Evaluation of the Business Case for Using Analytics for Corporate Sustainability and
Overcoming the Challenges in its Execution
By
Nihar Dalmia
Bachelor of Commerce
St. Xavier's College, University of Calcutta, 2005
Master of Business Administration
SaYd Business School, Trinity College, University of Oxford, 2013
SUBMITTED TO THE MIT SLOAN SCHOOL OF MANAGEMENT
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE
DEGREE OF
MASTER OF SCIENCE IN MANAGEMENT STUDIES
AT THE
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
JUNE 2014
D 2014 Nihar Dalmia. All Rights Reserved.
LIBRARIES
The author hereby grants to MIT permission to reproduce and to
distribute publicly paper and electronic copies of this thesis document in
whole or in part in any medium now known or hereafter created.
Signature redacted
Signature of Author:
MIT Sloan School of Management
May 9, 2014
Signature redacted
Certified By:
Matthew Amengual
Assistant Professor
Institute of Work and Employment Research
MIT Sloan School of Management
Thesis Supervisor
Accepted By:
Signature redacted
(I
Michael A. Cusumano
SMR Distinguished Professor of Management
Program Director, M.S. in Management Studies in Program
MIT Sloan School of Management
1
i
Evaluation of the Business Case for Using Analytics for Corporate Sustainability and
Overcoming the Challenges in its Execution
By
Nihar Dalmia
Submitted to the MIT Sloan School of Management on May 9, 2014 in
partial fulfillment of the requirements for the degree Master of Science
in Management Studies
Abstract
in an age where organizations across industries and sectors are placing increasing importance on
sustainability, leaders are looking for more accurate information to guide their decisions on
sustainability-related issues. Analytics and big data have been recognized as valuable tools to advance
sustainability efforts both at a community as well as an organizational level. In the last decade specially,
tools have been adopted by many organizations to gather and analyze data relevant to sustainability, to
gain insights through business intelligence tools and to extend visibility through the supply chain.
However, with opportunities come challenges and questions remain about the validity of the business
case for sustainability analytics. As sustainability analytics gains momentum, understanding, identifying
and addressing the challenges that come along with its adoption is going to be critical for companies to
be well prepared to take their sustainability efforts forward. This thesis evaluates the emergence of
sustainability analytics and the business case for its adoption, identifies the challenges that hinder its
success and points towards shifts in current approaches to overcome those obstacles. Further, two of
the assumptions about big data are that organizations are 'swimming in a sea' of data and thus the
challenges lie in finding valuable insights, and that challenges associated with big data are primarily
technical in nature. This work investigates these claims from the perspective of sustainability analytics
and aims to identify the opportunities and challenges that organizations must be aware of to maximize
its sustainability efforts using analytics. Various cases and interviews are cited to support the analysis
and arguments presented in this paper. Chapter 1 opens the paper with a discussion of the link between
sustainability and analytics and the emergence of sustainability analytics. Chapter 2 investigates the
expected and realized value based on the business case(s) for sustainability analytics. Chapter 3
evaluates the challenges that can lead to the failure in realization of this value and addresses common
assumptions that contribute to this failure. Chapter 4 discusses and recommends a shift in the finance
function as a key ingredient to the success of sustainability analytics. The paper concludes with a
proposed strategic framework in Chapter 5 that organizations must consider in their pursuit of
overcoming the challenges of successfully adopting sustainability analytics.
Thesis Supervisor: Matthew Amengual
Title: Assistant Professor, MIT Sloan School of Management
3
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4
Acknowledgements
To my wife, Ekta, for her eternal love and patience, my parents for their unending support
and inspiration, and my brother for constantly pushing me forward
A special thank you to my advisor for guiding me through my research and nudging me
forward when it was most required
5
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6
Table of Contents
Abstract.........................................................................................................................................................
3
Acknow ledgem ents.......................................................................................................................................5
Introduction ..................................................................................................................................................
1.
W hat is the relationship betw een sustainability and analytics? ....................................................
11
Em ergence of Big Data and Analytics ....................................................................................
11
1.1.1
W hat is Big Data?................................................................................................................
11
1.1.2
M ajor m ilestones ................................................................................................................
15
1.1.3
The big data landscape ....................................................................................................
17
1.1.4
The big data cycle - A system s dynam ic view ..................................................................
18
1.1
Em ergence of sustainability analytics .....................................................................................
19
1.2.1
W hat is corporate sustainability? ...................................................................................
20
1.2.1.1
People (The social pillar of sustainability)........................................................................
21
1.2.1.2
Planet (The environm ental pillar of sustainability).........................................................
22
1.2.1.3
Profit (The econom ic pillar of sustainability)................................................................... 23
1.2.2
W hat is Sustainability Analytics? ....................................................................................
23
1.2.3
Sustainability Analytics Applications...............................................................................
25
1.2
2.
9
What is the business case for sustainability analytics? ...................................................................
2.1
Searching for the goals................................................................................................................
27
27
2.1.1
M ethodology.......................................................................................................................
28
2.1.2
Com pany 1: Pharm aceutical ..............................................................................................
28
2.1.3
Com pany II: Autom obile ..................................................................................................
29
2.1.4
Com pany Ill: Services ...................................................................................................
30
2.1.5
Com pany IV: Technology ................................................................................................
31
2.1.6
Com pany V: Internet...........................................................................................................31
2.1.7
Com pany VI: M anufacturing ............................................................................................
32
2.1.8
Com pany VII: Courier delivery services..........................................................................
32
2.1.9
Com pany Vill: M edical Equipm ent...................................................................................
33
2.1.10
Com pany IX: Chem icals.......................................................................................................34
2.1.11
Com pany X: Retail...............................................................................................................
7
34
2.2
3.
Analysis of the business cases................................................................................................
2.2.1
Ranking the business cases.............................................................................................
36
2.2.2
Finding the insights .........................................................................................................
38
W hat are the challenges of adopting sustainability analytics?.......................................................
5.
41
3.1
Tension betw een data collection and data analysis ..............................................................
41
3.2
Phases in big data analysis.......................................................................................................
43
3.2.1
Data acquisition and recording ......................................................................................
44
3.2.2
Inform ation extraction and cleaning ..............................................................................
45
3.2.3
Data Integration and Aggregation ................................................................................
46
3.2.4
Analysis ...............................................................................................................................
47
3.2.5
Interpretation......................................................................................................................48
3.3
4.
35
Tension between technical vs. organizational challenges and technical vs. adaptive change... 50
3.3.1
Technical Challenges.......................................................................................................
52
3.3.2
Organizational Challenges.............................................................................................
53
3.3.3
View ing sustainability analytics as a m ulti-stakeholder im itative ..................................
56
W hat is the role of the finance function?......................................................................................
59
4.1
View ing sustainability analytics as a strategic initiative .........................................................
60
4.2
Integration of sustainability and financial analytics ................................................................
61
4.3
Using the right m etrics............................................................................................................
63
4.4
Integration with business planning, and broaden the role of finance professionals .............
64
4.5
Com bining financial and sustainability expertise ..................................................................
66
Expanding the Three Vs ......................................................................................................................
5.1
68
Lim itations...................................................................................................................................70
Conclusion...................................................................................................................................................71
Annex ..........................................................................................................................................................
73
Annex 1: Innovation in data collection techniques ............................................................................
73
Annex 2: Educate - Explore - Engage - Execute ...............................................................................
74
Bibliography................................................................................................................................................75
W eb sources............................................................................................................................................75
Papers and publications..........................................................................................................................
8
78
Introduction
Big data and analytics are heralded as the next frontier of discovery, innovation and productivity. It is
estimated that by 2020, one third of all data will be stored, or will have passed through the cloud, and
we will have created 35 zetabytes worth of data'. Advocates claim that the numerous applications of
analytics in different industries are opening up infinite opportunities for organizations to transform its
practices and gain competitive advantages. This has led to rapid innovations in big data technologies
resulting in some applications of data that might not have been anticipated by experts. One of these
applications is the potential of big data to support and develop sustainability initiatives of organizations.
Organizations such as Nike, Ford and Novo Nordisk are beginning to harness large amounts of data
related to sustainability to gain insights about their organization's environmental and social impact,
deliver meaningful results for their sustainability initiatives and manage external risks associated with
sustainability regulations. This new field of expertise called sustainability analytics has become a
valuable tool in the hands of business leaders to manage the complexities associated with sustainability
related data and to measure and achieve the triple bottom line impact that organizations seek.
Sustainability analytics has the potential to enable organizations to measure, manage and analyze
sustainability performance, to turn insights into actions and to lend credibility to on-going sustainability
programs.
Although several companies have adopted sustainability analytics as part of their sustainability
initiatives, questions have been raised about its business case and its potential to make a significant
positive impact on business and sustainability performance. While sustainability analytics has received
praise from organizations that have found value in its adoption, these doubts have resulted from the
1CSC.com,
"Big Data Universe Beginning to Explode", (2012), last retrieved from the web on Dec 5, 2013:
http://www.csc.com/insights/flxwd/78931-bigdatagrowth justbeginning_to_explode
9
failure of several organizations to achieve the goals that were set for its implementation. Unfortunately,
the reasons behind these failures and the challenges associated with the adoption of sustainability
analytics have remained hidden due to several false assumptions made by organizations about where
the challenges lie and some core strategic mistakes that organizations make when evaluating the
characteristics of using analytics for sustainability.
In this paper, we will attempt to answer the following questions: (1) What is the relationship between
sustainability and analytics? (2) What is/are the business case(s) of sustainability analytics and what are
the challenges associated with its adoption? (3) What are some of the invalid assumptions that business
managers make with regards to using analytics for sustainability and what are the necessary conditions
for organizations to successfully adopt sustainability analytics?
In the thesis, I argue that necessary conditions for the successful adoption of sustainability analytics are
(1) A focus on and tackling the challenges associated with data collection as opposed to the emphasis
laid on data analysis (2) Recognition of the organizational challenges that form a critical road block to
success and (3) Integration of the roles of sustainability and finance functions. To tie things together, we
will propose a strategic framework that extends the traditional 3Vs of big data and addresses the key
issues exposed in this paper.
These arguments are supported by interviews with sustainability executives and research that revealed
that there were some unique challenges being presented by the application of analytics for
sustainability and that a fundamental shift in strategy was required to overcome the challenges that
organizations were facing in their pursuit of maximizing the benefits of using sustainability analytics.
10
1.
What is the relationship between sustainability and analytics?
In this chapter, to provide clarity about the meaning of sustainability analytics, we describe the
relationship between sustainability and analytics. We start with a brief discussion of what big data
analytics means, a review of the major milestones in the emergence of big data, the big data landscape
and current trends (1.1). In 1.2 we explore the three pillars of sustainability and its value to
organizations. To tie things together, we then discuss the emergence of sustainability analytics and what
it means.
1.1
Emergence of Big Data and Analytics
1.1.1
What is Big Data?
Experts claim that big data is revolutionizing the 2 1 't century without many knowing what it actually
means. There is no disputing that big data has the potential to offer unprecedented insights, better
decision-making and more comprehensive risk management. However, there is a lot of confusion over
the definition of big data. A research conducted by SAP revealed that of 154 C-suit executives who were
asked about the meaning of big data, a quarter believe it to be "technologies designed to handle the
massive amounts of data swamping organizations" 2. 28 percent defined big data as the "flood of data"
itself. Another 19 percent equated big data with "storing data for regulatory compliance" while 18
percent thought that big data relates to the "increase in data sources, including social networks and
mobile devices". This was confirmed during discussions with several senior executives in different
organizations that revealed that there are as many definitions of big data as there are people talking
about it 3 . This inability to understand the meaning of big data analytics is a problem - not only for
2 SAP.com
3 Based
on interviews conducted with executives in organizations in various industries
11
people buying and selling analytical services but also for leaders looking for opportunities to innovate
using big data. The word 'big' creates added confusion as data sets vary greatly in size and what is 'big'
or 'complex' for one organization may not be the same for another. Moreover, 'big' data sets today will
certainly be considered small tomorrow. A clearer understanding of the meaning of big data and what it
entails is required for managers to be prepared for the challenges that the various applications present.
We attempt to provide clarity to the definition of big data in order to avoid this confusion in later
chapters when we address its challenges.
