Chapter 10: Data Analysis, Interpretation and Presentation

Chapter Introduction | Web Resources | In-Depth Activity Comments | Teaching Materials

There are many sites about data at scale or big data as it is more often called. describes some of the ways data at scale is used in business and stresses the importance of analytics for seeing and predicting how customers behave and what they want. also describes some uses of big data including examples from sports and under the heading of “industry solutions” offers a wide range of examples from banking to education to media, to markets, and more. For an example of how big data analytics was used to identify sources of corrugated iron roofing to help earthquake victims in Nepal watch the video Microsoft’s data science edX describes many big data projects iand also how to collect and analyze big data See also examples of use of Google Analytics discussed in Chapter 16 and the web resources for chapters 8 and 9 on data collection and analysis. For those interested in nature, and specifically birds you can go to and explore the data visualizations for hundreds of bird species. You can also download data and data analyis that data by clicking on

An overview of big data sensing is in which data from networks of sensors is collecting and analyzing is discussed in this paper entitled “Emergent Technologies in Big Data Sensing: A Survey” published by Sage Publications: The US Geological Survey site describes remote sensing and how it used to track clouds, forest fires and map the ocean floor, among other things:

The Electronic Frontier Foundation describes how facial recognition works and how it can be used. You may have experience using facial recognition on a cell phone or as you go through immigration and customs at some airports. This website discusses how facial recognition is also used in law enforcement and some of the problems and threats associated with facial recognition. The use of drones and other forms of surveillance are also discussed. This site by Norton Symantec discusses many of the same issues:

If you have Netflix you many want to watch the documentary, “The Great Hack” about the role of Facebook and Cambridge Analytica in shaping people’s political views. There is also a TED talk by reporter Carole Cadwalldr entitled “Facebook’s role in BREXIT and the threat to democracy” And a recent interview with Cadwalldr entitled; “It’s not about privacy – it’s about power” As you watch the videos try to relate what you hear to the discussion in chapter 10, and if you or someone you know uses Facebook think about whether you are concerned about privacy, and if so, what precautions could be taken.

Data visualization has been an important topic in UX design and HCI for many years and there is a wealth of information on the web and many conference publications devoted to this topic. These include examples of commercial tools for big data analysis and display, such as:, This article by Jessica Hullman, Assistant Professor at Northwestern University describes “What is visualization research? What should it be? For recent research in data visualization see papers from the IEEE 2019Vis Conference and scroll down to check out previous conferences.

As we discuss in the ID book there are major concerns about ethical issues associated with the use of data at scale. Some of these are discussed in the links mentioned above. For more discussion of data and ethics see this paper published by the European Social and Economic Committee – EESC – published in 2016 entitled “Study on the ethics of Big Data: Balancing economic benefits and ethical questions of Big Data in an EU policy context” which raises many important issues. Euro Scientist also describes ethical issues associated with rapidly growing big data and so does this article from IBM

“Practical Approaches to Big Data Privacy Over Time” from the Berkman Klein Center for Internet and Society also provides links to other sources on this topic:

HCI is beginning to make contributions to the ethics of big data and AI. The approaches are human-centred. See the following university centres that are concerned with informing and developing human-centered AI and big data technologies.

Also there is an in-depth interview with Peter Bull (2018) on the importance of developing human-centred data science:

Links provided in the book: