Working with Open Data means doing more than just looking at and making some guesses about what it means. Making Open Data into something educational, informative, or revealing requires a lot of different skills, and in many cases, a whole team of people.

Skills and Tools for Open Data

Collecting and Understanding Data: If you’re working with Open Data, then you’re probably not finding your own data. Regardless, a lot of labour can go into collecting data. In some cases, cities develop apps to help collect data, offer consultation, update databases, or install sensors to collect things like traffic data.

Cleaning and Processing Data: Open Data might require significant structural changes to be used in a project. Data cleaning makes the data analagous to similar sets (like those based on a standard, see above), so that scripts that make correlations between relations, or process them to place them onto maps can operate smoothly.

Exploring and Analysing Data: Finding insights in Open Data relies on an understanding of statistics, as well as the tools used for manipulating them. Beyond spreadsheeting tools there are software and programming languages for statistical analysis.

Visualizing and Presenting Data:

  • Developing Web Sites: Open Data isn’t worth much if it isn’t used by the public, and this might mean publishing findings or reports. Web resources like hosting, version control, as well as skills using both front-end tools like javascript, html/css, as well as integrating content management systems are essential to keeping analysis of Open Data open as well.
  • Mapping: Sometimes the goal of an  OD project is to collect information and to make it available to the public. Whether you are actively collecting information from different places and bringing it together, or spatially correlating different datasets, maps can be powerful and easy to use tools. Geographic Information Systems (GIS) traditionally have been standalone programs people install on the computer, but now a lot of them are moving to the web as online services that people can use.
  • Application Development: Sometimes OD projects leads to applications, like when you want to improve an existing service. A lot of applications now run on the web and so the same skills mentioned above apply; in addition, people make applications that work on mobile devices like phones and tablets. Skills including programming and working with databases are part of this work.

Developing Data Literacy

Like many skills, data literacy isn’t something one simply has, there are a huge amount of practises, methods, and tools for working with Open Data. Being able to find yourself in the right context to develop and use these skills, as well as being open to learning, is half the battle. To be data literate doesn’t mean all citizens need to become data scientists; there are many ways of working with data that do not require those highly specialized technical capabilities. According to the School of Data, citizens can be considered data literate if they can:

  • Find and access data
  • Understand that data can be used for different ends and think critically about those ends
  • Formulate questions about data, or that data can be used to answer
  • Use tools to process and summarize data (e.g., through visualization)
  • Advance goals by making arguments with data
  • Perform basic statistical analysis on available data
  • Feel comfortable using data

Closely tied to the concept of literacy is that of Technological Fluency. To be a fluent user of technology, one must understand what mechanisms go into the completion of a technological task: not just what a given technology does, but also some of how it does that thing. Carl DiSalvo uses the example of a search engine; a technologically literate user can effectively use a search engine, but a technologically fluent user also understands that pages are indexed and returned according to algorithms, and may have understanding of how this is done (Disalvo, 2009). Fluency with technology also requires the ability to think about technologically critically– meaning that its effects on society must be questioned, rather than assumed to be progressive, or historically necessary. These ideas make a lot of sense in the abstract– but it can be hard to understand how it applies in everyday activities, and harder to know how to go about beginning to develop literacy or fluency.


The term Digital Literacy broadly encapsulates the knowledge, skills, and understanding required to effectively use digital information and communications tools. Data, or Information Literacy is the translation of that concept into the domain of finding, evaluating, using, and managing data or information.