Big Data and Environmental Sustainability: A Conversation Starter (in brief)

In 2014, I wrote “Big Data and Environmental Sustainability: A Conversation Starter” for my 15,000-word MSc in Biodiversity, Conservation and Management dissertation at Oxford University. I later published the 11,000-word working paper version through the Smith School of Enterprise and the Environment during my Oxford MBA in 2015. The intent of this much shorter version is to provide the key findings of the study in a more digestible word count (~2,500) and format.

Overview, Impact, and Study Participants

Big Data: A revolution that will transform how we live, work and think (2013) is one of a growing number of books that provides accounts of compelling applications of big data analytics. A review of the literature on big data revealed however, that environmental sustainability is largely not yet part of the lexicon of big data in action. Big data is playing a transformative role in sectors such as retail, manufacturing, and healthcare. Yet, the environmental sustainability effort requires a transformation of its own in the wake of human-driven climate change, mass extinction, and biodiversity loss.

By gathering insights from NGOs, consultancies, think tanks, corporates, and governments, I provide a multi-perspective view of:

1. How big data is being applied to the environmental sustainability effort;
2. To what extent big data is impacting environmental sustainability;
3. Who the leaders and late or non-adopters are; and
4. What some of the reasons are for late or non-adoption of big data tools.

Table 1a. *Organisations interviewed by category

Table 1b: List of Interview Participants by Organisation

Results of the Study

Table 2. Organisations’ level of engagement with big data

Defining Big Data

A broad range of views on big data’s definition was encountered throughout this study. Just over half of the organisations indicated that clarity around the definition of big data is important. None of the participants had a refined definition. Rather, a working interpretation was provided or the participant simply stated that he or she does not have a definition for — or interest in defining — big data.

The World Resources Institute (WRI), for example, has directly engaged with big data, but they are not concerned with the general lack of agreement around big data’s definition internally or externally. The British Trust for Ornithology (BTO) has been collecting data since the 1930s and has accumulated a lot of it, but the organisation identifies simply with data as opposed to big data. The most detail applied to big data’s definition came from the consultancy category. Anthesis Group draws from the International Institute for Analytics’ (IIA) model, which places big data as a second-generation development, following traditional analytics and preceding Analytics 3.0. Analytics 3.0 combines traditional analytics and big data to yield “insights and offerings with speed and impact”.

Big Data Skill-Sets

Only five of the organisations employ data scientists in name, highlighting the potential absence of the skills needed to manipulate big datasets. None of the NGOs interviewed employ a data scientist in name. Several participants indicated however, that their organisation contains a variety of roles that touch upon elements of the data scientist’s description.

BT Group and LinkedIn employ data scientists, as do WRI and the World Bank. Of the consultancies, Accenture is the lone organisation that employs data scientists, but Anthesis works with data specialists external to the organisation in addition to employing data analysts that function as data scientists. The housing of these skill-sets signals that the organisation is capable of directly engaging with big data.

Big Data’s Relevance to Environmental Sustainability Efforts

Figure 1. Each category’s scoring on big data indicators listed in Table 2 and overall score. ‘Overall Engagement with Big Data as a Concept’ indicates the extent to which the overall category is engaging with big data based on its scoring across the factors on a scale of zero (no engagement) to one (full engagement).

Greater consistency was found on perspectives of big data’s impact on environmental sustainability efforts. LinkedIn and BT are making big data a core element of their environmental sustainability efforts, largely driven by energy efficiency management. The consultancies have largely integrated big data into their practices as a competency, finding that the supply of big data initiatives is met with demand from customers. Both the Dutch and US governments stated that they recognise the utility of big data applications toward environmental sustainability efforts.

NGOs expressed less optimism on the impact of big data. The Zoological Society of London (ZSL) and BTO highlighted technology improvements that are helping them to collect data in new ways, but expressed doubt as to whether big data as a concept is having a significant impact because projects that might be thought of as big data initiatives are not referred to as such. Conservation International (CI) believes that while the volume of data they collect may not be considered big data in some sectors, it could be considered big to the field of biodiversity conservation and especially big to some of the remote regions in which CI is working.

