BIG DATA AND PUBLIC POLICY Patrick Dunleavy and Michael Jensen

Huge volume of data, terrabytes or petabytes Exhaustive in scope, N = All cases High velocity, frequent changes, created in or near real time Highly variable, unstructured, temporally and spatially referenced Resolution fine- grained, very detailed, responds to questioning Uniquely indexical, in multiple ways Relational in nature, shares fields with other datasets Flexible, so can add new fields easily Scalable, can expand rapidly in size ‘Big data’ defined – one rough and ready approach Source: Rob Kitchen (2014)

Main sources of ‘big data’ advances in government Analysis access to administrative data in government systems (as opposed to transactions/business access) Analysis (anonymized) access to administrative data in private corporations’ online systems Easy external database accessing via Automated Programming Interfaces – often free, or low cost Massive textual digitization, online or accessible, and new analytics – via APIs or other recovery Analysis of online audio, image, video and other data (accessed/trawled by automatic systems, as in intelligence agencies)

Survey-based dataUnobtrusive & big data CoverageSmall, statistically representative sample Whole population MethodsAsk (a few) questionsObserve actual behaviours Interaction with subjects Reactive – subjects may tailor their responses Non-reactive – observed behaviours Key limitsSmall N sample, so analysis is necessarily shallow Large N – so analysis can go very deep down, to small sub-sets CostsVery substantialLow – data often already available TimingEpisodic with long lagsReal time or short delays Meaning problems Responses may be artefacts of Qs asked ‘Revealed preferences’ – because behaviours have costs & benefits Interpretation problems Often must infer views and behaviours from scant data Checking for coerced exchanges and available options Technical problems with datasets Non-response. Questions pre-set so data is inflexible Managing very large datasets COMPARING SURVEY & BIG DATA

Online randomized control (RCT) trials and Behavioural Public Policy (BPP) Almost all current experimental data for public policy is tiny, and scale used is too small to see if Nudge techniques work or not Online RCTs can work at massive scale, and in real time – e.g. alternative tax forms, or alerts to pay fines Hal Varian, ‘New tricks for econometricians’ – focus on stimulus & ‘control’, not on causation Behavioural insight sciences (b.economics, b.psychology, nudge theory) provide strong theories to mine

egalitarian (‘hippy’) anti-statism libertarian anti-statism pluralist mainstream public policy & civil service mainstream IT industry thinking and hype ‘dark side’, surveillance state liberal, on the left conservative, on the right politically neutral centrist anti- bureaucratic, populist corporate, hierarchist Six main forms of thinking about ICTs and the state conventional liberal democracy little activity little activity little activity

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