1.
The rise of Data:
Business Value and
The Management Imperatives.
Sheriff Shitu
Slightly revised from the version submitted to the University of Bedfordshire in January 2015 to satisfy
academic requirements for obtaining an MBA with specialization in IT management.

2.
2 | P a g e
Executive summary
“Data as the new source of competitive advantage” -- a promise that has beamed business spotlight
on Data; “Big Data” to be more specific. The search is on as companies increasingly turn to huge
chunks of data in the hopes of insightful discoveries that could produce new strategic choices never
previously conceived. Many jumped on the Big Data bandwagon needless to say without sufficient
planning or guidance, wasting fortunes along the way as they “tried their lucks” on various
commercial tools and technologies.
This work directs the attention of business managers and strategy executives away from the flood of
Big Data marketing unto internal organizational factors important for the success of Data-related
initiatives. Such factors include developing a coherent understanding of the potential of data,
assessing the preparedness of the business from a capability perspective, maximising ROI and limiting
waste by starting small, avoiding common pitfalls, and understanding the requirements for sustaining
these initiatives through strategy, culture, and governance.
While the “Bigness” of the Data is valuable from the standpoint of insights that can be generated
(provided data reliability and integrity is assured), it is established in this work that smaller
organizations with smaller data volumes are also able to achieve success from Big Data. This is not
only because of increasing availability of lower cost tools, but because being data-driven is an
attitude that can be developed by any organization regardless of organizational size or volume of
data available. This work also establishes the fact that while digitalization can be a good step towards
becoming data-driven, it is not a prerequisite; what is instead of critical importance is that an
organization is able to identify the major business processes and customer engagement channels
from which useful data can be generated (regarded as datafication of processes) for certain learning
or capability development purposes.
IT in the organisation is said to have transcended mere computerisation, and with the current trend
of digitalisation of business processes, it is becoming easier for any company to develop data-driven
competencies, adopting Big Data along the way, and eventually achieving the promise of competitive
advantage through the use of data. This work navigates through the development of necessary
operational and management competencies required for achieving and sustaining this data-driven
culture.

3.
3 | P a g e
Contents
Executive summary.............................................................................................................................. 2
1.0 Introduction............................................................................................................................... 4
1.1 Background............................................................................................................................ 4
1.2 Motivation ............................................................................................................................. 5
1.3 Aim and Objectives................................................................................................................ 5
1.4 Method of Analysis................................................................................................................ 5
2.0 Literature Review ...................................................................................................................... 6
2.1 What is Big Data .................................................................................................................... 6
2.2 A Historical Review................................................................................................................ 7
2.3 The Difference in Big Data..................................................................................................... 9
2.4 The Potential of Data........................................................................................................... 10
2.5 Embracing Data: Developing Data-driven competencies.................................................... 11
2.6 Data meets Management.................................................................................................... 14
2.7 Common Big Data pitfalls and criticisms............................................................................. 16
3.0 Discussion................................................................................................................................ 18
3.1 Case Studies......................................................................................................................... 18
3.2 Analysis................................................................................................................................ 21
4.0 Conclusion and Recommendations......................................................................................... 24
4.1 Conclusion ........................................................................................................................... 24
4.2 Recommendations............................................................................................................... 25
5.0 References............................................................................................................................... 27

4.
4 | P a g e
1.0 Introduction
1.1 Background
Global business expenditure on IT is projected to hit $3.8 trillion in 2014, according to a report by
Gartner (Global DataPoint, 2013). As competition gets tougher in many industries, and the state of
technology advances, organizations are increasingly relying on technology to innovatively create and
deliver business value. Unfortunately, Business IT can get very complex and hard to copy – every
organization has to find the right set of digital initiatives to invest in, and then select the tools and
techniques that generate optimal results. The phenomenon of Big Data is one of such technology-
driven initiatives which although was previously more popular only among large corporations, is
recently receiving significant attention from smaller organizations due to the availability of low cost
tools.
Beyond “Big Data”, this project will consider the broader view of the idea of “data-inspired
innovation” as a means for organizations to gain competitive advantage. While acknowledging that
every organization will need to employ technical expertise to identify the best Data tools for their
initiatives, this work rather takes a management approach to draw attention to often overlooked
management practices (mostly strategy and culture related) which can aid the innovation,
implementation and sustenance of data-inspired initiatives.
Analysing data to acquire business insights useful for forecasting, decision making, understanding
customer needs and organizational performance is not a new idea in the business world. However, in
the past few years, owing to the ubiquity and falling costs of computing devices and networks, and
the prevalence of social media and other internet based services, the amount of data generated
globally has grown significantly beyond the storage and processing capabilities of existing tools. As at
2013, about 90% of the world’s data had been generated within just over 2 years (SINTEF, 2013) at a
rate reported by Harvard Business review to be 2.5 exabytes per day (2.5 billion gigabytes) -- a figure
which has doubled after every 40 months or so thenceforth (McAfee and Brynjolfsson, 2012). With
so much data available to organizations (either generated through internal processes or externally
through customer engagement channels), more sophisticated tools are required. However,
maximising value from Big Data goes beyond simply selecting the right tools.