Here are some definitions used by influential organizations:
Microsoft: "Big data is the term increasingly used to describe the process of applying serious computing
power-the latest in machine learning and artificial intelligence-to seriously massive and often highly
complex sets of information." 4
Oracle: "Big data is the derivation of value from traditional relational database-driven business decision
making, augmented with new sources of unstructured data."5
While the above definitions are generally true, in my research, I asked several executives to define big
data as they view it in their organizations. The terms that were most commonly used to describe big
data were: "analytics, large datasets, finding correlations and patterns, hidden answers, unstructured to
structured data, real-time analysis and data mining". These terms relate closely to the definition of big
data adopted by Gartner. In 2001, a research report published by META Group (now Gartner) analyst
Dough Laney defined data growth opportunities and challenges as being three-dimensional, i.e.
increasing volume (amount of data), velocity (speed of data in and out), and variety (range of data types
and sources). Many in the information technology industry continue to use this "3Vs" model for
4 TechnologyReview.com. "The Big Data Conundrum: How to Define It?", (2013), last retrieved from the web on
Dec 8, 2013: http://www.technologyreview.com/view/519851/the-big-data-conundrum-how-to-define-it/
s Ibid.
12
6
describing big data . In 2012, Gartner updated its definition as follows: "Big data is high volume, high
velocity, and/or high variety information assets that require new forms of processing to enable
enhanced decision making, insight discovery and process optimization."7
In this thesis, we will use Gartner's definition to dissect the 3Vs of big data and apply the analysis to the
use of sustainability analytics.
We look at an infographic that reveals some interesting facts about big data and establishes the
tremendous momentum with which it is growing.
Gartner.com, Beyer, Mark, "Gartner Says Solving 'Big Data' Challenge Involves More Than Just Managing Volumes
of Data", (2011), last retrieved from the web on Dec 5, 2013: http://www.gartner.com/newsroom/id/1731916
Laney, Douglas. "The importance of 'Big Data': A Definition", (2012), last retrieved from the web on Dec 6, 2013:
http://en.wikipedia.org/wiki/Bigdata
6
13
Asigra.com, Infographic on big data, "A look at some of the astounding facts and figures behind big data", (2013),
last retrieved from the web on Dec 20, 2013: http://visual.ly/what-big-data
8
14
1.1.2
Major milestones
it is often assumed that the concept of big data is relatively new and that analyzing data has become
critical for organizations in the last two decades. This leads organizations to believe that recent
technological innovations have enabled data to be more easily accessible making it vital for businesses
to find opportunities to convert that data into insights to gain competitive advantage. However, a
careful look at the history of big data reveals that data has been a central business tool for many
decades and that big data as a concept has actually been existent for far longer than one might think.
This is important for our analysis as it illustrates how the various uses of big data have slowly started to
emerge into more complex applications.
We briefly look at some of the major milestones in the evolution of big data analytics.
1890: US Government decides to perform a national census. Herman Hollerith came up with a novel
new Pantograph tabulating machine symbolizing the beginning of the mechanized data collecting age9.
1944: Fremont Rider, Wesleyan University Librarian, acknowledged the idea of big data when he
estimated that libraries in universities across the US would consist of 200,000,000 volumes. At present,
Yale Library alone has over 12.5 million volumes'0 .
1949: Known as the "Father of Information", Claude Shannon researched big storage capacities for items
such as punch cards. He also looked at the Library of Congress that measures over 100 trillion bits of
data".
Siliconangle.com, Mike Wheatley, "Five Biggest Milestones in the History of Big Data", (2012), last retrieved from
the web on Dec 20, 2013: http://siliconangle.com/blog/2012/10/09/five-biggest-milestones-in-the-history-of-bigdata/
10 Hcltech.com, Daniel Tuitt, "A History of Big Data", (2013), last retrieved from the web on Dec 20, 2013:
http://www.hcltech.com/blogs/transformation-through-technology/history-big-data
" Ibid.
9
15
1961: The "Law of exponential increase" was used by Derek Price to estimate that scientific journals had
doubled every 15 years12
1965: The first data center was conceived when the US Government attempted to transfer all tax
returns and finger prints to magnetic computer tape and storing it all on one big computer 3 .
1989: The World Wide Web was created by Tim Berners-Lee to leverage the internet to share and
search for information. "The information contained would grow past a critical threshold, so that the
usefulness [of] the scheme would in turn encourage its increased use," he wrote at the time 4 .
1997: The term "big data" is first used by researchers M. Cox and D. Ellsworth identifying that the
growth of data was becoming a problem for current computer systems's.
2001: Doug Laney publishes a research note "3D Data Management: Controlling Data Volume, Velocity,
16
and Variety." These "3 V's" become the defining dimensions of big data
2004: Hadoop, the free and open-source software program, is launched. "In the last eight years, Hadoop
has become so big that it controls entire search engines, determining everything from which ads they
show us, to which long-lost friends Facebook pulls out the hat, and even the stories you see on your
Yahoo homepage",17
2013: Businesses begin to adopt new in-memory technologies to analyze and optimize large quantities
of data. Organizations view data as a business asset to gain competitive advantage and to identify
opportunities and risks. The term 'internet of things' starts to become ubiquitous.
HcItech.com, Daniel Tuitt, "A History of Big Data", (2013), last retrieved from the web on Dec 20, 2013:
http://www.hcltech.com/blogs/transformation-through-technology/history-big-data
Siliconangle.com, Mike Wheatley, "Five Biggest Milestones in the History of Big Data", (2012), last retrieved from
the web on Dec 20, 2013: http://siliconangle.com/blog/2012/10/09/five-biggest-milestones-in-the-history-of-bigdata/
14 ibid.
is HcItech.com, Daniel Tuitt, "A History of Big Data", (2013), last retrieved from the web on Dec 20, 2013:
http://www.hcltech.com/blogs/transformation-through-technology/history-big-data
16 Ibid.
17 Siliconangle.com, Mike Wheatley, "Five Biggest Milestones in the History of Big Data", (2012), last retrieved from
the web on Dec 20, 2013: http://siliconangle.com/blog/2012/10/09/five-biggest-milestones-in-the-history-of-bigdata/
16
1.1.3
The big data landscape
There is definitely a lot of hype around big data. However, it cannot be denied that the amount of data
in the world is increasing exponentially, doubling every 18 months18 . Experts claim that big data is slowly
but surely changing the way companies do business. It is unravelling abilities to work with data in
different industries and unlocking its potential to make organizations more efficient, risk free and
profitable. One of the most striking trends has been the drastic increase in the number of Big Data
companies in different categories. Not surprisingly, recognizing the power of big data to gain powerful
insights, several of these companies are finding applications of big data for sustainability. To understand
the processes of big data analysis in the next chapter, we briefly discuss the vertical landscape of big
data.
The big data landscape depicted below by Dave Feinleib, author of the website bigdotalandscape.com
attempts to put together some of the many hundred companies now in the 'business' of analytics. These
are divided into different categories. Technologies such as Hadoop and Apache are open-source
software frameworks that allow for the storage and large-scale processing of data-sets on clusters of
commodity hardware. Infrastructure solutions provide the necessary tools to query the data that is
stored. In the last decade this is where the focus was to innovate and to unlock the possibilities of big
data. Finally, the applications are tools that convert unstructured data to data that can be used for
decision making, visualization and operating intelligence. Today, the focus and potential to innovate is at
the level of applications. From marketing to healthcare to legal services, there exist applications to
leverage big data for every industry.
ClO.com, Thor Olavsrund, "10 Real-World Big Data Deployments That Will Change Our Lives", (2013),
retrieved from the web on Jan 20, 2014: http://www.cio.com/slideshow/detail/92712
1
17
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1.1.4
The big data cycle - A systems dynamic view
Although data has been at the disposal of managers for several decades, it is only recently that it has
become more accessible. Based on my research and literature review of trends in Big Data, I created a
simple systems model (shown below) to explain why, both the amount and value of big data has been
growing at a considerable rate in the last decade and why its various applications have been increasing.
As data is increasingly generated from everything - buildings, vehicles, transit systems, mobile phones,
computers - there's a growing opportunity to capture it and make it useful. This opportunity creates
requirements and demands for better analytics technologies that can not only store this data but also
provide new insightful results. As new technologies emerge, data collection and storage techniques
improve thus improving the efficiency of data. As data becomes more organized and easy to use, novel
applications for big data are discovered in industries where data existed but was never analyzed. This in
18
turn increases the amount of big data being consumed and generated, thus closing the Big Data Cycle.
This reinforcing loop has led to the extensive growth of data over the last decade and continues to be
the primary driver of growth in big data analytics. Further research can be done to extend the
boundaries of this model with questions such as what impact does the increase in efficiency of big data
have on profitability of firms, what is the impact on cost, what time delays are existent and is there a
saturation point after which excess data becomes a burden.
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1.2
Emergence of sustainability analytics
To put the discussion about the value of sustainability analytics into context, we first briefly analyze the
significance of sustainability to organizations.
19
1.2.1
What is corporate sustainability?
Globalization along with technological advances has created immense opportunities for businesses
worldwide. While this has brought the world closer, it has resulted in shared problems that impact
everyone around the globe. Some multinational organizations are realizing that their bottom-line is
highly dependent on their sustainability goals. This is because dramatic changes in the global economic,
political and social forefront not only impact the economic objectives of companies but also shift their
goals from being primarily profit generators to also being sustainable organizations or socially
responsible companies. This major shift has resulted in the integration of sustainability into the core
strategies of organizations and the creation of the three pillars of sustainability: Environmental
Sustainability, Economic Sustainability and Social Sustainability. These three pillars combine
environmental, social, ethical, legal, financial and political issues that are both internal and external to
the company. They describe the drivers necessary for companies to set goals and milestones to address
these issues. As a result, many fortune 500 companies are looking to implement programs that target a
broad array of sustainability issues. According to a 2002 survey by PwC, "70 percent of global chief
executives believe that CSR is vital to their companies' profitability"' 9.
Another way to look at this is the concept of triple bottom line - people (social pillar), planet
(environmental pillar) and profit (economic pillar). A case in point is that of Novo Nordisk that claims to
have adopted the triple bottom line approach by taking a proactive stance on environmental issues.
According to Mckinsey & Company, Novo Nordisk partnered with local energy suppliers to realize energy
savings that account for 85 percent of its global carbon emissions. They claim that in three years this
initiative has eliminated 20,000 tons of carbon emissions and by 2014 green electricity will power all its
19
Vogel, David "Is There a Market for Virtue?", California Management Review, 2005
20
activities in Denmark. Using such initiatives Mckinsey claims to have helped Novo Nordisk not only
reduce its emissions and cut costs but also build Denmark's market for renewable energy 20.
Corporate sustainability therefore is a business approach that creates long-term consumer and
employee value by not only creating a "green" strategy aimed towards the natural environment, but
taking into consideration every dimension of how a business operates in the social, cultural, and
economic environment. By emphasizing that business goals are inseparable from the societies and
environments, within which they operate, corporate sustainability ensures that the three pillars of
sustainability are integrated into the organization's long-term strategy.
We now analyze a few cases of each of the pillars of sustainability to show how the triple bottom line
approach can be fundamental to an organization's long term strategy. It is important to note however
that although many organizations claim to have taken initiatives related to sustainability, it is difficult to
accurately assess their effectiveness. We use these examples to simply demonstrate the potential of
integrating the three pillars of sustainability as a basis for our discussion about the business case for
sustainability analytics.
1.2.1.1 People (The social pillar of sustainability)
The social pillar focuses on balancing the needs of the individual with the needs of the group2 1 . For
example, Walmart claims to have taken initiatives such as market oriented skills training for employees,
sustainable food donations, worker safety initiatives and women empowerment programs 2 2 . Nestle also
has similar initiatives that focus on water scarcity, wellness of communities near their factories and land
Mckinsey.com, McKinsey Quarterly, Sheila Bonini, Timothy M. Koller, and Philip H. Mirvis, "Valuing Social
Responsibility Programs", (2009), last retrieved from the web on Jan 25, 2014:
https://www.mckinseyquarterly.com/Valuingsocialresponsibilityprograms 2393
2 Truist.com, "The three pillars of sustainability", (2013), last retrieved from the web on Feb 5, 2014:
http://truist.com/the-three-pillars-of-sustainability/
20
22
ibid.
21
management that respects the rights of local people2 3 . Another example is Verizon that is attempting to
introduce energy saving technologies in schools, medical clinics and senior living facilities as well as
supporting community efforts through volunteer grants, disaster relief programs and cause collection
efforts where employees donate time, money and material goods24 .
1.2.1.2 Planet (The environmental pillar of sustainability)
The environmental pillar is aimed at processes, systems and activities that reduce the environmental
impact of an organization's facilities, products and operations 25 . Herman Daly, a pioneer in
environmental sustainability, suggested that
1)
For renewable resources the rate of exploitation should be less than the rate of regeneration.
2)
For pollution, the rate of generation should be less than the assimilative capacity of the
environment
For non-renewable resources, the depletion should require comparable development of
3)
renewal substitutes.
Walmart claims to have integrated the environmental pillar through initiatives like increasing imports
from 'green' factories, zero-waste goals, recycling programs and energy management systems
26
.
Similarly, Nestle manages its environmental sustainability initiative with a view on 4 priority areas:
water, agricultural raw materials, manufacturing and distribution, and packaging. Verizon has built
several programs related to telecom equipment recycling, energy reduction in offices, eco-friendly
solutions for fleet, green packaging and many other initiatives27 .
