Big data: Missing the situatedness of corporate environmental data


Corporate environmental data abounds. But are the big data analytics of corporate environmental information fit to meet the promises of evidence-based environmental governance through markets or deliberative democracy? A core premise of such governance is that the subjects and parties of governance – citizens and consumers – have symmetrical access to information and are able to make sense of them. I empirically ‘tested’ this premise drawing on an ethnography of environmental accounting in one of the globally 50 biggest companies, a Fortune 50 player. The present article retraces this ‘test’, approachings corporate data practices by attending to the ‘control zones’ of carbon accounting, specifically focusing on the words and material action with which data is handled and managed.

My argument builds on the analysis of enactment of environments. Following this approach, corporate environmental conduct is enacted through environmental information practices. These practices shape the environmental realities that matter for the company as well as for external control agents. A range of entities take part in configuring control zones, shaping data practices: accountants and managers, algorithms and databases as well as auditing organisations. The control zones I consider extend from the sourcing of data in a subsidiary via the processing of data between subsidiary and headquarters, the reviewing of data at the headquarters, the control of the company’s data practices through audit guidelines and the release of data to a ranking.

Now, let us enter the company.

Several instruments were supposed to standardise how environmental data sourced at the subsidiary level results in carbon emissions. Yet, these instruments were not able to determine accounting practice. Uncertainties involved data entry agents and included the design of data entry interfaces as well as the (non-)fit and dynamics of carbon conversion factors.

This is a key finding: Within the company headquarters’ control zone, sociotechnical configurations of the data flow–from subsidiary into the central database–incentivised enacting data that did not raise questions. Data handling in data entry, review and processing was controlled by several routines that shaped data to be alike earlier reported data. Thus, corporate environmental data production was not producing normally distributed errors, but it was shaped to straighten data out.

A zone of external control is supposed to ensure good data practices within companies: audit, rankings and indices promise transparency. My analysis shows that the auditors were not demanding perfect data but were satisfied with assurable presentations of data. I register a shift from control over data practices to control over the staging of data for particular audiences. A ranking organisation foundationally trusted the data they received from companies, not calling the companies to account for their data. Control was substituted by trust. Both forms of supposedly external control, by the auditors and by the ranking organisation, are decoupled from uncertainties, contingencies and dynamics of data practices on the ground.

For my argument, these empirical findings matter in two ways. First, they detail the enactment of corporate greenness. Second, they provide the ground for explicating the particular politics of these enactments. The configuration of enacting environments was choreographed by the company; it was set up to settle environmental accounts, rather than to unsettle them. Whilst environmental realities were processed in the companies’ informational governance, these environments were shaped not to disturb, or trouble the corporate flow.

This analysis disillusions Mol’s ‘informational governance’ of corporate ‘environmental reform’. Facing this result, enthusiasts of automatisation might simply call for eliminating the humans – their agency in decision-making – thereby promising the prospect of proper data practices. Against such promise, I consider two points. First, imagining data analytics ‘without humans’ simply misses the normativities and biases inscribed in software and hardware. Second humans matter also in configuring datasets. When I discussed the conclusions of my study with a colleague, we considered prospects of automatising environmental data. She offered the following contrast: From the headquarters’ perspective manual data practices are sources of error, whereas from the subsidiary perspective manual data practices add quality. Thus, automatising accounting shifts control zones, with more algorithmic agency to control some data while less in control over how data actually relate to situated environmental concerns.

These findings have implications for two key discussions of big data. First, as boyd and Crawford suggest, big data needs to consider how context can be retained. In contrast, my analysis suggests that the doings of data–environment relations are not fully specifiable but inextricably bound to the situation. Metadata and other attempts to translate context into a machine-calculable state lose the situatedness and uncertainties of translating environmental relations into data. Second, the promise of re-useability of corporate environmental data faces the problem that data is dynamic. My study shows that the relation between data and what they supposedly represent is subject to change as data is made and remade in loops of review, processing and adjustment. Following Lagoze, if datasets are uncertain and not fully specified/specifiable, their reuse and combination increases fuzziness, decreasing controllability; uncertainties are not likely to decrease with big data analyses but to multiply; consensual understandings of corporate environmental impacts become impossible.

Scaling up the (re)use of corporate environmental data (for example, with big data analytics) to inform governance is, thus, troubled in several ways: not only is the reversibility and referentiality of data questioned the more data use is removed from the situations in which environments are related to data, but also are the scales of what and how environments are measured rendered less specific. In sum, the kind of corporate environmental data practices I observed is not compatible with the core criterion of providing full information symmetrically to citizens and consumers. Significant information about how environmental information is constituted and how that information is related to local environments evade datafication, so that corporate sustainability reporting feeding discourses of climate change policy and markets asymmetrically informs decision-makers and market actors. Proliferating big data analytics of carbon reporting is likely to fail markets as much as deliberative democracy.

A significant environmental risk is that staging governance as evidence based and, thus, in control, may become eased through discourses of big data. If big data analytics (re)use (even if unwisely) corporate environmental data to offer results to decision-makers, the latter will be propped with even more ‘data’ they can enrol in the play they want to perform. Just as data is enacted to enable performances of audit/ability, (big) data can be employed in performances of evidence to sustain plays of evidence-based governance. To generalise, such big data analytics may prefigure an environmental politics in which environments matter neither ecologically nor economically but environments would exist as an unaccountable, decoupled, post-integrity, ‘post-truth’ but ‘comforting’ dreamscape – an eternal present of environments that do not challenge the political and economic order, prefiguring intensifying market and political system failure. In other words, the crossover of big data with environmental governance cannot be expected to unsettle political and economic arrangements that hitherto have excelled in producing and sustaining unsustainability.

This environmental risk is relevant for reflexive considerations of big data, economy and society. If big data refers to data (practices) ‘beyond integrity’, then the normative questions crops up: should big data analytics be unequivocally supported? Lagoze thinks they should: ‘it is futile and even undesirable to seek a return to traditional, rigid control zones’. I question this stance with boyd and Crawford: ‘how [do] the tools participate in shaping the world with us as we use them’? We have to consider what worlds are prefigured and enacted through (big) data informed decision-making. How to live with governance that merely claims to be evidence based? Forward-looking, at the intersection of constructive societal engagement with nature, truth and technology–specifically big data, the environment and political theory we might have to think differently about politics in techno-environmental zones: how could (post)governance actually be grounded in engagements by all affected parties with troubling and rich stories (of non-standardised, situated, dynamic and deeply contextual realities), rather than mere spreadsheets and PowerPoint slides? Instead of governing as if we knew the facts, we might have to approach environments by radically questioning how ‘modern’ institutions can develop relations of care for environments and their uncertainties. A precautionary approach could call to constrain economic, industrial and societal activities to levels at which detailed, contextual, situated and informationally symmetrical accountability – among the humans affected by the activities – is possible.

This piece summarises Ingmar Lippert’s original research article published in Big Data & Society. It is licenced under CC BY 4.0.

Image: eltpics.