Learning about data

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What do we mean by data?

Data are collected everywhere – from mobile GPS and accelerometers to traffic movements and AI. When working with data, there are lots of different aspects to consider: how these data have been collected, who has controls over them, as well as issues of privacy, security and agency.

You have probably heard about lots of different terms related to data – big data, open data, or just ‘data’. What are they? What are they for? Where are they from? How are they used, and by whom?

  • ‘Big data’ are extremely large data sets that reveal all kinds of different patterns. The sheer amount of data is usually much, much more than a household computer can cope with in one go. As such, it is often multinational companies with the infrastructure who end up collating and analysing this data. This is why licensing and access is so important.

  • ‘Open data’ are data that can be freely used, re-used and redistributed by anyone, though there are different levels of access and attribution required.

  • Data also includes the smaller, local level data sets that large and small organisations collect every day, such as contact details.

Why do organisations collect data?

Most third-sector and charity organisations collect data of some sort. For example, an organisation that supports people with health and wellbeing issues will probably track how people are referred into the service, a range of quantitative and qualitative datasets that arise from an initial client assessment, how each person progresses through the service, and what the outcomes are.

These data sets may or may not be easy to compare across different teams, platforms and formats. This can be because people collect slightly different data that are difficult to compare directly, or because file formats cannot easily be merged.

Fears, hopes and opportunities

In order to work with data, teams and organisations need to have a shared vision of exactly what data they are talking about and how they would like to use them. Different members of staff might also have particular fears and hopes in relation to working with data. It is useful to discuss these in detail before a team embarks upon an organisational data project.

Find out more: Data icebreaker exercise

To get in the mindset of thinking about and analysing data, try this ‘analogue spreadsheet’ exercise put together by one of the ART/DATA/HEALTH project’s advisers, Catherine D’Ignazio. This is an introductory activity for groups and it is perfect for non-technical newcomers to familiarise themselves with basic concepts like ‘data’, ‘datasets’, ‘data types’ and ‘cleaning data’.

Try this: Suggested team discussion points
  • What does this organisation understand by ‘data’? Does everyone share an understanding of what data means for your organisation/team?
  • What are people’s hopes for working with data? What does your team/ organisation want to achieve or change?
  • What does your team fear most when it comes to working with data? How will these fears affect the organisation’s work?
  • What are the opportunities within the organisation’s wider sector for using data analysis to work differently?
  • Look at your existing data sets being: Are they complete enough? What data are missing? Or are there so many that it is hard to filter out the most important pieces of information?
  • Does the team have the right skills and resources in-house to collect and analyse its data? If yes, who is best placed to do so? If no, what training or external support is needed?

See also:

Health and Wellbeing Data: What forms do health and wellbeing data take?

Data for Advocacy: How can we use data to advocate for social change?

Open Data: Why do we need open data?

Data Art: How can data art help improve health and wellbeing?

COVID19 Data: What can data tell us about the pandemic?


Other Resources: Download the ART/DATA/HEALTH Toolkit