Truist.com, "The three pillars of sustainability", (2013), last retrieved from the web on Feb 5, 2014:
http://truist.com/the-three-pillars-of-sustainability/
24 Ibid.
23
2S
26
27
Ibid.
Ibid.
Ibid.
22
1.2.1.3 Profit (The economic pillar of sustainability)
Economic sustainability involves ensuring that the business makes a profit and also that the
organization's operations do not create any social or environmental issues that can adversely affect the
long term performance of the company. McKinsey supported a triple bottom line model for businesses
to address and tackle critical issues that lead to sustainable development2 8 . They believe that companies
that take a long term view use environmental, social and governance activities to create value in many
ways that support growth, improve returns on capital, reduce risk and improve management quality2 9.
1.2.2
What is Sustainability Analytics?
Having explored the meaning and emergence of big data analytics and established the relationship
between sustainability and organizational performance, we can now shift our focus to the relationship
between sustainability and analytics.
In an age of greater demand for both environmental and corporate accountability and when
organizations are increasingly focusing on sustainability issues, business leaders are finding that success
is being measured not just by financial results but also by social and environmental accomplishments.
The ability to manage sustainability performance requires a strong foundation that can measure the
environmental and social effects of a company's operations as well as analyze the data to find
opportunities to improve. The inherent complexity that this comes with requires that business leaders
are equipped with tools that can not only enable existing sustainable practices but also build new
initiatives. During my research3 , sustainability managers in several industries including apparel,
consumer goods, manufacturing and social networking, highlighted the importance of collecting and
Mckinsey.com, McKinsey Quarterly, Sheila Bonini, Timothy M. Koller, and Philip H. Mirvis, "Valuing Social
Responsibility Programs", (2009), last retrieved from the web on Jan 25, 2014:
https://www.mckinseyquarterly.com/Valuingsocial_responsibility-programs_2393
29 Ibid.
30 Based on interviews with sustainability managers in various companies
23
analyzing data to measure and act upon sustainability key performance indicators and results. The
common idea that they all agreed to is that there is a difference between a company that has a
sustainability program and one that is actually sustainable. This difference according to my interviewees
is measurement and the ability to find insights. Similar to other business goals, when there is data on
the results, it becomes much easier to review progress, set targets and to take corrective actions when
needed, especially when the issues at hand are both internal and external to the organization. Business
analytics and big data tools enable organizations to measure, manage and analyze sustainability
performance, and to identify actions that lead to their sustainability goals. Many companies are already
capturing sustainability related data such as greenhouse gas emissions, distances travelled by
employees and frequency of worker health hazards. The next logical step for these organizations is to
implement and use data analytics capabilities that can convert this raw data into actionable insights
such as the relationship of health hazards to profitability and the direct impact of reducing energy usage
in plants on the bottom line, and thus create the ability to make informed decisions about how to
approach the opportunity and risks that come with sustainability issues. Big data analytics can therefore,
be seen as the potential key to unlocking this ability of businesses to understand and act on the
environmental and social impacts that can be both, within and outside of their control.
Deloitte, who were amongst the first to use the term 'sustainability analytics', expect that the strong
and growing interest in analytics, together with the increasing emphasis on sustainability issues such as
climate change have led to a new field of expertise that integrates big data analytics with
sustainability1 . They define sustainability analytics as "an approach that aims to effectively use
technology to collect, disseminate, analyze, and use sustainability-related information across the
31
Deloitte.com, "Analytics for the Sustainable Business", last retrieved from the web on Apr 5, 2014:
http://www.deloitte.com/assets/DcomUnitedStates/Local%20Assets/Documents/IMOs/Corporate%2Responsibility%20and%20Sustainability/usdsccfbu
sinessanalytics_011711.pdf
24
enterprise.
n,32
It is important to note that this concept is often misunderstood as 'green IT' by many
organizations and experts. The difference between 'green IT' and 'sustainability analytics' is that while
green IT focuses on reducing the IT function's energy consumption and thus reducing the organization's
carbon footprint, sustainability analytics looks at the entire value chain, thus using technology as an
enabler for sustainability throughout the value chain. Sustainability analytics views the organization as a
member of a system of different inputs from where data is gathered for analysis and improvement.
1.2.3
Sustainability Analytics Applications
In 1.1.3, we discussed the big data landscape which consists of technologies that store and process data,
infrastructure tools that query and analyze data, and applications that turn data into insights.
Technologies and infrastructure tools are generic across different applications of big data and therefore
can be used for sustainability analytics as well. However, several applications focused on sustainability
analytics are now on the market that help transform sustainability information into action. These
applications integrate sustainability data to enable real time analysis and risk management which can
potentially help organizations save resources, proactively manage risk and continuously improve
products and solutions. They also can help organizations meet reporting, compliance, energy and
product safety needs and can be customized depending on different requirements of large and small
businesses.
It is important to recognize here that these applications are only as effective as the data that is supplied
to them. As discussed later in this paper, the efficacy of the data analysis phase is heavily dependent on
Deloitte.com, "Analytics for the Sustainable Business", last retrieved from the web on Apr 5, 2014:
http://www.deloitte.com/assets/DcomUnitedStates/Local%20Assets/Documents/IMOs/Corporate%2OResponsibility%20and%20Sustainability/ussccbu
sinessanalytics_011711.pdf
32
25
the data collection phases. Many organizations have implemented sustainability applications but failed
to maximize its potential due to the challenges associated with data collection.
SAP as part of its analytics solutions offers various such applications that claim to improve transparency,
help manage safety and environment, support regulatory requirements and unravel insights. The SAP
Environment, Health, Safety (EHS) management solution helps holistically manage operational, product,
and reporting risks across facilities33. The Energy and Resource management solution helps in
maximizing the return on the energy investments organizations make by correlating energy use with
production and reporting on energy used per site4. The Product Safety and Stewardship solution
provides insights to help design and deliver sustainable, safe, and compliant products, while meeting
regulatory and customer requirements and managing supply chain collaborations. Similarly, Oracle
offers solutions for risk and performance management, business operations and IT infrastructure that
measure organization's environmental impacts, and provides insights on how to reduce that impact and
manage risk36 . Another vendor that is focused on sustainability solutions is SolidWorks that offers a tool
that can integrate data to conduct life cycle assessments and assess the environmental impact of the
design and production process 37. It creates a dashboard for executives to make real time sustainability
decisions such as those related to materials sourcing and energy use.
SAP.com, SAP Sustainability Solutions, last retrieved from the web on Apr 1, 2014:
http://www.sap.com/solution/lob/sustainability/software/ehs-management-overview/index.htm
33
3
3s
ibid.
Ibid.
3 Oracle.com, Oracle Sustainability Solutions, 'Walmart Drives Sustainability with Oracle RightNow', last retrieved
from the web on Apr 2, 2014: http://www.oracle.com/us/solutions/green/it-infrastructure/index.html
3 Solidworks.com, "SolidWorks Sustainability", last retrieved from the web on Mar 10, 2014:
http://www.solidworks.com/sustainability/sustainability-software.htm
26
2.
What is the business case for sustainability analytics?
In this chapter, we investigate the business case(s) for sustainability analytics. We first analyze several
examples of the use of sustainability analytics to build a repository of business cases or goals that
organizations seek when implementing analytics for sustainability (2.1). In 2.2, we evaluate this
repository based on expected value before implementation and the realized value after implementation
and use of the tools. After ranking the goals and assigning scores to each goal we then look for insights
that the research unravels to evaluate the success or failure of achieving the business case(s) for
sustainability analytics.
2.1
Searching for the goals
Until relatively recently organizations found it difficult to understand the value of sustainability
analytics. This is because the information required to get an accurate understanding of something
relatively simple such as energy consumption was kept in different documents, formats and sites,
making it difficult to integrate all this information. Moreover, many organizations simply did not have
the capabilities to set up and measure sustainability indicators. Now, leading companies such as Nike
and Ikea are trying to better understand how to embed analytics into their sustainability initiatives to
achieve the triple bottom line benefits that were highlighted above. However, there are still questions
that need to be answered about the benefits of sustainability analytics for corporations when compared
to its perceived value. As with any new technology and its application, the potential of big data analytics
to resolve sustainability issues has been questioned by everyone including big data experts themselves,
and a systematic evaluation of its business case is necessary to identify its opportunities and pitfalls.
27
2.1.1
Methodology
In order to evaluate the business case for sustainability analytics, I first researched the potential "selling
points" of sustainability analytics through several case studies and interviews. This resulted in 8
variables or benefits that organizations look for when implementing big data for sustainability. We will
explore these variables in this section. Instead of challenging the claims that these organizations make,
we will use those claims to create a list of all "potential" selling points.
To investigate the expected and actual benefits realized based on these 8 potential benefits, I then
conducted a survey of 12 companies in different industries that are currently using analytical tools for
corporate sustainability. This resulted in the surfacing of the business cases for sustainability analytics
amid the confusion there is about its value. We will explore this in section 2.2.1. While the sample size
used was relatively small compared to the number of organizations who have implemented
sustainability analytics, the results gathered show a clear trend towards some challenges that helps
build a strong foundation to understand the challenges because of which some of the goals were not
being achieved. A study using a larger sample size is recommended to further strengthen the results
obtained in this paper.
2.1.2
Company 1: Pharmaceutical
A typical example of using sustainability analytics is Novo Nordisk, the Danish-based pharmaceutical
giant. Novo Nordisk was named as one of the world's 10 most sustainable companies by Corporate
Knights in 201438. According to Susanne Stormer, Novo Nordisk's vice president of corporate
sustainability and global stakeholder engagement, "It's about showing respect for people, the patients
that we're serving, and the people who work at the organization." They claim that this triple bottom line
GloballOO.org, "Global 100 Index, Corporate Knights Capital", (2014), last retrieved from the web on Mar 24,
2014: http://globalOO.org/global-100-index/
3
28
approach has enabled them to integrate reporting from 200439. The company integrates financial
reports with their environmental and social performance. According to Stormer, this not only helps set
targets but also keeps the organization accountable and makes sure that sustainability is a priority. "The
reason why we do that is because we think it's important that when you talk about sustainability, that
you don't just [tell] the tree hugger stories and the compelling emotional part of why a company should
be sustainable, but ... to hold ourselves accountable to our stakeholders and to drive internal
accountability," Stormer said, "so that when we set a target, whether it's a financial target or an
environmental target, we work towards achieving that target and we can track our performance,
whether its progressing or stagnating."4 This is possible primarily because of the amount of 'big data'
that Novo Nordisk is able to collect and analyze. The ability to use big data analytics for sustainability
issues such as to find trends in C02 emissions from energy, to analyze the effect on communities of
selling cheaper insulin, to quantify the intangible results of efforts in developing countries such as
training of doctors and to find the direct economic benefit of their operations on societies, is the
primary driver of Novo Nordisk's sustainability programs.
2.1.3
Company II: Automobile
Ford Motors claims to be one of the pioneers in implementing sustainability analytics. They have been
building complex mathematical models to sharpen its competitive edge while reducing its
environmental impact 41 . They have developed a model that uses its 'big data' to project C02 emissions
Businessinsider.com, Max Nisen, " Why The World's Most Sustainable Company Publishes C02 Emissions Next
To Its Earnings", (2012), last retrieved from the web on Mar 24, 2014:
http://www.businessinsider.com/measuring-sustainability-is-essential-2012-12
4 Businessinsider.com, Max Nisen, " Why The World's Most Sustainable Company Publishes C02 Emissions Next
To Its Earnings", (2012), last retrieved from the web on Mar 24, 2014:
http://www.businessinsider.com/measuring-sustainability-is-essential-2012-12
41 Sustainablebrands.com, Jennifer Elks, " Ford Utilizing Analytics, Big Data to Guide Sustainability Innovations",
(2013), last retrieved from the web on Mar 24, 2014:
http://www.sustainablebrands.com/newsvandaviews/infotech/Jennifer-elks/ford-utilizing-analytics-big-data39
guide-susta ina bi Iity-i nnova
29
generated by its vehicles on different roads for the next 50 years. Using this data they have been able to
set fuel economy targets as well as be eco-conscious. They have also used analytics to make decisions
between alternative engine designs thus building a case for the hybrid technologies that are not only
less harmful to the environment but have also created a differentiation factor for Ford. Ford considers
analytics and big data to be the next frontier in innovation, competition and productivity 42. Innovations
such as the Ford Fusion Energy plug-in generate 25 gigabytes of data every hour and helps in improving
fuel economy and reducing emissions43. Some other innovations that they have been working on are
'Green Routing', an analytical system that optimizes routes to reduce vehicle emissions near hospitals
and schools, life-cycle analysis tools that measure energy and water use associated with different
materials, and statistical analysis of vehicle usage data that gives insights about adoption rate of electric
vehicles4.
