Guide to Data Science and Sustainability
Meet the Experts
Jennifer Lewis Priestley, Ph.D. is the Associate Dean of The Graduate College at Kennesaw State University. She oversees the Analytics and Data Science Institute, which houses one of the country’s first Ph.D. programs in Analytics and Data Science. Dr. Priestley has published dozens of articles related to the application of emerging methods in data science. She is a national speaker and frequent contributor to articles relating to the evolution and maturation of the discipline of data science. She earned a BS from Georgia Tech, an MBA from Penn State and a Ph.D. in Decision Sciences from Georgia State University.
Sherrill Hayes, Ph.D. is the Director of the Ph.D. Program in Analytics and Data Science at Kennesaw State University. Dr. Hayes currently serves as an associate editor for the Journal of Peacebuilding and Development and is the coeditor of Atone: Religion, Conflict, and Reconciliation (Lexington Books 2018). Dr. Hayes received his BS and MS in Human Development and Family Studies at the University of North Carolina at Greensboro and his Ph.D. in Social Policy from Newcastle University in the UK.
In this guide, we will be exploring just a few of the many ways that Data Science and Sustainability are working together to provide new opportunities for students, scholars, and business professionals to do interesting work and have a positive impact on the environment and on society.
What is Sustainability?
One of the most common definitions for sustainability comes from a 1987 United Nations (UN) conference – “(sustainability is) development that meets the needs of the present without compromising the ability of future generations to meet their own needs.”
Sustainability is based on a simple principle: Everything that we need for our survival and well-being depends, either directly or indirectly, on our natural environment. To pursue sustainability is to create and maintain the conditions under which humans and nature can exist in productive harmony to support present and future generations.” 1
While the guiding principles of sustainability have loosely informed previous generations, Millennials (those born between 1982-2002) have not only embraced and integrated sustainability into their vernacular but increasingly have established sustainability as a core belief system that informs decision making holistically. Millennials are using their generational size and power to shape the world around them – socially, economically and environmentally. This power has given rise to movements like “Buy Local, Eat Local”, demands for public investments in alternative energy, preferences to work for companies that embrace LEEDs certification for physical buildings and have carbon reduction policies.
The size of the Millennial generation and the growing push for more “sustainable solutions” have driven businesses to develop an interest as well. The National Association for Environmental Management defines sustainability as “a term that describes a company’s strategies for acting as a responsible corporate citizen, ensuring its operations are financially sustainable and minimizing its environmental footprint. Sustainability initiatives may include natural resource reduction, supply chain management, worker safety and health initiatives…”. These policy changes appear to be sticking – and will likely be standard practice for succeeding generations.
Data Science and Sustainability and the Talent Gap
The combination of societal needs and “corporate social responsibility” programs have generated an unprecedented need for individuals with backgrounds in environmental sciences…as well as the need for individuals with training in analytics and data science.
To be clear, the need for individuals with deep analytical skills is not unique to environmental sciences – studies from McKinsey 2 ,IBM 3 , PWC 4 …all continue to identify the needs for data scientists continue to outpace the supply.
There are several reasons for the talent gap in analytics and data science:
1. Universities have not kept pace with the explosive demand for the analytical talent. The ability to translate massive amounts of structured and unstructured data into information to inform a decision making process does not fit neatly into any traditional academic discipline. Data Science requires a combination of computer science, statistics, mathematics and the knowledge of the domain of application. Most programs in analytics and data science have only emerged onto the academic landscape in the last ten years.
2. Data Scientists are not just for the Fortune 500s. As the cost to capture and store data continues to drop and the volumes of publicly available data continue to grow and expand, small and medium sized firms, as well as non-profits, can benefit from the same kinds of analytical talent that have been traditionally recruited by only the largest (and wealthiest) organizations.
3. The way we define data is continuing to evolve. Data has traditionally been defined by numbers in rows and columns. Today, the definition of data has expanded to include text, voice, and images. In addition, data may need to be analyzed “in motion”. Because of the skills that it takes to manage data, as these definitions continue to evolve, the talent gap will likely continue to exist.
Environmental sciences are part of this conversation. According to the Bureau of Labor Statistics, the demand for environmental scientists and those with a focus on environmental sustainability will grow at an annual pace of 11% for the next eight years.5 As with other dimensions of the economy, data science is increasingly integrated into positions aligned with environmental sustainability movement. The website environmentalscience.org6 states that:
With the heavy focus on quantitative data, a bachelor’s degree (in environmental science) will generally not be enough unless the candidate can display strong aptitude for math, particularly statistics… it is vital that you study minors and electives in math and / or statistics. You are advised to take a master’s degree where available so that you develop a more comprehensive understanding of high-volume data, how to process, handle and interpret it.
Examples of the high demand for environmental scientists with core skills in data science can be found in both the private as well as the public sector.
Companies Integrating Sustainability and Data Science
Over the past decade many private sector companies have become interested in demonstrating what they are doing to improve the world and how these changes have a real impact on their financial bottom lines. External efforts focused on “corporate social responsibility” and internal initiatives like reducing waste in manufacturing processes have both costs and benefits. Companies have turned to “Sustainability Analytics” to help them demonstrate to the public and to their shareholder the impact of their sustainability initiatives.
