Library Data
can we do with it?’ If big data offers a number of opportunities, then in order to make the most of those opportunities we need to understand how to analyse it.
Harvey Goldstein said that all professions need to engage with big data. He commented on GetStats, a campaign to ensure society has a better understanding of numbers. It is the campaign’s goal to ensure that data is not misused or misinterpreted. In particular, he highlighted the fact that if misused, big data has the potential to facilitate commercial or state control over citizens. In addition, he noted, that in order to help address misinterpretation, the media need to be trained to better understand how to interpret data, and to talk sensibly about these issues. Another key issue was ‘what should we measure?’ Emphasising the importance of theoretical grounding, Paul Boyle stated that we must engage with data in the right way and for the right reasons. Paul Woobey extended this point by stating that we should not always take data at face value: what you can infer is not necessarily obvious from the data itself.
Big data brings a whole range of ethical
issues, and issues of interpretation. There are genuine concerns about privacy and trust: for example ensuring that data is made anonymous. How can we demonstrate validity? Is it good data, or not? Additionally, how can we query this data? Very different analytical techniques
‘The internet has made more data available – anyone can use it, and it is even government policy to promote this’ Harvey Goldstein
are needed, said Paul Woobey. Paul Boyle extended this point by discussing the importance of developing interdisciplinary training for researchers. Such training would enable them, he remarked, to link biomedical, social, economic and environmental data, for example. Paul Woobey further commented that the ONS has joined together methodologists and IT people. Traditionally these groups wouldn’t talk to each other
ANALYSIS
but this blend is what is needed for big data interpretation.
Both the ESRC and British Academy are looking at ways to enhance quantitative skills training at undergraduate level. But as Farida Vis commented, we also need to ensure that qualitative data analysis is conducted in big data research, to give context to what is being explored. She saw negative implications in reducing people to numbers. This, in turn, led onto a further concern: the politics of data. At some point, human interpretations of data will create bias. A greater understanding of what you are looking at is critical in there being real value in big data analysis. All in all, are we creating more informed citizens? Does more information do us good? From the panel there was a sense that there are some huge benefits and opportunities for the social sciences in big data. But equally there is plenty of bad data, and issues around interpretation. Who controls the data? The challenges around skills, validity and trust will need to be addressed as more and more interpretation of such data is undertaken.
Mithu Lucraft is PR manager at SAGE Publications
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