2.1.4
Company III: Services
Another example, one that is cited by Deloitte4 5, is of a company with 80 facilities around the world that
implemented an analytical solution to track its sustainability metrics. By collecting and analyzing data
related to the energy usage in different facilities and the travel time of its employees, it was able to
identify conspicuous consumption of large amounts of energy and higher than average travel times of
employees. The data also revealed insights that indicated opportunities to reduce this consumption,
thus enabling the company to reduce costs as well as its carbon footprint.
Sustainablebrands.com, Jennifer Elks, " Ford Utilizing Analytics, Big Data to Guide Sustainability Innovations",
(2013), last retrieved from the web on Mar 24, 2014:
http://www.sustainablebrands.com/newsandviews/info-tech/ennifer-elks/ford-utilizing-analytics-big-dataguide-sustainability-innova
42
43
ibid.
44Ibid.
4s Deloitte.com, "Analytics for the Sustainable Business", last retrieved from the web on Apr 5, 2014:
http://www.deloitte.com/assets/DcomUnitedStates/Local%20Assets/Documents/IMOs/Corporate%2Responsibility%20and%20Sustainability/usdsccfbu
sinessanalytics_011711.pdf
30
2.1.5
Company IV: Technology
IBM uses big data analytics in its environmental, social and governance programs such as its Small and
Medium Enterprise (SME) toolkit to develop a good reputation with local stakeholders such as
government officials and NGOs
.
In partnership with the World Bank's International Finance
Corporation, IBM provides free web-based resources on business management to small and midsized
enterprises in developing countries. All this not only improves IBM's reputation in new markets but also
helps develop relationships with future customers. Further, by focusing on sustainability programs, IBM
has also been able to create new business opportunities through new analytical products such as green
data centers solutions4 7 . IBM partners with the Nature Conservancy to develop 3D imaging technology
to help improve water quality. This program uses IBM's sensor capabilities to provide decision makers
with summary information to help in water management. While this is addressing environmental issues,
it is also building IBM's capabilities.
2.1.6
Company V: Internet
An interview with a sustainability manager at a large online social networking service revealed that
sustainability analytics can even be used by internet companies. By analyzing large quantities of data
related to energy consumption in their data centers, this organization is taking several initiatives to cut
down on energy usage and find opportunities to better utilize and expand its data centers. Data centers
consume 1.2 percent of global power8- a staggering number. The number of data centers built around
4 Deloitte.com, "Analytics for the Sustainable Business", last retrieved from the web on Apr 5, 2014:
http://www.deloitte.com/assets/DcomUnitedStates/Local%20Assets/Documents/IMOs/Corporate%2Responsibility%20and%20Sustainability/ussccbu
sinessanalytics_011711.pdf
47
Ibid.
Triplepundit.com, Richard Jenkins, "The Sustainable Data Center of the Future", (2013), last retrieved from the
web on Mar 25, 2014: http://www.triplepundit.com/2013/02/sustainable-data-center-future/
4
31
the world is expected to double by 2016 49and while data centers are becoming more energy efficient,
this is a huge increase in power consumption along with heat and C02 emissions. This company is
addressing this issue by collecting large amounts of accurate data that can empower decision makers to
make changes proactively and in real-time. Also, by integrating these results in financial metrics, they
are being able to monitor and manage financial compliance, measure asset capitalization timelines and
strategically forecast. Their goal is to ultimately use the power of analytics to build data centers that are
self-sustainable and that can drastically cut down costs as well as their carbon footprint.
2.1.7
Company VI: Manufacturing
An interview with a global electronics manufacturer revealed the use of sustainability analytics in the
supply chain. By collecting data about the different materials used in different products and their
properties, this company is analyzing their carbon footprint from bottom-up. Gaining from insights
about the recyclability of materials used in different products, the location of suppliers and the
environmental impacts of each material upon sending to landfill, this company is able to make decisions
about switching to more environmental friendly raw materials. Further it is also being able to cut down
large amounts of costs related to transportation and recycling50 .
2.1.8
Company VII: Courier delivery services
FedEx, using sustainability analytics has been able to decouple growth and resource consumption
through sustainability initiatives that serve the environment as well as business. Using the data gathered
from millions of deliveries in each city, they have developed an Eco-Driving program designed to lower
vehicles' effect on the environment by helping drivers change their daily driving habits. Moreover, FedEx
Triplepundit.com, Richard Jenkins, "The Sustainable Data Center of the Future", (2013), last retrieved from the
web on Mar 25, 2014: http://www.triplepundit.com/2013/02/sustainable-data-center-future/
50 Based on data gathered during interview conducted with a multinational electronics manufacturer
49
32
has also implemented programs that lead change at a community level. In Mexico City, FedEx partnered
with EMBARQ 51 to create an environmentally responsible transportation system that eliminated 114,000
tons of carbon emissions every years2. FedEx's strategy to overcome the 'capability trap 3' is to have a
seamless overlay of sustainability goals on top of the traditional business goals of maximizing profit and
shareholder value, and creating product differentiation. This is especially relevant to the courier industry
that is suffering from volatile fuel prices and high operating costs leading to shrinking margins and a
necessity to continuously differentiate. Several initiatives with the help of sustainability analytics have
led to significant cost reductions for FedEx through savings in energy consumption, fuel usage and
landfill costs.
Interviews conducted with DHL and UPS revealed similar sustainability initiatives with the help of
analytics that makes their business more productive and efficient. UPS for example, installs sensors
throughout their vehicles that generate data that help plan smarter routes and educate drivers about
fuel-efficient driving techniques. Each night they upload the day's driving data to find opportunities to
get more efficient and to optimize vehicle maintenance schedules to keep them running clean and
safe54.
2.1.9
Company VIII: Medical Equipment
Varian Medical Systems, a Silicon Valley-based company that creates technology for treating cancer uses
SAP's sustainability solutions and product life cycle management tools to analyze the chemical
composition of its products to redesign them, eliminate hazardous substances and comply with
51 Environmental Leader.com, "FedEx to Fund Transportation Projects in Mexico, Brazil, & FedEx", (2011), last
retrieved from the web on Apr 6, 2014: http://www.environmentalleader.com/2011/12/21/fedex-to-fundtransportation-projects-in-mexico-brazil-india/
s2 FedEx Earthsmart website, last retrieved from the web on Apr 6, 2014: http://earthsmart.van.fedex.com/
s3 MIT, Repenning and Sterman, "Nobody Ever Gets Credit for Fixing Problems that Never Happened", (2001),
California Management Review Vol. 43, No. 4, Summer 2001
s4 Urbantimes.co, UPS Corporate Responsibility, "Continuous Technology Innovation Infographic", last retrieved
from the web on Apr 6, 2014: http://urbantimes.co/2013/01/brown-is-the-new-green/
33
regulations. Using sustainability analytics has not only helped them be more environmentally friendly
but also safeguard their revenue in the European Union where regulations are much stricter than the
United States. They are now re-engineering products at a faster pace and predict that the ROI from the
use of the SAP solutions is anticipated to be $2.5 million5 5 . Further, it has helped them understand and
meet the expectations of different stakeholders.
2.1.10 Company IX: Chemicals
Dow Chemical, the second largest chemical manufacturer in the world, uses SAP's Environment, Health
and Safety (EHS) management solution to integrate latest regulatory, product and cost information that
helps them be more agile with changing environments. Besides cutting down on costs, it also helps them
standardize processes through sustainable allocation of resources based on the insights gathered from
the data 6 .
2.1.11 Company X: Retail
Walmart, the largest retailer in the world, has adopted Oracle's 'RightNow' sustainability solution, to
capture the data from their various sustainability initiatives. This cloud based solution gathers data
throughout the supply chain and not only helps executives make decisions with regards to cutting down
on energy usage and evaluating the carbon impact of each product, but also manage supplier
relationships through wider transparency and easier communication57
ss Smartplanet.com, Rachel King, 'Varian Medical Systems explains how it is using SAP software to support both its
sustainability and compliance efforts.', (2012), last retrieved from the web on Mar 25, 2014:
http://www.smartplanet.com/blog/smart-takes/sap-publishes-sustainability-report-touts-compliance-methods/
SAP.com, SAP Sustainability Solutions, Dow Chemicals, last retrieved from the web on Apr 1, 2014:
http://www.sap.com/solution/lob/sustainability/software/ehs-management-overview/index.html
s7 Oracle.com, Oracle Sustainability Solutions, 'Walmart Drives Sustainability with Oracle RightNow', last retrieved
from the web on Apr 2, 2014: http://www.oracle.com/us/solutions/green/it-infrastructure/index.html
56
34
2.2
Analysis of the business cases
The following goals/benefits/selling points/business cases were identified based on interviews with
sustainability managers and the cases researched including the ones identified above:
i)
The ability to cut costs through the use of innovative sustainability analytics tools - Cost
reduction
ii)
The ability to identify internal and external risks related to sustainability within an organization -
Risk management
iii)
The ability to create a differentiating factor and find opportunities to create new products and
enter new markets - Differentiation and Profit building
iv)
The ability to make decisions and select different courses of actions that address sustainability
issues - Decision Making
v)
The ability to understand and anticipate the relationships between economic, social and
environmental factors that are connected to an organization's value chain - Preparedness
vi)
The ability to allocate resources within the organization effectively in order to have the
maximum impact - Resource allocation
vii)
The ability to understand the different requirements and expectations of stakeholders within
the larger community, and to design programs that address all those issues - Stakeholder management
viii)
The ability to measure sustainability key performance indicators and to detect the efforts that
are achieving the desired results - Measurability
35
2.2.1
Ranking the business cases
Sustainability leaders in 10 organizations58 were then asked to rank these 8 'business cases' based on
their organization's perceived (before implementation) and actual (after implementation) value of using
sustainability analytics tools. The respondents were instructed to indicate a score of 0-10 for each of
these variables. They were explicitly asked to indicate a score of 0 for goals that were not realized or not
expected. The survey was followed up with a conversation with some of the respondents to understand
the reasons why some of the expected values were not realized. The interviews resulted in the following
order of ranking based on expected and actual value for the 8 business cases. Upon aggregation we
compare the perceived and realized value to identify the goals that were achieved or not achieved.
Rank
Rank
ased b
an
based on
on
expected
actual exece
value
1
2
3
Goals of
implementation/Benefits
of adoption
Perceived Value
before
implementation
(Cumulative
score)
Realized Value
after
implementation
(Cumulative
score)
Goal
Achieved/Not
Achieved
1
The ability to measure
sustainability key
performance indicators
and to detect the efforts
that are achieving the
desired results.
(Measurability)
89
92
Achieved
2
The ability to identify
internal and external risks
related to sustainability
within an organization.
(Risk Management)
84
85
Achieved
7
The ability to create a
differentiating factor and
find opportunities to
create new products and
enter new markets.
(Differentiationand
ProfitBuilding)
47
75
Achieved
5 The organizations that participated in the survey were medium and large companies of at least
300 employees, 8
public and 2 private companies and with mature corporate sustainability programs.
36
4
3
The ability to cut costs
through the use of
innovative sustainability
analytics tools. (Cost
Reduction)
78
64
Not Achieved
60
52
Not Achieved
61
50
Not Achieved
59
40
Not Achieved
stakeholders within the
larger community, and to
design programs that
address all those issues.
(Stakeholder
management)
58
30
Not Achieved
Total
536
488
Not Achieved
The ability to make
decisions and select
5
5
different courses of
actions that address
sustainability issues.
(Decision making)
The ability to allocate
resources within the
6
4
organization effectively in
order to have the
maximum impact.
(Resource allocation)
The ability to understand
and anticipate the
relationships between
7
7
economic, social and
environmental factors
that are connected to an
organization's value
chain. (Preparedness)
The ability to understand
the different
requirements and
expectations of
8
6
37
2.2.2
Finding the insights
The survey results led to some interesting insights about the value of sustainability analytics as
perceived and actually realized by organizations. As seen in the value chart below, three goals measurability, risk management, differentiation - were met. This was true for all the organizations that
responded. However, none of the other 5 goals were realized. This was also true for all the organizations
that responded.
Value Chart
100
90
80
70
60
-
50
50
Value
*" Perceived
Perceived Value
40
40
*" Realized
Value
Realized Value
30
20
10
0
1
2
-
-
3
-
4
5
6
7
8
Goals/Benefits ranking (Realized value)
The results indicate that the ability to measure key performance indicators related to sustainability
programs, the ability to manage risk associated with external elements and the prospects of cost
reductions were the primary objectives of implementing the analytical tools. This was confirmed by
respondents during debriefing who found these three as the primary triggers for their organizations to
approve a budget to implement sustainability analytics.