Consider for example, how UPS, the world’s largest package delivery company, uses data science to calculate the optimal routes for its drivers, reducing the number of miles they travel by the millions. This optimization not only spares the environment unnecessary pollution, but it also saves the company millions on fuel costs and wages.7
One of the many ways that Google is engaged in sustainability is through the Geo for Good team that investigates the use of maps, data and machine learning to solve environmental problems, such as creating databases and software to monitor the health of tropical forests around the world or track illegal fishing.8
Companies are also leveraging IoT – sensor-based data in motion – and analytics as a way for consumers to have an impact on everyday sustainability efforts and research on issues like climate change. For example, Canadian thermostat company Ecobee is asking consumers to “donate their data” to better understand how people actually use energy with the ultimate goal of improving overall energy efficiency. Ecobee then partners with universities to provide access to this interesting and much needed data on sustainability issues to faculty and students. As more projects like these emerge, having students and faculty knowledgeable in both sustainability issues and data analysis becomes more important.
Sustainability, Data Science, and the UN Sustainable Development Goals (SDGs)
More broadly, the United Nations has adopted a set of goals to end poverty, protect the planet and ensure prosperity for all as part of a new sustainable development agenda. Each goal has specific targets to be achieved over the next 15 years.9 Each of these goals requires “a more comprehensive understanding of high-volume data, how to process, handle and interpret it.”10 Specifically, addressing the non-trivial challenges related to data collection, metric development, and predictive modeling will likely drive successful attainment of the UN’s sustainability agenda.
In 2017, the UN’s Data for Climate Action contest saw groups of researchers from universities, NGOs, and companies around the world produce a series of interesting studies that examined issues ranging from “Electro-mobility: Cleaning Mexico City’s Air with Transformational Climate Policies Through Big Data Pattern Analysis in Traffic & Social Mobility” (which was the ultimate winner) to tracking poverty using satellite images, gendered effect of climate change through analyzing social media, and climate stress on migration using mobile phone data. These projects show the diversity of data available and the multitude of ways it can be applied using analytics and data science strategies and techniques.
Becoming a High Value Contributor in the Sustainability Movement
For those considering a career in environmental sciences, you should be looking for course options that will allow for the development of deep analytical skills. For some, this may include an undergraduate degree in environmental sciences and then a graduate degree in analytics or data science.
In the Kennesaw University program, one of the Ph.D. students in Analytics and Data Science has an undergraduate degree in Anthropology and a Masters degree in Epidemiology. She is interested in pursuing a career in public health. When we asked her why she did not consider a Ph.D. in Epidemiology or something more similarly aligned with public health, she said “I understand the issues and challenges of public health. What I need is a deeper understanding of how to work with data to inform policy. Most programs in public health do not offer a deep enough curriculum in data science to work with the kinds of data that are now dominating the conversation. Basically, I know public health…I don’t understand machine learning and neural nets.” Her experiences are representative of the conversation in environmental sciences.
Those pursuing environmental sciences today will be faced with increasingly data-centric challenges like leveraging satellite imaging to control deforestation and using migratory tracking data to determine how climate change is impacting different species of birds. This requires subject matter expertise, but also the analytical skills necessary to be effective.
In addition to considering a graduate degree in analytics and data science, here are three suggestions for people looking to make a difference in environmental sustainability:
1. Learn how to translate data into information. Even if you are not in a position to apply to a graduate program, look for courses (even online options) in applied data science, including highly applied courses in statistics. These courses will help you to understand how to work with data. Also, learn how to code – R, Python, SAS, SQL. Once data is captured, its translation does not happen with an abacus.
2. Consider best practices from other disciplines. Environmental sciences are still pretty new – and still developing their own best practices related to working with massive amounts of data. There are opportunities to study data science in another context and port those best practices into a new context. For example, risk modeling, segmentation, predictive maintenance are well established in areas like Financial Services, Marketing, Health Care, and Manufacturing. These concepts all have potential application in environmental sustainability.
3. Get started on some applied projects. There are many organizations and forums which facilitate opportunities at no cost to get involved in data-centric projects. Examples include:
a. Statistics Without Borders
b. Data Kind
4. Additional resources for those looking for a career in environmental sustainability:
Sustainable Business was launched in 1996 and has since published over 26,000 articles. The site offerers daily green business news, covers green dream jobs, has a green education directory, and has a venture capital directory profiling investors that finance green businesses of all kinds.
Eco Jobs was first launched in 2006 and now has over 25,000 visitors per week. The site has an extensive listing of jobs across natural resources & conservation, environmental science & engineering, renewable energy, outdoor & environmental education, environmental law, and environmental advocacy.
Environmental Jobs is another site focused on jobs in engineering, science, policy, energy, and more. Their motto is “Saving the planet one job at a time.”
Green Jobs has an environmental job board as well as a social impact job board. The site lists over 20 different categories from climate change to wind power. They also have listings by state and cover many of the larger metro areas across the country.
Environmental sustainability is not just having a moment – it has become the new normal in the way that we interact with the world around us. And the most effective contributors to environmental sustainability are fluent in data science.