Everyone’s talking about it.
Many are asserting commitment.
Nobody really knows what it means.
THE ADVENT OF BIG DATA
Let’s be clear: Big Data is not novel. Debate has ensued amongst academia as to when exactly Big Data originated and to whom specifically credit should be warranted, but inklings of the terminology trace back to over a decade ago. Moreover, there is a school of thought, lead by Information Technology research group Gartner, asserting that Big Data hype has already passed its peak and we are well on our way to a trough of disillusionment.
The simple truth is that 90% of the data in the world today was created in the last two years alone. (IBM).
Big Data can answer some big questions.
Which is more carbon intensive: driving a car or watching T.V.?
The first inclination response is often “driving a car”. After all, isn’t that precisely why Elon Musk is striving to provide sustainable transit to the masses? Isn’t that precisely why ambitious entrepreneurs are brewing biodiesel in their own backyards and scientists are fishing for carbon-sequestering algae?
Interestingly, streaming a football match on a smartphone or tablet results in the exact same quantity of carbon emission as driving ten miles in a gasoline-powered vehicle. (Carbon Trust, 2013).
How does one reach such a conclusion? Two words. Big. Data.
THE TRUTH ABOUT BIG DATA
What can only be described as a blend of alarming and incredible, the magnitude of data generation has reached unchartered territory, never before anticipated by even the most astute experts.
Today, the rapid advancement of data gathering, information technology, connectivity, and computing power is allowing a level of quantification and analysis that could only be dreamed about a decade ago.
According to IBM, an approximate 2.5 quintillion bytes of data are created every single day. By the year 2020, there will be 300x more information in the world than there was in 2005, an approximate 43 trillion gigabytes.
Similar to the advent of “smart beta” in the financial markets, which has lead many to wonder if prior beta was “dumb”, the nomenclature “big data” lends assumption to past data being “small”. And really, what is the difference between small and big data?
WHAT DOES BIG DATA MEAN FOR SUSTAINABILITY?
A lot, actually.
The world has, perhaps unfortunately, run on small data since the beginning of time. Small data is limited and leads to erroneous conclusions and incorrect decisions.
In 2011, Moneyball taught the average movie-goer about the massive opportunity offered by an increasing availability of large arrays of data. Moneyball taught the world that analyzing data could lead to a collection of decisions – decisions that appeared incorrect in isolation, but in aggregate produced a winning solution.
We are fortunate to live in a world where Information Technology and Big Data have the potential to alter industries.
“It is rare that there is a confluence of two seismic events as transformative as climate change and big data. It presents amazing opportunities, as well as responsibilities.” – Gary Suvis, CMO of Big Data company Syncsort, Inc.
Consider the following.
Pharmaceutical giant GlaxoSmithKline (GSK) only retains control over approximately 20% of its carbon footprint. The other 80% of carbon secretion is from indirect emissions on behalf of customers, e.g. usage of GSK products.
British Telecom (BT) recently reported that 92% of its carbon emission is out of its direct control and that 17,000 suppliers in its supply chain account for 64% of the emissions.
GSK and BT serve as just two examples of the need for Big Data. These companies, along with the thousands of other corporations who not control their entire carbon footprint, prove that second- and third-order effects of supply chain and consumers can often overwhelm the more obvious first-order effects of the companies themselves. Successful decisions are dependent on the consideration of all relevant factors.
Companies have never before taken interest in the full impact of their operations. And why would they? Never before have consumers and investors demanded the level of corporate responsibility that is expected today. Never before have companies been pressured to internalize the negative externalities they put forth on the planet.
Improvements in data collection, analytics, business software and measurement requirements are coercing a level of transparency unheard of a decade ago.
QUANTIFICATION IS PARAMOUNT
Professor Robert Eccles and the Sustainability Accounting Standards Board (SASB) are breaking new ground. The mission of the SASB is to develop and distribute sustainability accounting standards that mandate public corporations to disclose material information to investors. Why? Because the global economy has reached a point such that a company’s impact on society and the environment is material to that company’s long-term profitability.
Big Data is the engine that fuels the SASB’s objective.
NOT ALL DATA CREATED EQUAL
The Wharton Business School published a report in September of 2014, Sustainability in the Age of Big Data, in which the following questions were posed:
- Can Big Data be used to influence people’s behavior without manipulating them?
- Can private enterprise capitalize on Big Data’s possibilities without riding roughshod over the rights of those who generate the data?
- Can the high-tech innovations already underway in the developed world help solve the problems of those most in need?
The simple answer? Yes.
But “Yes” is dependent on the cooperation and sharing of data. No company can tackle Big Data alone. “Yes” is dependent on the ability to interpret thousands of trillions of data points. The risk of contradictory data rises in tandem with an increased availability of analytics. Big Data is not foolproof.
THE NEXT FRONTIER:
Behavioral finance asserts that decision making is not always rational. Human tendency is to deny and justify decisions, even when based on faulty premises. Big Data creates a tremendous opportunity, but only if companies invest in the collection and interpretation of analytics.
According to IBM, the ROI of business analytics solutions that incorporate predictive analysis is approximately 250%.
Gone are the days when sporting an all-encompassing enterprise system and publishing data-driven reports were competitive advantages. Today, corporations are faced with a tremendous opportunity. Companies that can cost-effectively incorporate Big Data into the decision making process – and truly understand which data is relevant – will win.
Big Data will accelerate sustainable business. By analyzing a multitude of factors, we can use data to prove analysis that may seem counterintuitive, e.g. that driving a mile in a gasoline-powered vehicle emits less carbon than watching a Netflix movie. The extent of the necessary analysis is colossal. When will the tires of the car require replacement? What will be the effect of traffic congestion on carbon emission? How many carbon-emitting police cars are necessary to monitor speed? How many times does the average driver step on the break? The list goes on.
There are thousands of variables to calculate in order to make decisions about a five-minute span of time. This is Big Data.