5.
5 | P a g e
1.2 Motivation
From over eight years of professional experience in the implementation and delivery of business IT
solutions and products, I have serviced businesses across various industries. The knowledge of
Strategy and Organizational culture gained through the MBA program has revealed that
organizations sometimes place unrealistic expectations on IT. Companies have invested so much on
IT and now on digital initiatives, without necessarily considering how well their strategy and culture
support the intended results of these investments. Narrowing down to the recent buzz of Big Data,
given its promises and associated costs, I am motivated to research further in order to make more
knowledge available on how organizations could gain greater returns on their Big Data investments
or even lower the costs of becoming data-driven companies.
1.3 Aim and Objectives
The aim of this project is to help organizations maximise value from, and achieve the promises of Big
Data and other related data-driven initiatives, while identifying necessary internal changes required
not only from the standpoints of operations, but from management and strategy.
The objectives are:
 To analyse the potential value of Big Data to organizations, and thus provide managers with a
defendable case for data-inspired initiatives.
 To study and discuss the operational capabilities usually required to be positioned for
achieving better results and creating competitive advantages through Big Data.
 To identify and discuss the necessary management imperatives – strategic, cultural and
governance changes required to support and sustain Big Data initiatives.
1.4 Method of Analysis
Some organizations have succeeded in achieving competitive advantages through their application of
data-driven initiatives. Such will be used as case studies for a best practice benchmark. Evidence for
discussions will be drawn from the approaches taken by these organizations.

6.
6 | P a g e
2.0 Literature Review
2.1 What is Big Data
On recognising the lack of a unanimously agreed upon definition for Big Data, the University of
California at Berkeley recently invited 40 Data Science thought leaders from several industries to
proffer definitions for Big Data. These definitions differed around the lines of what each person
considered to be most important – the data size, the tools used for analysis, or the useful insights
that can be drawn from the data (Dutcher, 2014). However, there was relatively significant
agreement on the defining characteristics of the data that qualifies as Big Data using the 3 V’s
concept (Volume, Velocity and Variety) -- an idea which according to Baines (2013) was first
presented in a research paper written in 2001 by Doug Laney. Laney (2001) foresaw and described a
future challenge for the Enterprise as 3 Vs:
 Data growing to unmanageable Volumes.
 A rapid increase in the Velocity of Data creation.
 The Variety of forms in which data will exist.
With retail giants like Walmart having to deal with over 2.5 petabytes of data and more than a million
transactions per hour (Cukier, 2010), and Facebook, Youtube and Twitter with a variety of data types
to process at similar speed of data creation Dombrowski (2014), Laney’s foreseen challenge
undoubtedly holds true -- data at hand for some companies now surpass capacities readily storable
or analysable using conventional tools. There has since been a release of contemporary analytics
tools to handle Big Data. Hurwitz et al (2013) noted however, that Big Data does not refer to any
single new technology, but a purposeful combination of old and new ones.
The word “Big” has had its share of criticism rooted in its relativism. In a special issue of Nature
journal, “Big” was termed “a moving target” which is a sheer reflection of today’s datasets (Nature,
2008). George et al (2014) even argue that focus on the “Bigness” of the data makes “Big” a
misnomer in the vase of Big Data. They however state that this “Bigness” has been useful to attract
the attention of researchers. However, among practitioners, the data volume is no longer a defining
parameter, but rather the insights that the volume can provide. To further support the importance of
ensuring value in the data amassed, Saporito (2013) proposed to include “Value” as the fourth V
dimension of Big Data.

7.
7 | P a g e
It is very clear that while the 3 V’s dimensions of Big Data pose a challenge that will surely keep
Computer scientists and Data science corporations busy for some time, however, herein lies great
opportunity for business. Opportunity which according to Dombrowski (2014) can be realised by
ensuring that the data “does not become useless noise”, but rather analysed in ways to make
smarter and more strategic business decisions. In other words, from a business point of view, of
invaluable importance are the actionable insights that can be drawn from this data. Press (2014) has
attributed to Big Data a growing business “attitude” of believing that combining more data from
multiple sources can enable better business decision making.
2.2 A Historical Review
The three-V dimensions widely used to define big data have been accredited to Doug Laney’s 2001
research paper on Data management (Press, 2013). Laney (2001) however only described this
phenomenon and did not coin the term “Big Data” as this term cannot be found anywhere in his
work. Cukier (2010) asserts that the term was later coined by Scientists and Computer engineers.
In an annual research conducted by International Data Corporation, researchers Gantz et al (2007)
reported that the amount of data captured and replicated in 2006 alone was 161 exabytes (161
billion gigabytes) which they expressed as 3 million times the data in all books ever written. This
report was credited by Press (2013) to be the first ever to estimate and forecast the amount of digital
data created per year. In the 2012 version of the report, Gantz & Reinsel (2012) presented that this
data had grown to 2,837 exabytes (almost 18 times the 2006 size within just 6 years after the first
one).
How did data get so “Big”? In a special report of The Economist, Cukier (2010) answers this question
and observed certain trends responsible for the data explosion. His findings included: Continuous
digitising of video through surveillance cameras, Increased access to the Internet worldwide (which
he estimated at almost 2 billion people), the plummeting prices of computer memory and processors
that have led to the increasing forms of smarter devices, and Increased access to smartphones
(estimated at 4.6 billion mobile subscriptions worldwide). Cukier (2010) also noted the increase in
capabilities of digital devices, which according to Turner et al (2014) has led to the advent of Internet
of Things (IoT) – a term used to describe the era of adding software and intelligence to usual thing as
varied as trucks, cars, wristbands, watches, airplanes and dishwashers, dog collars etc. The

