The University of Sheffield Logo


Loading, please wait ...

Microsoft Excel Best Practices

Yu Liang Weng

Yu Liang Weng

30 September 2020 · 11 min read

About Excel

Microsoft Excel is a well-known spreadsheet developed by Microsoft for platforms like Windows, macOS, and various mobile operating systems. Excel has been the industry standards for spreadsheet since 1993 and commonly used for calculations, creating graphs and charts, and pivoting tables. Microsoft built Visual Basic for Application (also known as VBA, derived from the programming language Visual Basic) into Excel that allows you to write scripts that automates repeated tasks, this script often called a macro - a series of instructions. Excel is easy to use with graphics interface and useful documentations/hints, and the graphical representation of data and built-in mathematical formulas makes calculations straight-forward and simple. Additionly, Excel has substantial number of graphs and charts under the hood so that you can create some visualisations in minutes, providing you have the right data structure for chosen charts.

Note: All graphs/charts below are created using Excel 2016.

Best Practices

What steps could we take to make better data visualisations with excel?
No matter what kind of tools you want to use, there are some questions you need to ask yourself before creating visualisations.

Once we have answered each of the questions above, what specific actions could we take when making data visualisation in Excel?

Preparation of spreadsheets

To cook a dish you will allocate a substantial amount of time in the preparation of ingredients, whether you need to clean, rinse, slice, curing, freeze, decoat… Typically, the more time you spend in taking care of ingredients, the more flavour you get out of it. In making a visualisation you will also need to spend some time in preparation of the data. The table below listed some of common things you might want to take into account:

Data typesThere are over ten different data types available (Microsoft are expanding this to more than 100!) and Excel will interpret each type differently. Having a suitable data type for each column help to maintain the consistence of column values and help you to avoid unsuitable graphs.
Order of sheetsIt is common that you might have more than one spreadsheet in an Excel file, therefore, by ordering spreadsheets in a way that matches the order in your process could help you locate information more easily and will also be beneficial to your collarators.
Missing valuesMissing values could have a significant effect on your outputs, it typically occurs when a cell doesn't have a value (when it is required) or the value is represented by NA, NaN, Null etc. Generally, you can impute missing values by column average, median, most frequent value, and k-nearest neighbourhood (k-NN). However, missing value itself is a broad topic and there are different resolutions depending on the value's data type, the relationship between data rows, and potentially other hidden issues.
ClarityCheck that your spreadsheet is easy to read and understand, for example, don't use complicated or irrelevant column names.
Merge cellsAvoid merge cells if you can as this could discard some value and cause inconsistencies.
SortingExcel does not perform sorting for you when creating graphs/charts, so watch out for your column values if you need to present values in certain order.
MacrosConsidering using macro if you need to perform some repeated tasks.
Conditional formattingUse of conditional formatting could help you to identify trends and patterns within your spreadsheets by changing the visual appearance of cells.
Data validationData validation is a feature in Excel that validates users input for a cell. Use of data validation could help you avoid inconsistent data values.
ProtectionIf you're collarate with other people and do not wish them to change certain cells, you can add protection to the spreadsheet by lock cells up. If your data comes from an external database then prefer connection to DB to ensure integrity rather than copy data out of DB.

Select appropriate charts/graphs

You might have already noticed that Excel has a convenient built-in feature called Recommended Charts in the Insert section, as the name suggests it analyse and recommends charts to you based on the cells you have selected. If you want to make your own decision or wish to learn how to choose correct charts, check out the following resources:

Making visualisations

After you have made your mind of which chart to use, it doesn't necessarily mean that you are done and can walk away with this visualisation. There are some details you have to watch out:

Some examples

This section presents some (good and bad) example charts made from Microsoft Excel 2016. Click on images to enlarge. You can download data used for these examples from the Linkedin course Excel Data visualisation.

Bar/column chart

Column chart for mobile spend vs desktop spend


Left axis is removed and replaced by labels. Histogram 1 of height distribution of gym members

If your aim is to find the number of gym members with height in the range 180 - 185cm, highlight it should make some difference.

Histogram 2 of height distribution of gym members

Line chart

Line Chart for trade volumes of Apple Inc.

Scatter plot

Scatter Plot for USA states average income vs population



Heat maps

Heatmap 1 - cinema ticket sales

With numbers removed:

Heatmap 2 - cinema ticket sales

Pie chart

Labels or legends?

Pie chart 1

Pie chart 2

Geospatial maps

A 3D map tour created using Excel:

Geospatial maps - Power map

Going further

If you wish to explore more about Excel, it is worth to check the following resources:

If you think about moving from Excel to Python or R, get started on our Learning Path.

Edit this page on GitHub