Big Powerful New Data

Power and language are both crucial currents for innovation. Two alternative tools for macro analysis of an economy’s innovative capacity and output then suggest themselves. Firstly, a power-centric analysis of changes in the economy, from in physical and political senses. Secondly, linguistic analysis of mass printed and digital material produced in an economy, from standalone and comparative perspectives. These techniques can complement one another, given that shifts in power and language also interact. Power-centric analysis of technology is a technique introduced by Russell et al in “The Nature of Power: Synthesizing the History of Technology and Environmental History”. An example of linguistic analysis of the economy is the R-word index run by The Economist, where the frequency of the word recession is used as an indicator of recession.

In a power analysis of the economy, energy flows and transitions are modelled qualitatively or quantitatively. Using this lens, we may note that the rise of the Internet has accompanied surging electrical power needs in large relatively centralized datacentres, with cloud computing being the current extreme of this. At the same time it has disintermediated middlemen such as travel agents. The move of labour – and spending of biochemical energy – from a travel agent in an office to the consumer at home or on a smartphone in turn requires increased electricity requirements for mobile phone towers and households. Given this analysis we can get insight into Google’s investment and research into alternative energy and distributed generation technologies such as solar photovoltaics. We might also note that, globally, the Internet mostly runs on coal. Combining the physical energy analysis with political analysis, we can see where innovation actors are constrained by energy and whether shifts in power are dominated by local or foreign actors, be they wind power entrepeneurs or multinational oil companies.

A focus on physical power can yield quantitative metrics of joules and watts that are not available to more structural approaches such as the system of innovation model. It focuses on facts about the economy that are fairly readily available for most countries, and also in comparative form. Though power analysis does include the labour market and its use of biochemical energy, this focus on economic output may make analysis of innovation capacity relatively indirect. How much did the energy use of a US mathematician change over the twentieth century, except as a consumer of productivity tools, such as computers, available to all professionas? This is a technique pioneered by historians, and it may speak most clearly in retrospect, requiring extrapolations to deduce capacity which are more prone to subjective policy hobby horses.

The linguistic approaches strengths and weaknesses seem to complement power analysis. By focusing on words, it will tend to weight research and development activity more strongly, such as use of terms in journal articles or social media. One weakness of linguistic analysis is that mass corpuses of content must be available to do “big data” style analysis. A developing economy, particularly in the poorest parts of the world, may not produce enough readily available searchable content to discover meaningful shifts and opportunities. Relying on the linguistic approach too heavily in a poor developing country may skew policy too much to theoretical research and ignore useful innovations happening on the ground but not on Twitter.

The innovation systems approach may have a weakness that the initial categories of organization (university, R&D lab, etc) constrain future analysis, missing trends which cut across traditional organizations. In this way both power and linguistic analysis may show up perspectives that do not emerge as readily in the otherwise more comprehensive innovations systems approach, and thereby supplement it.

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