38
An interesting insight was that the goal with the third highest score of realized value and that was
achieved (differentiation and profit building) was in fact the least expected objective before
implementation. Upon discussions with some of the managers, it was discovered that although profit
building was not an initial expectation from the tools, organizations were quickly able to find
opportunities in new markets and products due to the insights gathered from sustainability analytics.
The analytical tools were able to unlock clues about potential opportunities that were not apparent
from the sustainability programs in place by itself. This suggests that managers initially do not
understand the triple bottom line approach of sustainability and do not recognize that sustainability
programs and tools not only support the social and environmental pillar but also the economic pillar of
sustainability. Sustainability managers must take into consideration the contribution of sustainability
analytics in business development and profit building when making decisions about implementing new
tools. This awareness can not only lead to a better evaluation of the different tools on the market but
also ensure that the business is prepared to take advantage of the opportunities that surface.
We observe that the overall objective of implementing sustainability analytics was not met. This was
confirmed upon discussions with the respondents, majority of who believed that the overall
expectations from sustainability analytics had not been met. Although the top two goals were achieved,
majority of the others including the third most anticipated after goal - cost reduction - were not met.
Given the number of successful cases cited by vendors and organizations using analytics, including the
ones identified in this paper, this result was unexpected and might sound biased. There could be several
possible reasons for this variance. Sustainability analytics is usually one of several sustainability
initiatives implemented simultaneously. As organizations discover the need to strengthen their
sustainability benchmark, initiatives are implemented to benefit the entire value chain - many of which
are not related to sustainability analytics. This can blur the individual results of different initiatives - a
sort of free riding problem that many organizations face when adopting several tools all at once. It is
39
also possible that since cases published in the press and on company websites are aimed at showcasing
sustainability initiatives and building organizational reputation, the success of sustainability analytics is
exposed to bias. Another reason for the possible variance is organizations' preconception that since a
tool is successfully working for another organization in the same industry or situation, it could simply be
a case of adjusting expectations in terms of timeline and level of benefits expected. Finally, there could
also be a case of survivor bias by sustainability managers who only concentrate on the goals that were
achieved, inadvertently overlooking those that did not because of their lack of visibility. Moreover, a
survivor bias by vendors and the media can lead to the false impression of a universal success story of
sustainability analytics. All these reasons can contribute to the lack of publically open stories about
failure of sustainability analytics. Individual discussions with sustainability managers are required to
determine credible results and to get a better understanding of the success or failure of sustainability
analytics that is yet to become a mainstream tool for sustainability.
In the next chapter we analyze the critical challenges that contribute to the failure of meeting the goals
of implementing sustainability analytics and the factors that sustainability managers must consider in
order to overcome the obstacles of adopting these tools.
40
3.
What are the challenges of adopting sustainability analytics?
In this chapter, we identify the challenges that can lead to the failure of sustainability analytics in
enhancing the sustainability initiatives of organizations. In 3.1 we analyze the tension between data
collection and data analysis that can lead managers to making incorrect assumptions about where the
obstacles lie and where innovations are necessary to overcome those obstacles. To substantiate this
discussion, in 3.2 we explore the different phases of big data, their individual challenges and possible
solutions. In 3.3, we address another assumption made by sustainability managers about the technical
and organizational challenges that are faced when using sustainability analytics. We compare this
tension with that between technical and adaptive challenges to establish the complexity of sustainability
analytics as a multi-stakeholder initiative.
3.1
Tension between data collection and data analysis
As discussed in chapter l and in the discussion of the emergence of sustainability analytics in chapter 2,
the promise of data-driven analysis and improvement of sustainability programs is now being
recognized broadly, and there is growing enthusiasm for the notion of "sustainability analytics" or "big
59
data for sustainability". While this promise is real, for example, according to research conducted by
SAS almost 46% of C-level executives believe that analytics can have an impact in the area of
sustainability, there is currently a wide gap between the expected and realized potential of sustainability
analytics. This was indicated in chapter 2 by demonstrating that although managers claim that some of
the major goals of implementing analytics for sustainability were achieved, analytics failed to deliver the
overall expectations from its application.
SAS, BusinessWeek Research Services, "White paper on Emerging Green Intelligence", Business Analytics and
Corporate Sustainability, (2009)
59
41
The statistic that 90%60 of the 2.7 Zeta bytes61 of data that exists in the universe today has been created
in the last two years and that 80%62 of all data is stored by enterprises, suggests that we are awash in a
flood of data today. The big data cycle is resulting in the doubling of the volume of business data every
1.2 years. Trillions of sensors and data recorders everywhere from electric bulbs to vehicles to software
capture structured and unstructured data that can be analyzed for different purposes. All this has
created the presumption that the challenges with sustainability analytics lie in data analysis. This results
in many organizations focusing solely on overcoming the challenges of the analysis phase while
neglecting the obstacles that the data collection phases present. Interviews with sustainability managers
after the survey revealed that the primary reasons for not being able to effectively use sustainability
analytics to make accurate decisions that could lead to cost savings or efficient resource allocation was
not because of a lack of understanding of how to analyze the data but due to the failure to capture
relevant data effectively, the inability to convert unstructured data to structured data and the lack of
understanding of how to handle uncertainty, and error due to noisy or heterogeneous data. This is
further discussed in later sections. For example, a large internet company indicated that the inability to
gather data from thousands of equipment installed in different data centers was the key issue facing the
sustainability group. A sustainability executive from a luxury apparel company expressed major concerns
about the "lack of understanding by the organization's different departments to locate, capture and
aggregate sustainability related data".
Even in the analysis phase, there were complexities that were not understood well. Upon data
collection, several executives found it difficult to aggregate dissimilar data to find valuable insights.
Baselinemag.com , Dennis McCafferty, "Surprising Statistics about Big Data", (2014), last retrieved from the web
on Apr 4, 2014: http://www.baselinemag.com/analytics-big-data/slideshows/surprising-statistics-about-bigdata.html
6
6
Wikibon.org, "A comprehensive List of Big Data Statistics", (2012), last retrieved from the web on Apr 4, 2014:
http://wikibon.org/blog/big-data-statistics/
6 Baselinemag.com , Dennis McCafferty, "Surprising Statistics about Big Data", (2014), last retrieved from the web
on Apr 4, 2014: http://www.baselinemag.com/analytics-big-data/slideshows/surprising-statistics-about-bigdata.html
42
Manual collation and interpretation of the data was time-consuming and led to several
misinterpretations.
This tension between data analysis and data collection exists in every organization that has
implemented sustainability analytics. An understanding of this tension prior to adopting sustainability
analytics will enable sustainability managers to be better prepared to face the challenges that the tools
present and to take advantage of the opportunities that the solutions can unravel.
3.2
Phases in big data analysis
To analyze the causes of failure to meet the goals set for sustainability analytics, we take assistance of
the distinct phases of big data analysis. These phases apply to all big data applications, whether
sustainability or not, and allow us to independently analyze the challenges that each phase inherently
introduces along with the issues that arise due to its specific application of sustainability. Although
organizations tend to have their own specific processes in place depending on industry, geographical
reach, size of operations, etc. to collect and analyze data, these phases summarize the overall process
that all organizations go through.
The following are the distinct phases of big data analysis:
63
63
Adapted from The Computing Research Association, Big Data White Paper, "Challenges and
Big Data", (2012)
43
Opportunities with
3.2.1
Data acquisition and recording
The data acquisition and recording phase deals with identifying the data generating source and
establishing the process to gather and store data. For example, a hospital gathers data about the heart
rate of patients to better understand the effectiveness of various procedures, a manufacturing firm
collects data about the presence of toxins in the air in factories and a retail store collects data about the
amount of energy used by each of its refrigerators.
All this data generated from just a single data source can easily produce terabytes of raw data every day.
Much of the data gathered in the data acquisition phase might be of no interest and can be filtered and
compressed using database tools". However, the challenge here is to define these filters in a way that
the useful information is not discarded. For example, if the energy usage data from one refrigerator
within a store is taken as representative for the purposes of analysis, how can we be sure that other
variables such as location within the store, the number of times the refrigerator is opened, the kind of
food stored, etc. do not matter? In addition, the data collected is often spatially and temporally
correlated (e.g. traffic sensors on the same road segment) 65. Innovative data analysis techniques are
required that can reduce the size of data to a limit that users can handle while not missing the needle in
the haystack66 . Further, organizations such as the CRA have stressed about the need for "on-line"
analysis techniques that can process data on the fly since organizations cannot afford to store data first
and then reduce to analyze6 7 . These problems are often experienced when using sustainability analytics
tools. Interviews with sustainability managers revealed that although the location of the data source
6
The Computing Research Association, Big Data White Paper, "Challenges and Opportunities with Big Data",
(2012), Page 5
Ibid.
Ibid.
67 Ibid.
65
66
44
was easy to detect, it was not clear what filters to apply so that the right quantity and quality of data
was captured given the limitations of data storage.
Another challenge with the data acquisition phase is the ability to automatically collect data. While it
might be easy to collect energy usage data from devices in a data center, it is extremely difficult to
identify data sources to collect accurate data related to employee satisfaction levels and impact of
manufacturing operations on surrounding areas. There are also several technical obstacles with regards
to collecting data from sources that are not compatible to existing technologies. To make sustainability
analytics more effective, the human burden of gathering data has to be reduced.
Finally, the scale of data is another challenge. The name "big" data is given to analytics for a reason.
Managing large and rapidly increasing volume of data is an issue. Although storage space is inexpensive,
it is important that organizations anticipate and budget for the expansion in storage capabilities over
time. Further, the 'bigger' the data, the more storage space is required. Storage space in the form of
data centers consume large amounts of energy to keep the systems turned on and water to cool the
facilities. This 'side effect' of sustainability analytics is often ignored and can be significant enough to
totally invalidate its initial business case. Sustainability managers must understand these impacts on the
larger system within which their organization sits and evaluate the amount of data that can be stored
for the investment to be worth it.
3.2.2
Information extraction and cleaning
Often the data that is extracted is not in the format that can be analyzed. For example, consider the data
that is in the form of images or diagrams or geo-positional location. Important data in these forms
cannot be left in their source formats. This phase extracts data, pulls out the required information and
expresses it in a format that can be analyzed. Achieving this task is a difficult challenge. It requires a
thorough understanding of the kind of data and its requirement. Research indicated that sustainability
45
managers were not considering important data in different formats due to the cumbersome exercise of
cleaning it to an understandable format. Further, organizations often think that sustainability data
simply means environmental data such as energy usage or carbon emissions. Data related to the social
impact of organizations such as health hazards, treatment of workers, contribution to community
development, etc. are often ignored and fail to make it to the data analysis phases. Organizations need
to create systematic data entry points related to environmental, social and employee performance to
capture and assess over sustainability performance. Some vendors such as Cloudapps offer solutions
that can effectively collect raw data from multiple sources (See Annex 1).
Also, it is often assumed that data always tells the truth. However, this might not always be correct. For
example, questionnaires or surveys collected from customers, employees and suppliers might not be
clear. Also, people might choose to hide information and provide inaccurate records. This human
obstacle of gathering data was a common feedback from many organizations that were interviewed.
Hence, extracting the most relevant data in a format that is reliable and that can be analyzed is critical
for data analytics to achieve its desired results.
3.2.3
Data Integration and Aggregation
Humans can comfortably collect and integrate information that is highly heterogeneous. However,
analytical tools expect homogeneous data. Therefore, data has to be carefully structured before it can
be analyzed. Further, heterogeneous data that is collected might not have any value unless it is
combined with other data that it is related to. Simply throwing it into a database is not enough for the
users to make sense of the information. The data integration phase aggregates and integrates the data
points into meaningful information that can be analyzed by users.
46
The challenge here is to maintain heterogeneous data in similar formats that can be aggregated.
Different data points can have different levels of structure. For example, surveys collected from factory
workers are far more unstructured than the data collected about energy usage in offices. Also, data can
differ across geographies. For example, due to differences in standards of units and the format of
storage in various locations across the world, an organization that was interviewed found it difficult to
make comparisons when data was unformatted.
Automation of this process is important to driving greater consistency and accuracy, as well as yielding
cost savings. A challenge to automating sustainability performance management is addressing data
governance for sustainability related data. To overcome this challenge it is important that automation is
viewed as an end-to-end process from initial acquisition of data to presentation of information to users
for analysis -just like any other financial data.
3.2.4
Analysis
The analysis process requires a good understanding of the data that has to be evaluated and the
acquisition of tools that can be used for effective analysis. Several business intelligence tools currently
being developed and sold by software companies such as SAP and Oracle have the capabilities to
analyze the data and provide insights. One of the challenges is to pick the right tool that fits the
requirements of the organization, the amount and type of data being collected and the skills of users
that will use the tool. Although most of the tools provide similar capabilities, a closer look reveals that
certain tools might be more suitable for specific industries. For example, Sedex offers a tool that allows
for data exchange between organizations and their suppliers in order to ensure sustainable and ethical
supply chains6.