8.
8 | P a g e
Figure 1: The Geography of the "Data Universe"
Source: Grants & Reinsel (2012)
highlighted that over 20 billion such devices exist in the world today constantly generating data for
analysis.
In a similar study, Cukier & Mayer-Schoenberger (2013) spotted the practice of rendering things that
were not previously quantified into data forms – an activity they termed ”datafication”. For example,
they noted that the advancement in GPS satellite systems have caused location information to be
easily datafied. With software on mobile phones and sensors able to report location data to servers
for storage and processing. Another example is the datafication of friendship, professional
connections and likes on social networks. According to them, datafication converts all aspects of life
to data and that differs from digitization which simply converts analog content such as books or
photographs to digital information.
In an attempt to make sense of geographic distribution of data all over the world, Gantz & Reinsel
(2012) in their study, established a pattern of where data was being either generated, captured, or
consumed. They successfully mapped users of the devices or software that either generated or
consumed data (through Internet use, digital TV, surveillance cameras, mobile phones, sensors etc.)
into locations as illustrated below. Their results show that more than 50% of the digital data available
in the world is being generated or consumed in the U.S and UK alone. This can be attributed to the
influence of Internet of Things, mobile devices, and the ubiquity of internet connectivity in these
countries.

9.
9 | P a g e
From the work of Friedman (2012), it is clear that the US government has supported and promoted
the Big Data concept through initiatives such as the 200 launch of the data.gov website to make
available datasets 445,000 datasets. In 2012, the US government called for every Federal agency to
have a Big Data strategy as the government made available $200 million for Big Data research and
development. A public-private partnership was also launched in the same year called Data 2X,
intended to measure progress of women and girls’ economic, social and political statuses all over the
world.
The 2014 version of the annual IDC study was released in April, and data available in the “digital
universe” was reported by Turner et al (2014) to have reached 4.4 zettabytes (4.4 trillion gigabytes)
and this figure was forecasted to double every 2 years.
2.3 The Difference in Big Data
Given that Big Data initiatives have been characterised with high costs and being more human-
capital-intensive than conventional Business Intelligence projects as noted by Derbotoli et al (2014),
it comes as no surprise that business executives often question whether there exists any much
differences between the business practices of Big Data and those of other conventional data-driven
decision making practices. McAfee and Brynjolfsson (2012) argue that even though there exists
certain similarities such as the intention to extract intelligence from data that translates into business
advantage, the differences are inherent in the nature of the data being analysed. Using the 3-V
dimensions of Big Data discussed in section 2.1, they assert that conventional tools are not able to
capture, store or process data as such Volume, Velocity and variety of Big Data.
Another major difference is the concept of Real-time insights inspired by Big Data. The focus here is
to minimise or eradicate the conventional time lag between the analysis and action taking. Cukier &
Mayer-Schoenberger (2013) cited the example of Google’s 2009 health care research using Big Data.
Google researchers tracked outbreaks of seasonal flu using 50 million archived history of searches on
their search engine between 2003 and 2008. They compared these searches against historical
influenza data from the US Centers for Disease Control (CDC). In a bid to discover whether the
incidence of searches coincided with flu outbreaks. The CDC already tracked actual patient visits to
hospitals nationwide, but reports were usually late by a week or two. Google's system, by contrast,
worked in near-real time. The real time effect of Big Data analytics in this case could be useful to
predict disease outbreaks in certain areas and even predict the direction of spread.

10.
10 | P a g e
Cukier & Mayer-Schoenberger (2013) have also noted a major difference in the nature of the insights
often discovered from Big Data analysis. They are usually based on correlations since the data at
hand are in large volumes. Cukier (2010) described this as a shift from causation to correlation, i.e
while Business intelligence attempts to detect the causes of certain occurrences, Big Data analysis
attempts to reveal patterns and correlations from larger datasets. Cukier & Mayer-Schoenberger
(2013) state that in healthcare, seeing correlations that can predict disease occurrences can be
enormously valuable, even when the cause are yet to detected.
2.4 The Potential of Data
As revealed in the April 2014 International Data Corporation study (see figure 2 above), almost 70%
of data created in today’s digital universe is created by the actions of consumers – interaction with
social media, sharing of photos and videos, streaming of digital TV, text messages and emails etc.
However, companies hold liability or responsibility for 85% of the total data in the digital universe
through copyright, privacy, storage, processing, or simply by transmitting through their networks
(Turner et al, 2014).
Most of these data can be tapped to the advantage of these organizations, mostly in the creation of
competitive edges. Taking companies like Amazon and Google (which are digital by nature) for
Figure 2: Data interaction between consumers and companies
Source: Turner et al (2014)

