Southampton Open Data Blog

Good Data Practice

June 7, 2013
by Ash Smith

While building the University’s Open Data, we’ve seen many different types of data. Much of the information is exported from Oracle and MySQL databases, or from enterprise systems like Sharepoint, but the vast majority of what we use is in a tabular data format such as a spreadsheet.

Spreadsheets are actually a really good way of producing linked open data without any technical knowledge. A technical person just needs to write a single program or script that converts a spreadsheet into a computer-readable format, and anyone can then modify the spreadsheet to their heart’s content, you just need to run the script again afterwards. But this allows us to fall into a very common trap caused by bad spreadsheet discipline.

Spreadsheets are generally designed for human use. Most modern spreadsheet packages, such as Excel, allow the user to include headings, cell colours, lines, even import images and other files. There are also no strict rules about data type, so you can type a list of numbers in a column and then enter “N/A” or “see below” as part of the list, and the spreadsheet will not complain. This is fine for spreadsheets that only need to be read by people. However when generating information that might one day be read by computer, there is one very important 1975 Doctor Who quote you should remembered, “the trouble with computers is that they’re very sophisticated idiots”. They can only handle what they’re programmed to handle. So if I were to write a program that processes a spreadsheet for converting into linked open data, and then someone were to update a cell in the spreadsheet using the word ‘None’ rather than the number zero, the computer running my program will get confused and behave unexpectedly. This is why good data practice is essential when generating or updating data that may one day become linked open data.

So how can we avoid this? Well, one way is to employ super hackers who can pre-empt every possible anomaly in the data. But in a world with time and financial constraints this isn’t always an option! Joking aside, it’s a really quick and cheap fix to make sure that if you’re designing or editing a spreadsheet, you keep it as computer-friendly as possible. To this end, we’ve come up with what we consider to be the four most important rules for making your spreadsheet ‘linked-data-friendly’.

  1. Standardise your data format
    Values should be numerical or a simple yes/no as far as possible. For example, if you were producing a list of food, rather than put ‘not suitable for vegetarians’ in a general comment field, add an extra column labelled ‘vegetarian’ and restrict the possible values to ‘yes’ or ‘no’. If this isn’t possible, keep to a small set of possible values and don’t deviate from these. ‘Red’, ‘Yellow’ and ‘Green’ is better than ‘Red’, ‘Burgundy’, ‘Yellow’, ‘Lime’, ‘Emerald’ and ‘Jade’, unless the exact shade of green is critically important.
  2. Keep free text to a minimum
    There is always room for a comments column. Sometimes we need to express something that can’t be represented as mere numbers. However, try not to put this in the actual data. The data should be as accurate as possible, and clarified by the comment field. So, for example, if you are maintaining a list of water coolers and their locations, you might have a ‘room’ column. If a cooler is in a corridor rather than a room, there are several ways you can represent this in a spreadsheet. You could leave the room empty and put ‘outside 2065’ in the comments, you could put ‘outside 2065’ as the room number, or you can put the room ‘2065’ as the room number and then write ‘outside’ in the comments. The third way is the linked data way! We still have consistent, numerical data to represent the room, but the comment clarifies to a human reader that the cooler is actually outside the room rather than within it. The computer may not be able to make sense of the ‘outside’ comment, but at least it can get the closest room correct.
  3. Consistent, unambiguous identifiers
    Computer scientists often refer to ‘primary keys’, and information architects will talk about ‘controlled vocabularies’, but at the end of the day we’re all talking about the same thing and that’s a way of identifying a specific thing in an unambiguous way. A good example of this is buildings in the University estate. Some buildings have names, some more than one, but all buildings have a number, so if you have a ‘building’ column in your data, make sure and use the number rather than the name. The same applies for rooms. A computer doesn’t understand ‘level 4 coffee room’ (and indeed many buildings may have a level 4 coffee room) but it does understand ’32/4032′ (for example).
  4. Style is nothing to a computer
    Although you may like to use headers, coloured cells and so on, don’t rely on them for meaning. When you export a spreadsheet to its raw data form, all the styling is lost, so making the vegetarian options in a menu green is not a good way to identify them. If it’s important, it should have a column. By all means, make your spreadsheet as pretty as you like – just be aware that it’s not going to look like that to a computer.

There are other things, but these are the most important. Next time you start a spreadsheet keep to these rules, and your spreadsheet will be trivial to convert and add to the open data service. Once its in it is really easy for us to give you loads of value add on your data. The value add increases the desirability and accessibility of your data and makes your data helpful. People use your data to make their lives easier and that reflects positively on you and boosts your reputation.

Categories: Problems & Fixes.

2 thoughts on “Good Data Practice

  1. joost says:

    Maybe a fifth point: one field, one piece of content. For example, separate street name and house number into two different fields.

  2. Nick Woodhill says:

    A really interesting blog post with lots of good advice. I work for the Government Statistical Service and we’ve just released some good practice guidance on releasing statistics on spreadsheets:
    Plenty of similar themes.

Leave a Reply

Your email address will not be published. Required fields are marked *