6
Sedexglobal.com, Home page, last retrieved from the web on Apr 4, 2014: http://www.sedexglobal.com/
47
Another challenge with analysis is that even after data cleaning and aggregation, there can be some
level of data incompleteness. This incompleteness can be managed at the analysis stage by making
appropriate assumptions and using probability analysis.
Scale of data is also an issue for the analysis phase. The larger the scale, the more processing power is
required. Although there have been significant innovations in processor speeds, the high energy
consumption of powerful processors and sheer increase in size of big data across organizations has
raised debates about the benefits of sustainability analytics as compared to its costs. To overcome some
of these problems, organizations such as EMC are offering cloud computing analytical tools that are cost
efficient and consume far less energy.
Finally, the larger the data set that there is to analyze, the longer it will take. Analytical tools must be
designed to effectively deal with size of data. This is important when the results are required instantly.
For example, organizations trying to act upon potential health hazards to employees require fast
analysis of the data that is collected. Although there have been many advances with indexing databases
to deal with this issue, managers must be aware of the size of data that is being analyzed and set
appropriate limits.
3.2.1
Interpretation
Big data analysis, whether related to sustainability or not, as little value if the intended users cannot
understand the analysis. Interpreting the results is important for making decisions. This involves
examining the different assumptions made along the different phases and tracking the analysis. Often, it
becomes increasingly difficult for users to evaluate assumptions as far back as the data extraction phase.
This requires proper documentation of the process and an efficient knowledge transfer process. Thus,
simply providing the results to the decision make is not enough. This should be accompanied with
48
supplementary information such as how the result was derived and what were the limitations
considered.
Further, visualizations make it easier to interpret large sets of data. Tools that can enable meaningful
visual representations of the information can add considerable value to the decision making process.
Finally, it is important for the ultimate user of the data to be able to drill down into the input data
behind the analysis. This assists in understanding the raw data and its source, and in overcoming
incorrect assumptions.
Finally, as the authors of a "The Parable of Google Flu: Traps in Big Data Analysis", explain, there is the
problem of "big data hubris"69. David Lazer and his colleagues explain that "Big data hubris is the often
implicit assumption that big data are a substitute for, rather than a supplement to, traditional data
collection and analysis". Google scientists in 2009, announced that "...we can accurately estimate the
current level of weekly influenza activity in each region of the United States, with a reporting lag of
about one day"70 . The assumption that google made was that people with the flu (the influenza virus)
will probably go online to find out how to treat it, or to search for other information about the flu. So
Google would be able to track such behavior, hoping it might be able to predict flu outbreaks even faster
than traditional health authorities such as the Centers for Disease Control (CDC). Ironically, just a few
months after announcing Google Flu, the world was hit with the 2009 swine flu pandemic, caused by a
novel strain of HIN1 influenza.
In fact, Google Flu was wrong for 100 out of 108 weeks since August
2011. A critical mistake that Google made was to assume that it is as easy to predict and interpret the
future as it is to analyze the past. The problem was that most people who think they have "the flu" do
"The Parable of Google Flu: Traps in Big Data Analysis", David Lazer, Ryan Kennedy, Gary King, Alessandro
Vespignani, (2014), Sciencemag.com
70 "Detecting influenza epidemics using search engine query data", Jeremy Ginsberg, Matthew H.
Mohebbi, Rajan
S. Patel, Lynnette Brammer, Mark S. Smolinski & Larry Brilliant, (2009), Nature.com
71 Forbes.com, Steven Salzburg, "Why Google Flu is a Failure", (2014), last retrieved from the web on Apr 30, 2014:
http://www.forbes.com/sites/stevensalzberg/2014/03/23/why-google-flu-is-a-failure/
69
49
not. The vast majority of doctors' office visits for flu-like symptoms turn out to be other viruses. CDC
tracks these visits under "influenza-like illness" because so many turn out to be something else. To
illustrate, the CDC reports that in the most recent week for which data are available, only 8.8% of
specimens tested positive for influenza. When 80-90% of people visiting the doctor for "flu" don't really
have it, you can hardly expect their internet searches to be a reliable source of information.
From the above analysis of the phases in big data analytics, it is clear that the challenges associated with
its use lie in both data collection as well as data analysis. A common mistake that organizations make is
to ignore the challenges associated with the data collection phase on the presumption that data is
ubiquitous and ready to be analyzed in the form that it is sourced. This leads to many of the failures in
achieving goals set for sustainability analytics as outlined above. It is imperative for organizations to be
aware of these challenges so that they are better prepared and equipped with the skills necessary to
tackle both data collection and analysis phases.
3.3
Tension between technical vs. organizational challenges and technical
vs. adaptive change
Another lens to look at the challenges that organizations face while implementing sustainability
analytics tools is from the perspective of the type and intensity of the complexities. Similar to the
tension between data collection and data analysis, the assumption that many organizations make is that
big data and analytics tools are highly mechanical and therefore the challenges associated with it would
be primarily technical in nature. While it is true that technical challenges are amongst the tallest hurdles
in the use of sustainability analytics, it is the lack of ability to view the organizational challenges that
come with using big data analysis that often lead to its failure.
These organizational challenges are further accentuated when analytics is used for sustainability.
Interviews with sustainability managers revealed that the questions asked by CEOs and CTOs while
50
building a business case for sustainability analytics were almost all related to the technical preparedness
of the organization's IT function. This was apparent from the budgets allocated to enhancing IT skills
when evaluating these tools. Considerations such as the ability to adopt change and overcome
resistance, the ability to understand data, privacy and coordination between business functions were
not discussed prior to implementation. It was only after the tools were implemented that the
organizations encountered issues related to change management, user privacy and internal
collaboration that exposed the organizational weaknesses and the inability to tackle adaptive
challenges.
It is vital for sustainability managers to differentiate organizational challenges from technical challenges.
An alternative view to look at the root cause of this tension is by understanding the differences between
technical changes and adaptive changes. Using big data analytics is often considered as technical
changes within organizations. The decision to implement analytical tools usually lie with the CTO. This
was confirmed by 3 interviewees who indicated that the recommendation to use analytics was made by
the sustainability managers but the decision was made by the CTO on the basis of the budget allocated
and skills available within the IT function.
Technical change involves people putting in place solutions to problems for which they know the
answers or for which technical skills can be developed.
While this is difficult, it is often not as difficult
as adaptive change, which involves addressing problems for which they don't know the solutions yet.
There is no authoritative expertise or standard operative procedures to solve these problems 7 3 . These
adaptive changes involve changing more than just routine behaviors or preferences and challenges,
people's habits, beliefs, and values7 4. It demands people to take a loss and experience uncertainty. It can
Adapted from Fullan (2003, 2005) who cites Heifetz and Linsky (2002)
73
Ibid.
4 Adapted from Fullan (2003, 2005) who cites Heifetz and Linsky (2002)
51
result in the transformation of the system. This is a reason why there are organizational resistance to
make these changes.
While it is true that adopting sustainability analytics is a technical change as it heavily engages the
internal IT group and external technology vendors, it also involves a major adaptive change. According
to Fullan (2005), "Addressing the problem of sustainability is the ultimate adaptive challenge". Unlike
traditional financial tools, sustainability analytics tools are dependent on the assumption that
stakeholders are aware of the value of integrating sustainability into the core business strategy. It
assumes that people are on board and prepared to store, collect and analyze sustainability related data
on par with any other financial data that is being analyzed in the enterprise. This assumption is not an
easy one to make once organizations understand that the requirements for success of sustainability
analytics differ significantly from the requirements for success of financial analytics. The success of
sustainability analytics is therefore as dependent on overcoming organizational challenges as it is on
addressing technical challenges.
3.3.1
Technical Challenges
Most of the technical challenges have already been discussed. These include heterogeneity of the data,
incompleteness of information, scale of data, effective filtering, real-time data analysis, automation,
timeliness, data governance and selecting the right tool for analysis. There is considerable amount of
research in place to overcome these challenges. Software companies such as SAP and Oracle are
constantly pushing the envelope to design the next generation of big data tools that are equipped to
handle the increasing technical complexities of analyzing big data. Sustainability analytics shares most of
the challenges of big data analytics and will seek to benefit from these innovations in order to build a
better business case for its adoption as well as to realize the anticipated goals of implementation.
52
3.3.2
Organizational Challenges
While sustainability analytics shares similar technical challenges with other applications of big data,
organizational challenges of adopting sustainability analytics are unique and arise from the increasing
realization that unlike the adoption traditional financial analysis tools that are primarily technical
changes, adoption of sustainability analytics is an adaptive change and requires a good understanding of
the organizational challenges that it presents.
It is important for organizations to recognize that big data tools present different challenges depending
on its applications. Interviews with a sustainability manager in a large luxury apparel company revealed
that one of the biggest reasons for the failure of sustainability analytics was the lack of preparedness
with regards to the unique challenges that big data solutions present when used for sustainability as
compared to its use in traditional financial analysis. These differences arise due to the nature of the
problems. The following table lists some of the differences between technical problems and adaptive
challenges75 . We use these differences to analyze the unique organizational challenges of adopting
sustainability analytics tools.
Technical Problems (Traditional financial
Adaptive Challenges (Sustainability analytics)
analytics)
Financial problems are easy to identify as there are
Sustainability problems are difficult to identify (or
defined datasets such as statement of accounts,
easy to deny) as there is no defined dataset.
cash flow, etc.
Require changes in values, beliefs, roles,
Often lend to quick and easy solutions such as cost
7s Adapted from Ronald A. Heifetz & Donald L. Laurie, "The Work of Leadership", (1997), Harvard Business Review,
January-February 1997; and Ronald A. Heifetz & Marty Linsky, "Leadership on the Line", Harvard Business School
Press, (2002)
53
benefit analysis, analysis of financial ratios, etc.
relationships and approaches to work. Challenges
are more embedded within the organization.
Usually can be solved by an authority or expert.
People with the problem are the ones usually
trying to solve it.
Requires changes in one or a few places and is
Requires changes in a number of places and
usually contained within the organizational
usually involves cross organizational boundaries.
boundaries.
People are usually receptive to technical solutions.
People resist adaptive challenges related to
sustainability due its inherent uncertainty.
Solutions can be implemented quickly.
Creating solutions is time-consuming, requires
experiments and new discoveries.
Benefits of overcoming technical challenges are
Benefits of overcoming adaptive challenges are
often seen in the short term.
usually observed in the long term.
The above differences highlight several organizational challenges unique to sustainability analytics
compared to financial analytics. It demands a shift in mentality with regards to values and beliefs within
the organization. For users of sustainability analytics to take the data and analysis seriously,
sustainability goals need to be embedded within the core business strategies in line with the three
pillars of sustainability. The decision to implement sustainability analytics should be made jointly by the
executives who are responsible for generating the data and directly impacted by the use of the data.
The ability to recommend changes and propose solutions should be tied with the responsibility to
analyze data.
Further, business managers should have a good understanding of the external elements that are
impacted by and influence the organization's sustainability performance. This will make the data more
54
relevant for analysis and improve both, data collection and analysis phases. It is important that financial
returns on the implementation of sustainability analytics should be expected in the long term and that
the sustainability managers who are making the business case for these tools do not oversell their case,
and hence risk losing long term credibility of taking these initiatives within the organizations. We discuss
this in more detail in Chapter 5.
Privacy issues have also been expressed by some organizations. A sustainability executive from an
internet company pointed towards privacy as one of the most critical issues when dealing with
sustainability analytics. For certain kinds of data such as health records, there are strict laws governing
what can and cannot be done. There is also great public fear regarding the inappropriate use of data,
particularly in sustainability analytics where data is linked from different sources. For example, location
based data tracked of employees is sensitive information that not all organizations are able to capture.
Human collaboration is another challenge that sustainability analytics faces. Effective big data analysis
requires efforts from multiple experts in different functions to really understand what is going on. These
experts maybe separated by space and time and it might be too expensive to assemble teams together
for ongoing analysis.
Finally, one of the biggest organizational challenges based on the feedback from interviews is the
resistance to change. The feedback from one manager was that "results of sustainability analytics are
often unclear and can trigger negative emotions among users". A resistance to change within
organizations can create roadblocks for the success of sustainability analytics and should be addressed
before deciding to implement analytics.
55
3.3.3
Viewing sustainability analytics as a multi-stakeholder imitative
We use the framework by Sterman, Repenning, Kofman (1997) and Schneiderman (1988) to estimate
the magnitude of organizational and technical challenge that the use of sustainability analytics tools
presents. We modify the framework to add conditions to identify level of technical complexity assuming
that technical solutions that can be found in the short, medium and long terms have low, medium and
high complexity respectively.