11.
11 | P a g e
example, these companies have always been aware of the data they collected and the potential of
this data. Amazon knew all the books that were researched before a final purchase, and did present
these to other potential buyers based on whatever they are currently viewing. Data was its secret
success even before other popular traditional bookstores started to move online (McAfee &
Brynjolfsson, 2012). Youtube ads for example are very well targeted because Google has data to
know what websites you have visited and things you have searched for even before visiting Youtube.
The company is therefore able to attract more advertisers. Farecast, which is a part of the Bing
search engine, analyses over 225 billion flight records to advice customers on the best times to buy a
flight ticket at the best price (Jen Leo, 2009). The data advantage however no longer lies solely in the
hands of these naturally digital companies anymore. Many traditional organizations are developing
digital capabilities to capture data that are useful for them to gain useful insights into customer
preferences, behaviours, health, location etc. Cukier (2010) argued that given enough raw data, and
the right Big Data tools, new insights can be revealed which would previously have remained hidden.
In an interview with MIT Media Lab’s Alex Pentland, he discussed the trend of Businesses investing
billions of dollars into strategies that rely on access to large amount of data (Berinato, 2014). This has
seen a lot of recent acquisitions such as Google’s $3.2 billion purchase of Nest (Grossman, 2014) and
Facebook’s $22 billion acquisition of 5-year old WhatsApp messaging software (Iyengar, 2014).
Comparing these huge deals to the $7.2 billion that Microsoft paid (just a year before) for the large
53-year old mobile phone maker, Nokia (Nokia, 2014), global attention is quickly being drawn to how
valuable a company could become through the amount of consumer data it has available or can
potentially generate.
2.5 Embracing Data: Developing Data-driven competencies
To be able to make business decisions with a high degree of certainty is an advantage which is
natural to data-driven organizations, given the insights that can be derived from data available. This
also gives room for improved agility, due to the timeliness of necessary information. This section
reviews literature around the practice of leveraging data to create competitive advantages, from 2
perspectives:
 Internal: Business operations
 External: Customer engagements

12.
12 | P a g e
2.5.1 Competitive advantage through internal processes
In his AIIM whitepaper, Moore (2011) identified a paradigm shift in Enterprise IT which he described
as one from “Systems of Record” to “Systems of Engagement”. Moore stated that the conventional
role of Information Systems in organizations was mainly to capture, process and store information.
He however noted that considering the scale of adoption, this has quickly become an organizational
way of life and no longer serves as competitive advantage. He then emphasized the effect of this
mass adoption of information systems and the information highway (the internet) on outsourcing.
Outsourcing has in turn changed the way businesses relate with each other – Collaboration is on the
rise, value chains are increasingly complex and supply chains are being integrated. This has led us to
an era of “Systems of Engagement”. Moore (2011) describes this as an era where business
information systems have become increasingly communicative and collaborative across business
boundaries, timezones and languages using tools such as wikis and portals which borrow ideas from
social media and other consumer technology products. In support of this argument, Gartner (2013)
also reported this trend under the term “Consumerization of IT”, describing it as a disruptive force
for traditional business processes and operational models. An MIT/Capgemini research report simply
described it as “Digitalization”, and cited 3 main benefits: “better customer experiences and
engagement”, “streamlined operations”, and “new business models” (Baldwin, 2014, p. 22).
Increased collaboration has demanded more visibility of business processes and this also is fuelling
digitalization, as more businesses move to integrate social media, GPS and location systems, Mobile,
and other similar consumer digital technologies within their business processes. Gartner (2013)
reported that in this post-automation era, organizations now face pressure to embrace digitalization
to survive and stay competitive. Digitalization is no doubt the current trend as even NetEnrich (2014)
enlists it as number 1 on their top 5 business technology predictions for 2015. However, what the
data-driven idea add to this is “datafication”, which as earlier described in section 2.2 according to
Cukier & Mayer-Schoenberger (2013) is the practice of rendering things that were not previously
quantified into data forms. In other words, to leverage data for innovation and improvement of
business processes, data-conscious companies don’t just digitalize their processes. They go on to
ensure that processes are datafied – able to generate measurable data which can be analysed to
identify correlations and useful insights. Such insights can help to spot impending threats or lags, or
simply improve business processes. Summarily, to the utmost possible, business processes needs to
be put in quantifiable, measured units to ensure that sufficient data I made available for analysis.

