Big Data creates enormous opportunities for better understanding the impacts and effectiveness of economic and public policy, such as social investment. But our newfound capacity for collecting data and targeting intervention creates risks as well as opportunities. How can we get the best out of big data without enabling the worst, whether in government, business or wider society? Has the big data waka already sailed, or are there still opportunities to shape governance around the use of data?
The Policy Observatory hosted a panel discussion on big data in August 2017. Chaired by business journalist Rod Oram, the panel featured:
—Professor Rhema Vaithianathan of the Centre for Social Data Analytics at Auckland University of Technology, which is pioneering research into predictive analytics;
—Professor Tahu Kukutai of the National Institute of Demographic and Economic Analysis, who recently co-edited the first ever book on indigenous data sovereignty;
—David Leach, CEO of Qrious, a data analytics firm backed by Spark; and
—Keith Ng, an independent data journalist and data visualisations expert.
Video from the event is below, which includes the full session, the introductory comments from each speaker, and some extracts from the event.
The full session, including audience members voicing the issues on their minds (1 hour 27mins)
Rhema Vaithianathan describes her research project in California that uses predictive analytics to help frontline child-protection workers make better decisions on which calls to follow up on that report child maltreatment. (6 minutes)
Opening remarks by Tahu Kukutai, National Institute of Demographic and Economic Analysis, University of Waikato. Tahu discusses the power of data and what big data means for indigenous peoples. She expresses concern that data-driven innovations that have massive potential to impact on lives – for good and for bad – are taking place without meaningful partnerships and participation. (7 mins 53)
Opening remarks by Keith Ng, data journalist, on the analogy between big data and big oil. Most people fit into the big data production chain as ingredients, or passive participants. But how do passive participants become active participants in the process, when the process is long and complicated? (5 mins 34)
Rod Oram on why we keep using the phrase ‘unleashing the potential of big data’. (45 sec)
Tahu Kukutai critiques the way big data and machine-driven learning is talked about as value-neutral ways of accessing the truth. (4 mins 19)
Rhema Vaithianathan on the Data Futures Partnership guidelines for trusted data use. (4 mins 7)
Keith Ng on over-estimating the private or individual risks of big data versus under-estimating the structural risks of big data. (2 mins 8)