Trendster: Big Data and Sustainability

Decision Scientist and current SUMA student Aksheya Chandar sheds light on the connection between massive data sets and sustainable development in business and beyond.

Sustainability entails decisions that are socially, environmentally and economically equitable. This isn’t a new concept, but rather a different way of approaching traditional decision making at the residential, commercial, industrial, and political levels. Once we are able to appreciate this fact, it naturally follows that information is critical to enabling sustainability in the same way it has traditionally been used for decision-making. The question then is- what information is required for sustainable development and where is it available?

Data in its rawest form is just an ingredient. It needs to be organized, crunched and visualized in a certain way before it can be termed as useful information. Organizations are able to collect raw data at an unprecedented scale; every aspect of an individual’s recorded activity is data for someone. For example, one exits their apartment to buy something at the grocery store using their credit card, followed by a walk to the bank to deposit a $100 bill. An Uber is then ordered to meet a friend, and one exchanges several texts with her along the way. Millions of people perform similar actions every day and in effect are generating data that can be used to generate insights both at the macro and microscopic level. To elucidate this clearly, let us imagine that we work at a retail corporation and have access to the rawest form of their transaction data. Using this data and the right skill set, we can visualize information at various levels and generate descriptive reports, model scenarios and develop predictive solutions as well as conduct experiments and monitor results. All these activities are conducted with the goal of increasing trade and improving the company’s bottom line. However, this data could also be used by governments and policy makers to analyze regional income trends and consumer behavior, assess the footprint of a typical shopping basket, and a myriad of other activities that can aid in development and education.

Another example- Target was able to successfully analyze purchase patterns to conclude that a certain teenage girl was expecting a child. The same information that requires ultrasound technology and medical tests was achieved through complex pattern recognition algorithms. While this information is often used to fine tune marketing strategies, imagine how the same information could be used by developmental authorities to delineate fertility patterns across regions and generate useful insights. It is up to us to imagine and propose such uses as well as build a case for them. The beauty of it is that the biggest challenge, i.e. privacy of information, isn’t an issue when it comes to macro-level decision making. We aren’t concerned about specific individuals but rather larger trends exhibited by masses.

Through regular activities at the individual and commercial level, such data is accumulating at an ever increasing pace and needs an infrastructure that makes it accessible and usable.

This is where Big Data comes in- it refers to a term for data sets that are so large or complex that traditional data processing applications are inadequate to deal with them. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, querying, updating and information privacy.

While it is important to appreciate the technologies that enable effective analysis and information generation, the first step is to visualize sustainability as a map of problems that need to be solved and the associated information that, if available, could help solve those problems.

This involves taking a step back to understand all the data (and potential information) that currently exists, the gaps that need to be filled and the challenges that prevent this potential information from being used for social and environmental good. Very often, data is proprietary and not easily accessible due to privacy issues. This is a primary challenge and has resulted in a form of information asymmetry, where a wealth of raw data exists with private corporations but is yet to be tapped into for macro-scale development decisions. Those who are sitting on this gold mine of data often lack either the skills to extract the information, or the impetus to use it for anything other than bottom line gains. It follows that Sustainability Managers should prioritize policy design and skill set development to enable better insight generation and data driven action.

What is important to embrace is that there are ways to influence sustainability without actually being “hands on” in a sustainability role. Big data requires problem solving, analytical and intermediate computer skills while sustainability requires an in depth understanding of all systems that operate and run this world (or if that is too broad, at least in your field of interest). It is the marriage of the two that makes a perfect candidate, capable of visualizing and leveraging the power of information to enable suitable actions.

-Aksheya Chandar, M.S. Candidate
SUMANI Trendster Contributor, 01/23/17

*Trendster is a voluntary, crowd-sourced initiative facilitated by SUMA Net Impact. It does not represent the collective views of Columbia University, the Earth Institute or Net Impact

One thought on “Trendster: Big Data and Sustainability

  1. Pingback: SUMANI Trendster: Editor’s Note – SUMA Net Impact

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