Identification of Empirically Grounded Social Science Insights

Identification of Empirically Grounded Social Science Insights which can Contribute to Solution of Big Data Problems

Project Team: Ellen Balka (Communications, SFU), Peter Chow-White (Communications, SFU), Sylvia Roberts (Library Liaison), Frederick LeSage (Communications, SFU), Veronique Sioufi (Communication, SFU).

Considerable discussion about big data focuses on topics such as development of data mining algorithms, computer security and the need for highly qualified personnel in technical areas. But, experience has shown that many projects are slowed down by a failure to address social issues (such as organizational capacities to manage data, unclear policies about data ownership, issues of trust in data, inconsistencies in data definitions which undermine data quality), and a plethora of other issues of a social nature. Many of these issues have been identified and addressed in empirically grounded social science literature, infrequently read by computational and natural scientists. The team will conduct a systematic meta-narrative review of social science literature in order identify insights which can contribute to the solution of big data problems; and conduct interviews and focus groups with SFU big data scientists to identify problems which social scientists can help mitigate. Improving the value proposition of big data projects can bridge the disciplinary gaps between the social sciences and technical stakeholders.