Examining the Gap between Big Data and Environmental Sustainability Measurement

The Scenario:

Imagine sitting in an executive chair, about to make a potentially business-changing decision, while a team of fellow leaders hurls information at you concerning the past year’s performance and growth. Each department head comes forth with valuable information: summaries of earnings, expenditures, revenue, and a number of other quantifiable facts. It’s a stressful time for many leaders – but relatively easy to cope with given that the numbers are something we deal with and understand on an intuitive level.

Now, envision being in that position and being informed of all of the information concerning the past year’s environmental sustainability efforts and progress. Now it’s the chief sustainability officer’s turn to explain some annual stats: the company’s carbon footprint increased by 5 million tons, the water footprint decreased by one million gallons, and the company generated 200 tons of waste.

The typical response is for the members of the group to look around and nod their heads appreciating that these numbers mean something significant. But what exactly do they mean? Although quantifiable, these data points remain unclear.

The reality is that while these numbers give us some notion of the levels of resource consumption in each specific domain, they don’t help us understand the environmental impact, or level of resource consumption, a company is achieving, in any way that supports decisions. It’s not the CSO or CEO’s fault; every business uses these numbers because it’s what is measured. But it still holds no decision-support meaning. This is also the biggest problem plaguing sustainability reporting – the lack of a common, universally accepted language for environmental sustainability that serves as a common denominator for comparing projects in different domains.

The Problem

Because of its massive data sets, today environmental sustainability meets Big Data. The problem is not data but interpretation. To start, environmental sustainability is a complex idea. Generally speaking, most people are not well-versed in the language concerning the idea. Overly complex and hard to understand, executives are unable to make intuitive decisions based onthe information, because they cannot interpret the data. The data lacks context and therefore holds little meaning to top-level executives.

Big Data refers to the ability to accumulate, structure, and interpret unstructured data.  The term generally refers to data sets whose size is beyond the ability of commonly used software tools to capture, manage, and process within a tolerable elapsed time. And these data sets are huge. As of 2012, Big Data sets range from a few dozen terabytes to many petabytes of data in one single set. To put that into context, one terabyte can hold 1,000 copies of the Encyclopedia Britannica while one petabyte is able to hold 500 billion pages of standard printed text. Since environmental information concerns input such as satellite images and power plants emissions, it falls by definition into the big data category.

Executives are lost in a sea of information, laced with confusing terminology. This lack of clarity makes business decisions even more difficult. To an outsider, resource consumption isn’t measured in traditional terms, but rather in a set of foreign “currencies” without a currency converter:

• Water in kgal
• Electricity in kWh
• Heating in mBTu
• CO2 in tons

This vocabulary isn’t a commonly used language. The problem that appears to be halting the harmonious relationship between Big Data and resource consumption information is not the data. The root of the problem rests in the interpretation of the information and data sets. The numbers are already difficult to digest and unintuitive, so it doesn’t help the case that there isn’t a commonly shared, universal language in the measurement of environmental sustainability.

The Solution

Through the development of a mathematically rigorous, yet simple and intuitive way to interpret the different data streams, companies may be able to make better business decisions. Companies currently use business intelligence and analytics to better understand and predict future trends. Through the use of Big Data, similar techniques can be applied to better understand businesses processes and environmental sustainability efforts.

For instance, businesses might transform each domain-specific resource into the energy used to create that resource. This can then expressed in terms of what we call Energy Points, using the equivalent of the embodied energy of a gallon of gasoline as a unit and factoring in local parameters such as water scarcity. So instead of wondering what is the relative importance of 1,000 kgal and 1,000 kWh, businesses can simply treat each domain like Weight Watcher’s treats calories – based on efficiency points. This is one way we collapse Big Data to a common metric to address resource consumption decisions.

There are other benefits that come along with using Big Data. Companies are able to share and access real-time analytics and data sets, allowing progressive companies and organizations to release data to a broader ecosystem. A relatively simple task for IT, companies can exponentially increase efficiencies and provide the new material for building tangential business models and interactions.

Big Data is playing an increasingly critical role in decision making, given simple and rigorous interpretation, that is the power to help transform the sustainability from an exercise in feel good terminology, to a quantifiable approach that has a true impact on our environment. The road to get there is paved with mathematics – developing intuitive, yet mathematically rigorous ways to interpret the data.

Dr. Ory Zik founded Energy Points to enable corporations to capture the value of environmental sustainability in a simple, accurate, and practical way. Prior to founding Energy Points, he was the founding CEO of solar thermal augmentation company, HelioFocus Inc., which develops solutions for conventional power plants.