work versus the raw data.

Finally, it is best to do analysis and charts in separate sheets, rather than having one sheet with 32 charts and 16 summary statistics. Of course, you don’t need to have a separate worksheet where you calculate the mean, and another where you calculate the median, and another where you calculate the standard deviation. But generally, you don’t want too much analysis on the same sheet. So for example, you might have one sheet for summary statistics and another for hypothesis tests.

The principle is separation of concerns: raw data, cleaning, analysis, and presentation each have their own place.

7.2 Aesthetics

In addition to calculations, Excel allows you to shade cells, put borders around tables, add text that describes the data, and insert images. These can all create a much nicer aesthetic than a wall of numbers. However, they can also make subsequent analysis of the data frustrating. So how many of these flourishes you add depends on your audience.

One audience might simply want to view the data – in which case you should format it so that they can see the dataset as easily as possible – use borders around tables, use bold text for totals, etc. An example of this is the data showing the capacity of grain elevators in Manitoba. The aesthetic is pretty minimal, but the bolding and borders allow the user to quickly understand the dataset.

Download this workbook · Open online (File → Save a Copy to view formulas and edit)

Source: Manitoba Agriculture, Foresight and Analysis — Markets and Statistics.

Another audience might want to do specific calculations with the data. An example of this is the Saskatchewan Crop Planning Guide, which allows users to input their own costs, expected yields, and crop prices to calculate the profitability of different crops. It is a fairly complex workbook, with a sheet for every major crop in the province. However, it is pretty user-friendly and easy to navigate: it opens with a disclaimer, lists its assumptions on their own sheet, and uses an identical layout on every crop sheet, so once you can read one you can read them all.

Download this workbook · Open online (File → Save a Copy to view formulas and edit)

Source: Saskatchewan Ministry of Agriculture — Crop Planning Guide and Crop Planner (2026 edition).

Yet another audience might want to do more detailed analysis of the data, and would prefer it in a more raw form. An example is the yield data from the Saskatchewan government (which we will work with more throughout this book), which gives yields of major crops in each of Saskatchewan’s rural municipalities. This data has no adornments — one header row, one row per observation, starting in cell A1 — which is exactly what an audience that wants to run its own analysis needs.

Download this workbook · Open online (File → Save a Copy to view formulas and edit)

Source: Government of Saskatchewan — Saskatchewan’s Dashboard: RM Yields. Extract filtered to 2024–2025.

7.3 Sympathy for Your Audience

Every piece of analysis has a reader. Before you write a single sentence, ask yourself:

  • Who is this reader? Their job, their technical background, what they care about.
  • What do they already know? Don’t explain things they know. Do explain things they don’t.
  • What decision are they trying to make? Every report exists to inform some decision. Find it.
  • What will they do with the report? Read it front to back? Skim the executive summary? Search for a specific number?
  • How much time will they spend? Be ruthlessly respectful of it.

Good writers of technical reports develop what I call sympathy for the reader — the ability to imagine what it’s like to read the report with fresh eyes. It is hard, because you have been immersed in the problem for days and they have not. The more effort you put into imagining their experience, the better your report will be.

A practical tip: after you finish a draft, put it away for a day and come back to it. You will read it with fresher eyes and spot places where you assumed too much context.

7.4 Formatting Excel Workbooks for Handoff

Sometimes your deliverable is an Excel workbook. Here are habits that will make the difference between a workbook your colleague can use and one they’ll quietly throw away:

  • Start with a README sheet. What is this file? Who made it? When? What does each sheet contain? How often is it updated?
  • Separate raw, cleaned, and analyzed data. As discussed in Module 1. Lock the raw data sheet if you can, so nobody accidentally edits it.
  • Use named cells for assumptions. Every assumption in your model (discount rate, growth rate, cost per unit) should live in a labelled cell that is easy to find and change.
  • Label everything. Every column, every chart, every table header. “The reader should never have to ask ‘what is this?’”
  • Use conditional formatting sparingly. It can be powerful, but it can also make a workbook look like a Christmas tree. Use it to highlight things that need attention, not to decorate.
  • Freeze panes. If you have a large table, freeze the header row so it stays visible when you scroll.
  • Document your formulas. At the top of an analysis column, write a cell explaining what the formula computes.
  • Include the date you last updated it. In the README.