13.
13 | P a g e
2.5.2 Competitive advantage through customer engagements
This has to do with the innovative creation and delivery of business value as more knowledge of
customer preferences and behaviour (and actionable insights) becomes available from the analysis of
data left behind (“data exhaust”) from their interactions with the company’s delivery channels. In an
interview transcript by Spann (2013), Jorg Lubcke confirmed that the better a company understands
the needs and circumstances of its customer, the better it can add value to its offerings – either
through the products/services offered or the service delivery process entirely.
As an organization increases in size, the degree of engagement with customers tend to decrease. This
is further worsened as delivery channels become digitalized. A bank customer for example might
never have direct physical contact with any employee of the bank given the maturity of the mobile,
online, social and even self-service automatic teller machine channels. Although the digitalization of
delivery channels save organizations on human resource costs, they have a tendency to adversely
impact service quality and customer experience. It therefore becomes paramount to understand how
customers are engaging with the delivery channels as a means of mining insights that explain their
needs. The organizations can therefore be in a better position to help them achieve these goals
through further customization or personalization of service delivery. Personalization at scale has long
been an advantage of naturally digital companies like Google and Amazon where, through their fully
datafied web delivery channels, are able to record (and report in data forms) every click of the users
in a way to understand their behaviour. Spann (2013) asserted that mobile channels are able to
reveal even more about customer preferences since this includes location-based information as well.
Insights from customer engagement channels inform the product or service development and affect
the way in which value is created and delivered back to the customer.
Datafication of channels is on the rise. Even the traditional in-store or in-branch channels have a
potential of being datafied. Cukier & Mayer-Schoenberger (2013) cited a patent that IBM was
granted in 2012 for a technology powering touch-sensitive flooring that digitalizes the floor into a
somewhat giant screen. Datafying the floor thereby means that the floor is able to identify the
objects on it. Applying this to a retail store, retailers can track the movement of customers within the
store. They can get to know which shelf they visited even if they did not buy any item from there.
Just like Amazon can tell what items you viewed even if you did not buy and then send you an ad
later when that item gets discounted, datafying the floor could change the way we view the physical
delivery channels (stores and branches) once again.

14.
14 | P a g e
2.6 Data meets Management
The previous section reviewed literature on what is required for internal business operations and
external customer engagement channels as an organization looks to derive competitive advantage
through their use of data. This section reviews literature on imperatives for management – what it
takes to sustain the Data culture from the standpoints of organizational culture, strategy and
governance.
2.6.1 Creating and Sustaining the data culture
In data-driven organizations, data takes centre stage. According to Cukier & Mayer-Schoenberger
(2013), this often requires a new way of thinking and can sometimes be challenging. McAfee &
Brynjolfsson (2012) argue that Managers must be willing to embrace a new culture of decision
making: Decision making based more on data-based evidence and less on own intuition. In addition,
when senior executives start to ask “what does the data say” as a first question whenever confronted
with important decisions, then the data culture spreads faster across the organization and sinks
deeper across all levels of management.
Griffin (2011) also emphasizes the need to get all employees to assume an obligation to implement
Data initiatives and to assume accountability for success or failure according to the role that they
play. He offered four “pillars” upon which a Data culture can be successfully sustained:
 Education: Ensuring that the knowledge is clearly assimilated.
 Buy-in: Ensuring that everyone is convinced about how this is going to work.
 Responsibility: Ensuring new responsibilities come with due incentives and motivation.
 Communication: Communication in all directions at all points in the lifecycle of the Data
initiative or program. This serves as foundation for all other pillars.
2.6.2 The case for a Data Strategy
Thought leaders (both academic and practice) vary narrowly in opinions as to how best to support
the data culture through strategy. While some have favoured the case for a separate Data strategy to
define how the entire organization benefits from data, others suggest amendment of business
strategies to accommodate the use of data.
Bell (2014) suggests a total reorientation of an organization’s business strategy based on clear
understanding and thoughtful use of data. On another hand, Hurwitz et al (2013) argue that there is

15.
15 | P a g e
a need for an enterprise-wide data governance policy and a Big Data strategy for every organisation.
In their adoption of data-driven initiatives, every organization is at a different level. They therefore
suggest that a Data strategy be defined clearly based on current organizational capabilities and then
developed gradually. As a good starting point, they assert that every organization has to look inward
through a discovery process to identify what data is currently available, where it is stored, how it is
used and who has ownership or control.
Bughin et al (2011) also noted that many organizations often have their Data strategies intertwined
with overall corporate strategy, and argue that this approach may get Data priorities constantly
demoted in the midst of other strategic choices. Data strategies therefore need to be treated
separately.
In an interview transcript reported by Jim (2013), it was reported that some companies already had
Data strategies in place before the topic of Big Data arose. Sunil Soares, a Data governance expert,
suggested in that interview that such companies should acknowledge their use of Big Data and
accommodate it in their existing Data strategies.
2.6.3 Data Governance: The case for a Chief Data Officer (CDO)
Bughin et al (2011) acknowledge that data required for Data-initiatives often cuts across internal
organization boundaries and therefore suggests that Data strategies be managed centrally and
should receive concerted efforts from top executives to ensure success. They however made no
mention of the new C-level role: “Chief Data Officer” in their work. Parmar et al (2014) also argued
that any initiative which is more disruptive than sustaining always requires strong leadership to
overcome the obstacles set by incumbent already established processes and organizational culture.
Sucha (2014, p. 26) describes Data governance as an “implementation of accountabilities for
managing data” and therefore includes the roles, plans, policies and procedures involved in the
management of data. Her definition tends to emphasize the weight of the responsibilities involved in
Data Governance, which could better favour the argument for a dedicated Chief Data Officer, rather
than sharing these responsibilities among existing top executives.
In an interview reported by Jim (2013), Sunil Soares argued that many CIO’s and CTO’s are already
dealing with data that qualifies as Big Data without being aware of the necessary governance, privacy
and compliance issues associated with it. He cited the example of Facebook’s policy that says that an
individual’s phone number should not remain in your master marketing list after the individual has