Increasing Half Lives
76
-
0.
I
Global
E
0
U
*
Society-wide
CFU
U
C
Multiple
organizations
CU
Cross functional
U
CuO
L_
0
Individual
High
Low
Short term
solutions
Medium term
solutions
Long term
solutions
Technical Complexity
Framework adapted from Sterman, Repenning, Kofman (1997), Schneiderman (1988)
56
As described in this paper, although there are significant technical challenges of successfully
implementing and using sustainability analytics, the organizational challenges are the principle factor
determining the realization of the potential benefits of sustainability analytics. Technical challenges with
big data, although significant, can be addressed through upgrades, training and external support.
Further, the amount of research underway to overcome technical barriers suggests that organizations
can be relatively confident that they can tackle these issues in the medium term. Hence, we assign a
medium level of technical complexity to sustainability analytics. However, organizational challenges
demand changes at a fundamental level throughout the organization across various functions. As we
have demonstrated earlier, the organizational challenges that come with using sustainability analytics,
extend not only to business functions within the organization (finance and sales, plant operations and
corporate operations) and to multiple organizations (suppliers, customers) but also to society-wide
stakeholders such as shareholders, communities surrounding business operations, government agencies
and the general public, all of whom are generating as well as using the data that is ultimately
contributing to the effectiveness of sustainability analytics. Further, although companies struggle with
technical challenges of big data, a research conducted by Tata Consultancy Services77 revealed that the
biggest hurdle to achieving success with big data initiatives was to encourage business units to share
information across organization silos.
Therefore, organizations must view the implementation of sustainability analytics as a multi-stakeholder
initiative that requires the active participation, consultation and coordination of several participants
within and outside the business. Failure to recognize this characteristic can result in the lack of
preparedness of the organization to make the decision to use analytical tools for sustainability
ultimately resulting in the failure to meet the anticipated goals of adoption.
Infoworld.com, Thor Olavsrud, "10 big data trends changing the face of business", (2013), last retrieved from the
web on Apr 5, 2014: http://www.infoworld.com/slideshow/113454/10-big-data-trends-changing-the-face-ofbusiness-223969#slidell
7
57
Recently, alternate stages of big data adoption have been recommended by organizations such as IBM.
In collaboration with the University of Oxford, IBM conducted a research of 1061 companies across the
globe 8 . In an effort to streamline the implementation process, they outlined four phases of big data
adoption which include educate, explore, engage and execute 79(See Annex 2 for more details).
Rethinking and adjusting the phases can help to reduce some of the complexities associated with data
collection, analysis and organizational challenges. Further research is required to investigate whether
the issues discussed in this paper are being adequately addressed through the innovations that are
being made to streamline the process of sustainability analytics.
Forbes.com, Maribel Lopez, "The 4 Phases of Big Data", (2012), last retrieved from the web on Apr 5, 2014:
http://www.forbes.com/sites/maribellopez/2012/10/31/the-four-phases-of-big-data/
79 ibid.
78
58
4.
What is the role of the finance function?
Although the business case for sustainability analytics makes it a compelling agenda for organizations,
there is a demand for the finance function to be involved in the data collection, analysis and decision
making phases. Most organizations currently assign the responsibility of collecting and analyzing
sustainability data to sustainability managers and the IT function. As three of the world's largest
accountancy organizations (CIMA, AICPA and CICA) recently put itso, 'To remain relevant, the accounting
profession must take ownership and embrace business sustainability. Accountants can apply the
necessary financial and commercial rigor to develop clear and measurable sustainability goals, 'decisionuseful' and reliable sustainability reports and become change agents for a sustainable future.'"" CFOs
and the finance function can give structure to the data and provide the incentives necessary to
maximize its business value. Plenty of people can advise an organization on how sustainability
performance should be measured. But few are as convincing as the CFO when it comes to
demonstrating not only what should be measured but also how this creates business value.
Sustainability performance management and therefore sustainability analytics can be a key enabler of
value when driven by finance professionals. The finance team has the visibility into every part of the
enterprise and is ideally suited to own the sustainability data. A research conducted by Accenture in
partnership with the Chartered Institute of Management Accountants (CIMA), indicated that 'CFOs who
nurture the cross-functional skills of both sustainability and finance professionals and find ways for them
to work seamlessly together will see more opportunities to grow value for stakeholders. It is this
marriage - of rigor and reporting on the one hand; and understanding the key attributes of sustainable
practices on the other - that ensures a business is able to deliver shareholder value over the long-term.
Accenture, "Optimizing Sustainability Performance Management", (2009); 'A word from the president', Financial
Management, January/February 2011
AICPA, CICA and CIMA, "Evolution of corporate sustainability practices: Perspectives from the UK, US and
Canada", (2010)
81
59
But making it happen requires clarity, vision, leadership and a willingness to act' 2 . In this section, we
analyze the role of the finance function in contributing to the success of sustainability analytics and
argue that the marriage between sustainability and finance professionals is critical for the successful
realization of the business cases for sustainability analytics.
4.1
Viewing sustainability analytics as a strategic initiative
Traditionally, sustainability analytics has been seen as a tactical tool adopted in response to increasing
amounts of sustainability data within the organization. For example, as overheads such as electricity
increases, finance managers attempt to gather the energy usage data to make a tactical decision about
reducing consumption. However, most organizations do not make this process a part of strategic
planning. Four interviewees who responded to the survey indicated that analytics was being used as a
tool for reactive measures rather than proactive goal setting. The decision to implement these tools was
triggered from their risk management function. This hampers the ability of sustainability analytics to
influence business performance. Instead, these tools need to be aligned with key business drivers and
financial processes so that they can become an integral part of the strategic planning process. Making a
commitment to sustainability and its data is relatively common. In fact all the organizations interviewed
faced almost no resistance by the senior executives to commit to analytics as a principle. However, very
few organizations integrate analytics into their strategy and financial planning, and use that data
throughout the organization for decision making. This integration is critical to close the gap between the
expected value and realized value of sustainability analytics. It gives organizations the ability to quickly
act upon insights gathered from the data and take advantage of the opportunities. The finance function
must work with the sustainability managers to integrate sustainability analytics within their strategy.
82
CIMA and Accenture, "Sustainability performance management: how CFOs can unlock value"
60
An organization that has successfully been able to integrate sustainability analytics into its core strategy
is Nike. Data analysis, future casting and scenario planning are helping Nike to decouple growth from
constrained resources as part of its key sustainability initiatives83. Using analytics Nike claims to have
reduced waste, developed a water-free dyeing technique and researched alternatives to cotton which is
a water-intensive crop8". Further, Nike's Materials Sustainability Index (MSI) set up with the help of
systematic data collection and analysis has set the standard for the footwear and apparel industry
through its free use by designers, buyers and consumers who can now determine the environmental
and social impacts of different materials. According to Nike's Hannah Jones, vice-president of
sustainable business and innovation, this use of "smart data"85, has helped them achieve the business
case for analytics through sustainable design.
4.2
Integration of sustainability and financial analytics
Another barrier to implementing sustainability analytics is the inability to quantify and then integrate
the effect of sustainability factors into financial performance and to the impact on shareholder value.
This explains the survey result about the lack of opportunities to cut costs using sustainability analytics.
Upon discussions with the interviewees after the survey, the fact however was that they did find
opportunities to cut costs but due to the lack of integration with the organization's financial data failed
to build a compelling case to implement measures that could demonstrate an improvement in
shareholder value. One respondent reported that although analytics helped them find opportunities to
cut cost, they were unable to quantify the amount of savings. Thus, he was not able to convince the
finance function to invest in the initiatives. This link between sustainability and financial performance
Theguardian.com, Rachael Post, "Ford and Nike use big data to make smarter sustainable design", (2014), last
retrieved from the web on Apr 6, 2014: http://www.theguardian.com/sustainable-business/ford-nike-big-datasmart-sustainable-design
8
4
85
ibid.
ibid.
61
becomes more important when investors start asking questions about risks and the value of sustainable
products or initiatives. Many of the executives indicated that organizations still manually compare
sustainability performance information to financial metrics. This is inefficient and prone to data quality
issues. Sustainability data to be effective must be gathered and shared across the enterprise. One way
to do this is to incorporate sustainability metrics into management dashboards that are used to make
real-time decisions such as initiatives to reduce cost.
A company that claims to provide the capability of integrating sustainability analytics with financial
analytics is SAS. Their product, SAS Sustainability Reporting86 , collects and analyzes the data, identifies
the areas of highest impact, and helps make decisions about future impact. It also provides visual
strategy diagrams that create compelling illustrations of the relationship between performance
objectives established for various business units8 7 . However, it is important to note here that although
analysis products such as SAS Sustainability reporting can provide valuable insights and create the ability
for organizations to find opportunities in sustainability data, the entire process is still heavily dependent
on the quality of data that is inputted. The use of solutions such as these does not reduce the
importance of the data collection phases as explained earlier. Several organizations have failed to
benefit from analysis products due to the lack of effective data collection.
An example of integration of sustainability analytics and financial analytics is that of Microsoft. On July 1,
2012, Microsoft created a new policy across 14 business divisions in over 100 countries making every
division accountable for its carbon emissions8. This ambitious project was successful due to the ability
of managers being able to view their division's carbon emissions on a frequent basis and from a financial
SAS.com, SAS Sustainability Reporting, last retrieved from the web on Apr 8, 2014:
http://www.sas.com/en_us/software/sustainability/reporting.html#section=1
8
87
ibid.
CSRWire.com, Aman Singh, "Carbon Policy: Inside Microsoft's Efforts to Integrate Sustainability into its Financial
Model", (2013), last retrieved from the web on Apr 9, 2014: http://www.csrwire.com/blog/posts/1012-carbonpolicy-inside-microsoft-s-efforts-to-integrate-sustainability-into-its-financial-mode
62
perspective. TJ DiCaprio, Senior Director of Environmental Sustainability at Microsoft says that "By
internalizing the otherwise external cost of pollution, the price of carbon is now part of the profit and
loss statement across business divisions. We have now integrated this across the financial structure and
engaged the executives and employees on our commitment to mitigating climate change and investing
the funds appropriately"8 9 .
4.3
Using the right metrics
Linking financial and sustainability analytics is not enough. It is important to use the right metrics that
can explain why sustainability matters and how it is impacting the organization. Sustainability analytics
can help collect and organize data to make measurements such as material intensity (pounds of material
wasted per unit output), energy intensity (net-fuel energy consumed to provide heat and power
requirements for processes), water consumption (gallons of fresh water consumed), toxic emissions
(pounds of toxic material emitted per unit output), etc. However, these measurements do not have a
business impact unless they can be compared to benchmarks, indices and standards.
There are various metrics, indices and approaches to measuring sustainability goals. Companies can use
benchmarks set up by organizations such as the United Nations, International Water Management
Institute and International Fertilizer Industry Association. Indices such as Air Quality Index, Gender
Empowerment Index, Environmental Performance Index and Education Index are also useful as an
aggregate sustainability indicator that combines multiple sources of data9". There are also several
auditing procedures such as the ISO 14000 and the ISO 14031 that can be used to evaluate the
CSRWire.com, Aman Singh, "Carbon Policy: Inside Microsoft's Efforts to Integrate Sustainability into its Financial
Model", (2013), last retrieved from the web on Apr 9, 2014: http://www.csrwire.com/blog/posts/1012-carbonpolicy-inside-microsoft-s-efforts-to-integrate-sustainability-into-its-financial-model
9
Ibid.
63
sustainability performance of a company using various indicators 1 . Finally, there are reporting
standards being set up by the Global Reporting Initiative to standardize, model and monitor
sustainability procedures. Thus, even when organizations are armed with sustainability data and the
capability to analyze the data, a framework is required to be put in place that can use the right metrics
consistently. Problems faced while setting metrics can be too much information, a lack of visibility into
the different organizational functions and the failure to aggregate the relevant data that is required for
different metrics. As Chen Ying of the Beijing Rong Zhi Institute of Corporate Social Responsibility
explains, 'Measuring these outcomes is difficult. You either are too broad or too narrow in what you
measure. Striking the balance is the challenge.' 92
The challenge of using the right metrics and identifying the data necessary to apply those metrics can be
effectively met by the finance function. Finance executives are well placed to select and explain the right
metrics and to compare performance over time. The CFOs department can understand how to prioritize
information and to make effective decisions that impact bottom line results. Using metrics gives the
finance function a yardstick to calibrate how well their company is doing in terms of their sustainability
initiatives and to develop strategies for improvement.
