Data-driven analytics applications are eating the world and transforming every domain. But the world is also being eaten up in a different way by several non-sustainable practices.
On Earth Day, we look at what we know about the relation between big data and the environment: how big data is used to measure sustainability and inform action, and what is the impact they have on the environment as a whole.
Analytics applications range from capturing data to derive insights on what has happened and why it happened (descriptive and diagnostic analytics), to predicting what will happen and prescribing how to make desirable outcomes happen (predictive and prescriptive analytics).
Each organization is on a different point along this continuum, reflecting a number of factors such as awareness, technical ability and infrastructure, innovation capacity, governance, culture and resource availability.
While businesses vary in each and every one of these factors, they typically have one thing in common: they have a specific domain they operate in, as well as business and governance models with clearly defined stakeholders and responsibilities.
But things are different when it comes to sustainability. There is no business model for sustainability per se, rather this is an externality for pretty much every business model. Although businesses are affected by factors such as environmental quality, and in turn their actions can also affect the environment, most business models fail to capture this interplay.
Hence the burden of measuring and promoting sustainability falls on the shoulders of governments, non-governmental and inter-governmental organizations. And this can by and large account for the gap we observe in analytics applications for sustainability.
Compared to businesses, these organizations are typically at disadvantage in every possible way. Case in point: the Sustainable Development Goals (SDGs). SDGs, officially known as "Transforming our world: the 2030 Agenda for Sustainable Development" comprise a set of 17 "Global Goals".
SDGs are spearheaded by the United Nations through a deliberative process involving its 193 Member States, as well as global civil society. So how does progress towards goals broad and ambitious such as "No Poverty", "Sustainable Cities and Communities" and "Climate Action" gets measured and evaluated?
Briefly - with great difficulty, if at all. SDGs are broken down to indicators such as "Percentage of urban solid waste regularly collected" or "CO2 emission per unit of value added". The difficulty is due to a few factors.
First, these metrics need to have solid and clear definitions that can be shared and agreed upon among UN members. There is work in progress in the UN to develop a global indicator framework for the SDGs.
Part of this work is dedicated towards building an SDG ontology to help formalize, share and integrate indicator definitions. Ontologies are formal data models that can greatly facilitate data definition and integration efforts, and the SDGIO project is working towards this goal by integrating relevant work in the field.
But even if metrics are defined and shared, they need to be populated with adequate reliable data to be useful. Relying on surveys is problematic, so the UN is leading efforts to coordinate stakeholders such as national statistics offices to provide concrete examples of the potential use of Big Data for monitoring SDGs indicators.
The UN has also assigned the Global Pulse innovation initiative to work specifically on applications that contribute towards achieving the SDGs. Global Pulse recently presented its work, most notably some prototype applications to collect data from sources such as satellite imagery and radio broadcasts.
The issues the UN has to deal with are huge and complex. Although these initiatives could signify a turn towards an effort to proactively collect data, rather than expect data to be handed over, there is still a long way to go.