16.
16 | P a g e
stopped following your organization on Facebook. Many organizations are already integrated with
Facebook to leverage social media but are not keeping up with these policies. Sunil’s argument not
only calls for the need for Data governing policies within an organization, but also the need for a
dedicated executive officer charged with accountability for everything data-related.
It therefore can be gathered that for the purpose of effective governance and the proper
implementation of Data strategies across the entire organization, any Data-driven organization needs
to consider the need for the dedicated role of a Chief Data Officer.
2.7 Common Big Data pitfalls and criticisms
This section discusses literature that cover the various pitfalls to avoid in the implementation of Big
Data and other Data-driven initiatives.
2.7.1 Privacy
Organizations are increasingly datafying their digital channels to ensure that as much data as possible
is collected on customers and their activities. While this data is very useful to the organizations, there
is growing concern by consumers and regulators globally on the tendency of this data to be abused.
Big Data has been criticised to bite back when organizations are found in violation of privacy and data
protection laws. In a Harvard Business Review interview by Berinato (2014), Alex Pentland argues
that people feel that they should have as much rights over data collected from them as they have
over their bodies and their money. He therefore suggests that companies could be more transparent
about the data that they collect and its use. They should also put the customers in charge of whether
to allow this collection or not.
Cumbley & Church (2013) argue that most data protection laws originated from the times of
structured data and that organizations in the Big Data era may find these laws too restrictive. They
however emphasized that each stage of the life cycle of data (data collection, combination, analysis
and use) has its own privacy implications which organizations should familiarize with to avoid
litigations.
2.7.2 Compliance
In an interview reported by Jim (2013), Sunil Soares warns that due to the novelty and speedy
development of Big Data, Data compliance policies are still emerging, citing examples of frequently

17.
17 | P a g e
updated social media policies. Organizations are therefore required to stay updated to be aware of
risks and ensure compliance at all times so as to avoid reputational backlash.
2.7.3 Loss of Jobs
While Big Data continues to create the need for Data Scientists, critics argue that computerisation of
decision making threatens the need for managers in an organization. While Bell (2013) agrees that
there is a chance that Big Data may replace some managers, he claims that for many others, Big Data
has a potential of enriching their roles with data related skills which when added to their managerial
knowledge and experience gives them a better opportunity with their jobs. Cukier & Mayer-
Schoenberger (2013) also note that the human element of instincts, accidents, intuition, common
sense, and risk taking will always be very important even in Data-driven organizations.
2.7.4 Knowledge Gap
McAfee & Brynjolfsson (2012) assert that Big Data technologies require skillsets that are evolving and
new to most organizations. Unfortunately, many executives have been discovered to embellish
reports with data to support decisions already made through intuition. This reflects poor knowledge
of what Big Data brings to the table. Lycett (2013) also asserts that a major barrier to achieving the
competitive advantage that Big Data offers is the knowledge required to use the analytics tools to
improve business. Such knowledge gap therefore need to be addressed through training as well as
the hiring of new employees with required skillset.
2.7.5 Funding
Big data, as many other Digitalisation initiatives often suffer from poor funding according to Baldwin
(2014). This is especially the case when senior executives are yet to see the value proposition. As
earlier suggested by Griffin (2011), ensuring that everyone is convinced about the value of data and
how the initiatives are going to impact the business profitably is key to achieving buy-in from senior
executives and assure better funding.

18.
18 | P a g e
3.0 Discussion
This chapter presents case studies showing remarkable application of Big Data to support discussions
presented in the previous chapter. These cases are benchmarked as a basis for discussion that
follows in section 3.2.
3.1 Case Studies
3.1.1 Case 1: Amazon and Big Data
Initially known as an online bookseller when it started business in 1995, Amazon now offers a wide
range of products, using technological innovation to drive growth through convenience and lower
prices (Amazon, 2013).
In almost three decades of strictly online operations, Amazon has amassed unrivalled data about
consumer purchasing behaviour from its over 260 million customers worldwide. In 2003, Amazon
built a recommender system that used this data to suggest products to users that visited the website.
Using item-item matching and a technique known as collaborative filtering which allowed users to
rate products, Amazon was able to make recommendations to other users as they browsed product
catalogues. This recommender system’s algorithm has been improved over time and now uses
historical purchase data and purchase intent data (based on previous searches) in a way that each
user is shown a personalized list of products (based on their perceived preferences) when they visit
Amazon (Rijmenam, 2013).
In a recent MIT report, Leber (2013) documents a new B2B initiative launched by Amazon: the
company has now decided to package the knowledge it has about consumers and sell to marketers
who can then best target their ads at people who really want to buy them. Google’s $38 billion
business was built on selling ads. Amazon’s advertising revenue prior to this initiative was just at
$500 million. What Google knows about consumers is what products they have researched through
Google search, YouTube, etc., even if the research was just for a school assignment and does not
indicate a purchase intent. What Facebook knows are the consumer’s social behaviour and the kinds
of brands that they are loyal to (through Facebook likes). Amazon, however, knows with a higher
degree of certainty the products that consumers have either bought (through purchase history data),
intend to buy (through search keywords), or will likely need (a complimentary product based on a