Applications of Big Data towards Environmental Sustainability Efforts

Private Sector — NGO Partnership: HP Earth Insights and CI’s TEAM Network

CI’s Tropical Ecology Assessment Monitoring (TEAM) Network focuses on biodiversity monitoring in tropical forests. HP Earth Insights was formed to apply a big data solution to biodiversity loss by combining CI’s data collection efforts with an HP software tool that allows scientists to “quickly analyse large and fast-growing volumes of data” through a platform that manages “the full spectrum of structured, semi-structured, and unstructured data”. HP reports that scientists can analyse data 87 per cent faster, reducing months or weeks-long analyses to hours by a single person. The initiative is generating “findings about the environment that were previously unknown”, such as species population declines in comparison to baseline levels.

Private Sector — International Institution Partnership: Global Forest Watch

WRI’s Global Forest Watch (GFW) merges the latest technology with on the ground partnerships to enhance forest information. GFW brings together satellite technology and crowdsourcing to generate a mapping application used, for example, by the Jane Goodall Institute to monitor chimp habitats, park rangers to monitor the boundaries of protected areas, and indigenous groups to monitor borders. Stakeholders are also able to set up email alerts for specific countries when forest loss occurs. The process is now consistent: an environmental problem effecting surrounding communities is identified, an effort is made to use publicly available data at the highest resolution, and the data is transitioned into digestible formats such as maps and rating systems.

The United States Government’s Open Data Initiative

Data.gov collates and harvests metadata from 175 different Federal agencies. 100 non-Federal or non-governmental agencies, including states and cities, contribute, as do 44 international countries and 163 international regions. Several ways in which these massive open datasets are applied, include:

1. To better understand how offshore wind development is impacting birds;
2. To factor in endangered species’ locations for ocean-based work; and
3. To map the seafloor for areas of biological sensitivity.

In addition to the oceans focus, the climate and ecosystems communities opened in 2014.

A Corporate Sustainability Effort: BT’s Better Future Programme

BT applies big data to further enhance its environmental sustainability efforts through smart energy management. BT has achieved this by adding more than 50,000 smart meters and implementing a smart energy management system, delivering annualised savings of nearly five million pounds. Each of the smart meters generates a vast number of data points, and the system automates energy use. Typically, implementing smart metering systems presents a shift from one energy reading per month to one every 15 minutes. For BT, that translates to more than 4.8 million reads per day.

Building Big Data into Sustainability Advisory Services at Anthesis Group

Anthesis has created the RiskHorizon tool, driven by demand from venture capital and private equity groups. It looks at critical issues grouped around recognised standards to identify circumstantial impacts and opportunities that a business might be exposed to. The tool evaluates 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 is one example of a risk that the tool quantifies. Factors in the model often overlap. For instance, external information, such as the OECD’s output table data, can be overlaid with internal data, which can then be added to forecasts to see how costs will change over intervals of time.

Additional Examples: ZSL’s Innovative Data Collection and Analyses

ZSL manages large datasets. One of these datasets is the Living Planet Index, which includes population time series for roughly 3000 species and 11,000 populations and contains approximately 30 to 40 years of data for each. Moreover, this data encompasses metadata about that species, such as threat status and whether or not it’s a migrant species. ZSL analyses this data and publishes the Living Planet Report in partnership with WWF-UK. Another example is found in movement ecology through the placement of GPS trackers on birds to record their locations and migration patterns over time. This data is also tied to other datasets, specifically predator activity and temperature. More affordable and lighter devices are enabling and changing the understanding of animal behaviour.

Barriers to and Opportunities for Big Data and Sustainability

Figure 2. Number of internal versus external barriers identified by category

Only the NGO category identified more internal (experienced within the organisation) than external (observed elsewhere) barriers, indicating that the category experiences or perceives significant internal challenges to big data adoption. Consultancies identified the lowest percentage of internal barriers, reflecting their outward looking perspective through the provision of expertise to clients.

