Excerpts from Alan Keeso's Big Data and Environmental Sustainability: A Conversation Starter in the Smith School Working Paper Series

Big data, referring broadly to, “the ability of society to harness information in novel ways to produce useful insights or goods and services of significant value”, has been heralded as, “the next frontier for innovation, competition, and productivity”. Despite these claims, a review of literature that highlights big data’s revolutionising effects across sectors and industries revealed that environmental sustainability is largely not yet part of the popular lexicon of big data in action. This study addresses this gap. By interviewing 14 organizations (including NGOs, consultancies, corporates, policy specialists and governments) across sectors, [Alan Keeso] examines how big data is perceived, employed, hindered, and enabled. [He] concludes that while big data adoption has broadly been slow to coalesce with sustainability efforts, emerging factors such as collaborative partnerships and business model innovation are positioning big data to become an integral element of environmental sustainability and vice versa.

Andrew Armstrong, Managing Partner at Anthesis, has found that the supply of big data initiatives is met with demand from customers, highlighting its relevance to their sustainability efforts.

Brad Blundell, Director at Anthesis, stated that there is an emerging area of work for the firm that is reliant on big data, and it is in demand by venture capital and private equity groups. This source of demand provides unique insight into some of the latest thinking around approaches to environmental sustainability. Investors are increasingly asked questions about the future issues that some of their investments are likely to face, and they are concerned about environmental and legislative impacts, including water and resource scarcity and trade blocks.

Anthesis was asked to create a tool that looks at critical issues grouped around recognised standards to identify circumstantial impacts and opportunities that a business might be exposed to. The tool will evaluate either the potential cost of mitigating an issue or benefit of realising an opportunity. A beverage manufacturer operating in an area where water scarcity is a concern, or a company developing a technology that allows for the substitution of water for concepts such as waterless washing machines exemplifies the risks and opportunities the tool will quantify.

The factors to be weighed are largely outside of the organisations’ and investors’ control, such as how climate change impacts food security, social unrest, or resource nationalisation. These factors also often overlap, which needs to be accounted for in the model. The overlap lends relevance to the definition of big data brought forward earlier by Armstrong, who views big data as a combination of internal and external data sources that begin to add value when correlations are drawn. For example, external information, such as the Organisation for Economic Co-Operation and Development’s (OECD) output table data, could be overlaid with internal data, which could then be added to forecasts to see how costs will change over intervals of time.

Anthesis hopes to access some of this data through open sources, such as flood risk resources in the UK. While the firm will be collecting massive amounts of data for this work, the important ‘V’ is variety, which Blundell substantiated by highlighting the focus needed on the most important aspects of the situation in order to find the right mix of information for the model.

The full paper can be downloaded from the Smith School of Enterprise and the Environment.

To learn more about the Risk Horizon tool, visit risk-horizon.com .