19.
19 | P a g e
previous purchase). Such data can be very valuable to marketers and could grow Amazon’s
advertising revenue like never before (Leber, 2013).
This marketing initiative will work by allowing marketers to submit product details to Amazon,
specifying the profile of their target audience. These ads will then be served to Amazon users on
Amazon website and even when they are not on the Amazon website (through participating sites). A
user who has previously viewed cameras or searched for cameras on Amazon might see an Amazon
website displaying cameras with their current prices later when viewing another website. Another
user who has currently purchased an inkjet printer could be presented with printer cartridges later
on (Leber, 2013).
3.1.2 Case 2: A data-driven General Motors
Although the use of data is not new to the automotive industry, given that this had always been
important for sales analysis and supply chain optimisation for decades, the advent of Big Data,
sensors and other recent digital technologies is changing how companies see their data (Harris,
2013). General Motors received bad publicity in 2014 after spending $2.5 billion in 20014 alone on
recall-related repairs, setting an industry-wide record of 29 million recalls in a single year, and having
to part with another $35 million as fine for delaying their response to the US National Highway Traffic
Safety Administration (NHTSA), as reported by Young (2014). Could the company be said to be
turning to Big Data for rescue? Having collected over 3 Petabytes worth of data (which includes data
from product development, manufacturing, customer care, procurement, logistics, sales, marketing
and finance), General Motors seeks to put its data to use by opening a second data centre, with a
total investment of $546 million on both data centres (Rijmenam, 2014).
Telematics and Smart Cars
With sensors attached to almost every car part, constantly measuring, tracking and transmitting
collected data remotely through 4g LTE to the car manufacturer, today’s cars are unimaginably
smarter. Analytics on collected data reveals so much knowledge to the car manufacturers about their
cars. For example, GM is able to understand which part quickly becomes affected by another
malfunctioning part. Such knowledge then informs product design to make their cars more reliable
(Rijmenam, 2014). According to a Time news report, GM recalled 2 million cars in 2014 for a
defective ignition switch which caused 19 confirmed deaths in the US. The company has then been
facing a legal suit which will determine how much compensation to be paid for each case. With
another 125 received claimed deaths (yet to be confirmed), the company has set aside between $400

20.
20 | P a g e
and $600 million in compensation. It is hoped that the use of Big Data and Telematics will save GM
on product recalls and such litigations in the future (Barber, 2014).
Understanding car health and other usage factors through transmitted data even as the car is being
driven enables GM to be able to advise car owners proactively on preventative maintenance using
realtime predictive diagnosis on data as it is being collected. GM provides information to drivers
through a mobile app that they can install on their phone. This makes sure that car owners are able
to take actions before their cars actually break down (Harris, 2013). In another news report, about
57,000 2014 Chevrolet Impalas were recalled because GM noticed that some cars experienced
reduced or loss of power steering. Just 1 crash was detected in a single accident and no death or
injuries were reported. Jeff Boyer, GM’s vice president boldly stated: "These recalls signify how we've
enhanced our approach to safety. We are bringing greater rigor and discipline to our analysis and
decision making" (Higgins and Jeff, 2014).
Car Insurance and Personalised Marketing
Singh (2014) suggests that if data collected can be made available to car insurance companies, then
they would be able to offer more customized plans, as well as bill customers for insurance based on
driving habits. He claims that customers that drive safely during off-peak periods, within speed limits
and on safe roads will be able to pay lower under the “pay how you drive” scheme. GM also builds
profiles of customers from data it collects. A better understanding of these demographics helps them
better target their marketing efforts because they know the customers that are likely to buy luxury
cars versus other ranges (Rijmenam, 2014).
Dealership Performance
General Motors uses data to improve the performance of its 4,300 dealers. Analytics of location data
of dealers and car owners tracked and collected by GM has revealed that customers use their nearest
available dealers when they need to service their cars, but would always opt for the dealer with the
best deal when purchasing a new car. Such geographic data is then made accessible to dealers in
order to help them understand characteristics of their locations, the local demographics, and
regional differences which may help them make better business decisions. GM is also able to track
dealer performance in a way that can influence their relationship with their dealers.
General motors recognises that Big Data requires a new way of working, and is therefore reported to
be undergoing a cultural change towards becoming data-driven. The company is said to be bringing
back 80% of its IT workforce in-house to enable organic growth is this regards. The company also

21.
21 | P a g e
emphasizes that all new and existing employees must learn and adapt to the digital and data-driven
trend (Rijmenam, 2014).
3.2 Analysis
Organizations are always looking for ways to create new competitive edges. The two sample cases
presented in the previous section (section 3.1) show how data is being harnessed as a means of
gaining competitive advantage, from Amazon’s creation of new business value or even entirely new
business lines, to GM’s cost saving techniques of telematics, to their improved business processes of
marketing and increased satisfaction of dealers… the list of possibilities with data is endless.
This section analyses the business practices of the two companies discussed from 3 perspectives:
 The potential business value in Big Data based on the applications discussed and how they are
developing competencies required to maximise these business values.
 The management actions taken – strategic, cultural and governance – to support the mission
of becoming data-driven.
3.2.1 Maximising business value of Big Data
 New customer value and increased loyalty for Amazon
Amazon is using data to achieve personalization at scale. Hence each one of its 260 million users is
presented with a customized amazon.com whenever they visit. Amazon uses data collected from
user engagements to understand user preferences, this data is used in real time by their
recommender systems to suggest trending products that each customer might be next interested in
whenever they visit the amazon.com website. This level of personalization creates additional value to
the customers and drive customer loyalty.
Competency developed: Datafication of customer engagements. Amazon has to ensure that
channels through which customers interact with their service (such as online and mobile) record and
report data from customer purchases, searches, and catalogue browsing. This data will be analysed
to understand customer preferences and behaviour for use by their recommender systems.