4.4
Integration with business planning, and broaden the role of finance
professionals
Another common theme that was raised by several sustainability executives was the contribution of the
narrow role of finance professionals to the failure of sustainability analytics. Due to the inability of the
finance function to make decisions based on the insights gathered from the sustainability data, the
CSRWire.com, Aman Singh, "Carbon Policy: Inside Microsoft's Efforts to Integrate Sustainability into its Financial
Model", (2013), last retrieved from the web on Apr 9, 2014: http://www.csrwire.com/blog/posts/1012-carbonpol icy-inside-microsoft-s-efforts-to-integrate-sustainability-i nto-its-financial-model
92 UNGC-Accenture, "A New Era of Sustainability: UN Global Compact-Accenture CEO Study 2010", (2010)
91
64
expected value from sustainability analytics is not realized. There is a growing need to integrate
sustainability analytics with business planning and to bring these two disciplines together. Due to the
separation of the data collection and analysis processes from the business planning processes, finance
professionals lack the relevant data to make decisions while sustainability professionals lack the
adequate resources, expertise and decision-making power to make an impact. Integration of
sustainability analytics with business planning can lead to the evaluation of sustainability initiatives with
the same diligence that financial investments are evaluated and to a better understanding of the impact
of sustainability initiatives on the bottom line, thus embedding sustainability into the organization in a
meaningful way than simply creating compliance procedures for business units to follow. Further, this
provides an opportunity to the CFO to discuss rigorously assessed sustainability performance along with
other business metrics, with investors and stakeholders.
An organization that claims to integrate sustainability into business planning is Novo Nordisk. Novo
Nordisk has introduced explicit discussions of sustainability measures at meetings and road-shows to
align sustainability with financial planning and reporting. We have done a lot in terms of investor
education,' says Susanne Stormer, Vice President, Global Triple Bottom Line (TBL) Management. 'We
have investor meetings where our mainstream analysts sit next to external environment, social and
governance analysts. Cross-cutting conversations mean that the mainstream investors build
understanding about what the ESG investors are looking for and ESG investors sharpen their way of
93
thinking and learn about nailing sustainability down into something that can go into their analysis.'
This broadening of the role of finance professionals allows for embedding sustainability into the core
decision making process of the organization, improving the data collection, analysis and reporting
processes, and linking sustainability to business performance systems. All this creates more value
through better decisions and makes sustainability explicit within the financial performance of the
93
CIMA and Accenture, "Sustainability performance management: how CFOs can unlock value", Page 14
65
organizations. Another organization that claims to work with the finance function to implement
sustainability is Standard Chartered. They provide their frontline employees with guidelines to identify
environmental and social risks and support them with the bank's Sustainable Finance team, who provide
technical advice and assistance. The aim of the bank's sustainability risk management approach is to
make the environmental and social risk assessment part and parcel of the financial transaction
process94. 'It's about bringing the rigor of running a business to running sustainability,' says Gill James,
Head of Sustainability 5 .
4.5
Combining financial and sustainability expertise
Although the broadening of the role of the finance function and the integration of finance and
sustainability roles helps in improving the effectiveness of the data collection and analysis phases,
deployment of the insights gathered from sustainability analytics requires financial and sustainability
professionals to upgrade their skills. Finance professionals need to understand the pillars of
sustainability, its benefits and the drivers associated with the different pillars. Sustainability
professionals need to be conscious of the requirements and process involved in creating business value
from sustainability and making that process more credible.
A report from CIMA in partnership with Accenture recommends various measures to blend financial and
sustainability expertise 96 . We examine these measures from the perspective of sustainability analytics.
i.
Providing advice on how to integrate sustainability measures into management actions to
deliver results - This can help identify the appropriate data to collect and the type of analysis
required to achieve the expected results. It will also help build more credible results.
94
CIMA and Accenture, "Sustainability performance management: how CFOs can unlock value", Page 18
95
ibid.
96
I6bid. Page 21
66
ii.
Training sustainability professionals with financial skills - This can help sustainability experts
understand why certain types of data are required for analysis and how to streamline the data
collection process. For example, it will help them understanding why it is important to find
monetary values for sustainability impacts.
iii.
Building knowledge of finance professionals in sustainability -This can streamline the data
analysis phase by providing a clear understanding of the link between sustainability data
analysis and business value. For example, understanding how and why carbon emissions affect
scenario analysis.
iv.
Building cross-functional teams between sustainability and finance -This can lend credibility to
the sustainability data and help in effective coordination in making decisions.
v.
Integrating sustainability and financial reporting - This can add more credibility to sustainability
data, standardize the different phases and clarify the business value of sustainability analytics to
external stakeholders.
Novo Nordisk claims to have merged financial and sustainability expertise by integrating sustainability
information into its annual reports with the help of a join editorial team consisting of finance, legal,
operations and sustainability professionals9 7. Given the lack of internationally recognized standards in
sustainability reporting, their goal is to 'integrate management system to develop this common
understanding and framework. This applies not only to internal sustainability and finance professionals
98
but also to the independent external verifiers.'
97
98
CIMA and Accenture, "Sustainability performance management: how CFOs can unlock value", Page 21
ibid.
67
5.
Expanding the Three Vs
Looking back at Gartner's big data definition, the three properties or dimensions of big data - volume,
variety and velocity - are universally accepted as the key characteristics for organizations to consider
and prepare for when adopting big data tools. Sustainability analytics inherits these big data
characteristics but poses several other challenges, as discussed in this paper that sustainability
managers and organizations must consider to take advantage of its potential benefits. Integrating the
discussions of this paper, in this section we propose a new framework with an extended set of
characteristics that serve as an overarching strategy for organizations making the decision to adopt
sustainability analytics. The diagram below shows how data volume, velocity and variety are expanding
at an increasing rate to create pressures on big data applications such as sustainability analytics.
Datasciencecentral.com, Diya Soubra, "The 3Vs that define big data", (2012), last retrieved from the web on Apr
10, 2014: http://www.datasciencecentral.com/forum/topics/the-3vs-that-define-big-data
9
68
The characteristics of volume - exponentially growing amounts of data from Megabytes to Petabytes,
velocity - increasing requirements on speed of data processing from batch to real time, and variety increasing number of types of data, each present challenges they expand. Sustainability analytics tools
will need to be able to manage large amounts of data, process data faster and aggregate and clean data
effectively to overcome the many technical challenges associated with data collection and analysis.
We propose 3 additional Vs that are unique to sustainability analytics and must be a part of an
organization's strategy when adopting sustainability analytics.
i.
Veracity - Veracity indicates the reliability and authenticity of the data collection process. While
volume, variety and velocity address some of the major data collection challenges such as scale
of data, automation, limitation of data storage and heterogeneity, veracity refers to the process
of understanding and selecting the appropriate sustainability data that includes environmental,
social and employee information, applying the required filters, making the necessary
correlations, ensuring precision and standardizing the process of data collection through
documentation of assumptions. Veracity stresses on the importance of the data collection phase
to give credibility to the data analysis phase.
ii.
Viscosity - Viscosity indicates the ability to process required data and transform it into action.
This represents the resistance to flow from organizational forces starting from data collection to
analysis and ending in action. Viscosity addresses change management issues within the
organization, the importance of getting employees on board with sustainability initiatives,
overcoming privacy and data governance issues and the need for effective coordination and
data sharing between departments.
iii.
Vision - Vision indicates the business strategy that is required to successfully realize the benefits
of sustainability analytics. It refers to the need for understanding the three pillars of
69
sustainability and thus setting the appropriate expectations from sustainability analytics, the
ability to integrate sustainability within finance functions, making sustainability analytics a part
of the core strategy and enhancing expertise of employees to maximize the realization of
benefits from sustainability analytics.
While volume, variety and velocity remain core considerations, veracity, viscosity and vision form allencompassing characteristics. Due to the lack of foresight on the part of leaders and false assumptions
made by managers when implementing sustainability analytics these 3 characteristics become invisible
and therefore lead to the failure of realization of expected goals of implementation. While each
challenge is complex and there is no 'one-fits-all' strategy, organizations must consider all 6 Vs before
making a decision to adopt analytics for sustainability.
5.1
Limitations
Writing this last chapter, my intention was to encourage a discussion to address the necessary
conditions and best practices to successfully adopt sustainability analytics. I recognize that further
investigation and an evaluation of a larger sample size of organizations would be required to
substantiate the results and identify all the critical challenges facing sustainability managers. I hope that
the issues identified and recommendation formulated in this thesis can serve as a basis for further
research.
70
Conclusion
Businesses recognize that measurement is one of the keys to effective management. Collecting and
analyzing data about how an organization should operate leads to knowledge that can improve decision
making, refine goals and focus efforts. Big data analytics has the power to transform how businesses the ones with biggest environmental impacts, but also access to large amounts of information - can take
action on sustainability. A drive for data collection can also incentivize smaller businesses to be more
responsible in their own operations, creating a domino effect1 '0. Measuring and understanding how
doing business affects the natural world can open up new opportunities for bringing sustainability inside
an organization: creating change, cutting costs and boosting long-term profitability in a resourceconstrained world. We are already seeing the pioneers in sustainability such as Novo Nordisk leading the
way, bringing suppliers and customers along for the journey. However, this isn't easy. There are several
challenges associated with the successful adoption of sustainability analytics.
In this paper, after establishing the relationship between sustainability and analytics, we identified the
business cases for sustainability analytics. Through a systematic investigation and comparison of the
expected and realized goals of implementation, we concluded that the overall goals of using analytics
for sustainability were not being met at least for some organizations. Several reasons for this failure
were identified based on the false assumptions of organizations and data experts that the issues lie in
data analysis and not in data collection and that the challenges associated with the adoption of analytics
for sustainability are primarily technical and not organizational in nature. These were further examined
from the lenses of different phases of data collection and data analysis, and of technical vs. adaptive
challenges. The role of the finance function was then evaluated and the necessity for its integration with
sustainability was made visible and established. Finally, a framework was proposed that extends the
Carbontrust.com, "Why Big Data will have a big impact on sustainability", (2014), last retrieved from the web on
Apr 12, 2014: http://www.carbontrust.com/news/2014/02/why-big-data-will-have-a-big-impact-on-sustainability
100
71
original 3 Vs of big data to 6 Vs of sustainability analytics, to address the challenges associated with and
to include all the characteristics unique to the adoption of sustainability analytics.
While there are mixed feedbacks with regards to the effectiveness of sustainability analytics and its
potential to achieve the ambitious business cases that it offers, this paper has shown that if
organizations recognize the unique challenges that analytics for sustainability presents and are prepared
to make the adjustments required in its strategy and execution of implementation, there are significant
opportunities to maximize the return on investment.
To conclude, I would like to highlight that when it comes to information, it is not about what you know
or how much you have, but how you use it. In the past, anyone who owned data might have been
inclined to believe that they have a competitive advantage. Today and in the future, the success of
sustainability performance will not lie in who implements analytics but in who uses it most effectively.
New technologies will enter the market and address many technical problems. However, setting clear
sustainability goals and understanding how to manage and use the technologies to maximize the
potential of analytics will be central to get the most out of sustainability analytics.
72
Annex
Annex 1: Innovation in data collection techniques
The CloudApps platform offers various mechanisms to address sustainability data collection issues and
integration requirements'0 1
Raw Data Sources
S&
as i
oIwre& a
soms
102
a104
Bulk Capture: A "data import wizard" helps
in rapidly loading data using .csv format files. It is an easy to
use, wizard-driven solution that matches columns in the data file with requirements of the database.
Also included is a bulk data loader tool that can handle more complex and larger data loads103.
Easy Capture: This automates the data collection from remote sites with the help of easy to use web
forms and ensures accurate and timely use through approval processes and reminders".
Direct Integration: This seamlessly integrates with back office systems and physical devices such as
smart meters and can be called from a wide-variety of middleware solutions to push and pull datal.
Cloudapps.com, "Data Collection & Integration. Easy as 1-2-3", last retrieved from the web on Apr 12, 2014:
http://www.cloudapps.com/product-overview/cloudapps-platform/data-lOading-integratiOn/
102 Ibid.
103 Ibid.
104 Ibid.
105 Ibid.
73
Annex 2: Educate - Explore - Engage - Execute
In 2012, IBM conducted a research in collaboration with the University of Oxford that surveyed 1061
companies around the world 0 6 . The survey found that 28% percent of the firms were either piloting or
implementing big data activities1 0 7 . Based on their research, IBM recommended four phases of big data
adoption. These phases can be applied to sustainability analytics as well.
Educate: Knowledge gathering and market research
Explore: Building a strategy and roadmap based on goals set by the business and anticipated challenges
Engage: Pilot big data initiatives to validate the business case and requirements of the solution
Execute: Deployment of several big data initiatives and application of advanced analytics
IBM also makes several recommendations to create value from big data analytics based on these
findings08 .
Forbes.com, Maribel Lopez, 'The 4 Phases of Big Data', (2012), last retrieved from the web on Apr 12, 2014:
http://www.forbes.com/sites/maribellopez/2012/10/31/the-four-phases-of-big-data/
107
108
Ibid.
Ibid.
74
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