Descriptions of the barriers can be found in the full working paper.

Figure 3. A Heat Map of barriers to big data adoption by organisation

Figure 4. Number of internal versus external opportunities by category

The total of opportunities mentioned by NGOs is nearly double the next highest total at 11, which was generated by the consultancies. All categories identified more opportunities as internally relevant than externally relevant. This also holds true for all organisations except one, CI, who has progressed much further on big data adoption than the other NGOs interviewed.

Descriptions of the opportunities can be found in the full working paper.

Figure 5. A Heat Map of big data adoption opportunities by organisation

Discussion

Meaning and Standards

Given that partnerships were widely identified as an opportunity for big data adoption, it seems prudent to facilitate a common language. For example, an NGO that uses big data terminology might better engage with emerging sources of funding that could otherwise fly overhead. Furthermore, the BT Better Future Forum confirmed the need for leadership in the establishment of big data standards. Without them it is difficult to understand how the collaborative partnerships needed to drive big data solutions to scale can form.

Given the government’s experience in establishing open data parameters, extending those efforts to big data standardisation seems appropriate. Additionally, greater government outreach is needed for organisations looking to escalate their sustainability efforts through big data solutions. Concurrently, raised environmental performance standards through government regulation might reduce corporate fears that investing in data analytics for sustainability efforts will jeopardise competitiveness.

The Relevance of Big Data to Environmental Sustainability Efforts

Does late or non-adoption of big data place organisations at a disadvantage? NGO scoring on the basic big data engagement indicators paints a bleak picture for the category. The UN Global Compact-Accenture CEO study (2013) highlighted that two-thirds of CEOs indicate that partnerships with NGOs are crucial to delivering sustainability. Notably, big data was the driving factor in the partnering of HP and CI, illustrating that big data can be the enabler for environmental sustainability goal attainment.

Can big data solutions align with conservation efforts? Big data gains traction with sustainability issues that occur at a large scale, such as climate change. Hands-on conservation tends to be applied to changing local behaviours for environmental outcomes. WRI demonstrates however, that big data can indeed empower communities to generate change themselves through pinpoint, source information on environmental issues.

The Relevance of Sustainability to Organisations’ Big Data Efforts

There is an emerging interest from the environmental field in big data that provides specialists with opportunities to build their businesses’ brands while also showcasing their technologies. This, combined with the UN Global Compact-Accenture CEO study findings that only 38 per cent of CEOs believe they can accurately quantify the value of their sustainability efforts, suggests that big data should indeed have precedence to enable corporate sustainability progress.

Also, companies that are leading sustainability efforts are doing so by embedding environmental outcomes into their strategies and business models. Big data has been an enabler in the emergence of the ‘circular economy’, for example, and drawing again upon the 2013 UN-Accenture CEO study, a third of CEOs, “report that they are actively seeking to employ circular economy models”. Big data and sustainability are likely to increasingly merge through these business model innovations.

Conclusion

The opportunities raised to adopt big data demonstrate that some organisations are thinking about how to make big data central to the value they can bring to environmental sustainability efforts. There is certainly cause for excitement, and I look ahead with enthusiasm to the future of environmental sustainability with big data further integrated. My hope is that the approach is a multi-disciplinary one between sectors, industries, and roles to drive the innovation forward on different fronts.

@AlanKeeso

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Top 5 Links

Big Data and Environmental Sustainability: A conversation starter (for my full working paper through Oxford University’s Smith School of Enterprise and the Environment)

Big Environmental Data: 5 Key Insights from 5 Minutes with Dr. Paul Jepson (for a video of Paul discussing big data and planetary management with a summary of insights from the conversation)

Anthesis Group’s Risk-Horizon tool (for more about their big data initiative)

Waste to Wealth: The circular economy advantage by Peter Lacy and Jakob Rutqvist (for more on the movement and big data’s role)

The Living Planet Report for 2014 (for ZSL and WWF’s work)