22.
22 | P a g e
 An entirely new business created for Amazon
Amazon’s advertising revenue was valued at $500 million because advertising was only on-site. This
was never a core business for Amazon whose primary source of revenue was ecommerce. Amazon
has now packaged its knowledge of customer’s purchases and future purchase intent through data
collected on its site and used this to launch a cross-site advertising system that allows other websites
to earn revenue by hosting Amazon’s ads. This instantly creates a new strong opposition for Google
in the online advertising industry – one which they had previously dominated with their $38 billion
business.
Competency developed: Development of internal processes required to serve advertising data to
external websites as well as manage ad listings self-service by marketers.
 Improved Marketing operations at General Motors
The case study discusses how General Motors is analysing data to build profiles of customers. Social
class of customers are inferred from data collected and GM is therefore able to target its marketing
more accurately for either their midrange or luxury autos.
Competency developed: Enabling datafication of both internal sales processes and external
customer engagement points from which these data are collected.
 New value for Customers and Dealers at General Motors
GM is using data collected from car parts to understand each car model as well as each car. This
knowledge is then made available to car owners through dashboards and mobile phone apps, saving
owners from emergency car breakdown. This offers increased value for customers which in turn drive
customer loyalty. GM is also collecting data about car servicing and purchases versus where the
owners come. Making such geographic data with dealers allows the dealers compare their
performance against expected performance and make necessary changes to pricing and marketing
strategies. This brings increased value to the service offered to dealers.
Competency required: Customer engagement point in the case of Telematics are the car parts
themselves. These parts are datafied by GM using sensors and 4G wireless communication. For the
dealers, GM develops internal capabilities to allow dealers controlled access to analysed geographic
information.

23.
23 | P a g e
 Huge Cost savings at General Motors
Using telematics as discussed in the creation of new value for customers above, GM is also able to
understand their car models better. Transferring this knowledge into product design increases the
reliability of their cars and saves them from costs associated with recalls.
Competency required: Internal processes required to analyse collected data for new knowledge on
car parts and other actionable insights.
3.2.2 The management imperatives of Big Data
 Supporting innovation through a strong data-driven culture at Amazon
Amazon started as a data-driven company. All customer engagement channels had always been
digitalized. The data-driven culture is therefore well seated as a foundation for innovation rather
than being a disruptive factor to manage. Amazon has become a popular example due to its
prevalence as a purely data-driven company and the effect this has had on its disruption of both the
publishing and retail industries. The developments mentioned in the study such as the continuous
improvement of their recommender engine and the inter-site advertising system are borne out of
their available data. Creativity and innovation at Amazon can be said to be fully supported by their
well-established data-driven organizational culture.
 Supporting General Motor’s adoption of data through strategy
General Motors is undergoing a Cultural change towards becoming data-driven, as reported in the
case study. The company is engaging all internal operations in this mission, having already collected 3
Petabytes worth of data from product development, manufacturing, customer care, procurement,
logistics, sales, marketing and finance. For GM, the data-driven culture is a disruption well managed.
The company is strategically driving its initiative and this is clear from the fact that the organization
has set aside funding worth $546 funding on data centres, and is recruiting skilled workforce to
ensure that all digital operations are implemented in-house. The case study reports that 80% of the
IT workforce is to be brought in-house.
Another evidence of a well formulated data strategy is the training of staff to be delivered across the
entire organization to ensure that knowledge is made available as a way of ensuring the success of
their data-driven mission.

24.
24 | P a g e
4.0 Conclusion and Recommendations
4.1 Conclusion
Through computerisation of business processes, IT had always supported business. Then came the
Internet boom to revolutionise Enterprise IT – from how value is created, to how businesses operate
and how value is delivered to customers. The current trend is digitalization through the innovative
use of Social media, Mobile, Internet of Things, Big Data, and Cloud computing. Data is a natural
“exhaust” or end product of computerisation and digitalisation. Fortunately, many organizations no
longer treat their data as waste, but as asset from which they seek to achieve the promise of Big Data
-- achieving new competitive advantages through the use of data. However, many do not understand
that there is an attitude that needs to be nurtured, which is of equal or more importance than the
choice of tools to be invested in. While it is important to select the right tools for each Big Data
initiative, this work has focused on the development of competencies required for organizations to
become data-driven using three dimensions:
 Datafication of internal operations from which useful data can be generated. Such data
reveals insights that can be used to save costs or optimize business operations. The question
to ask here is “how do we generate more data from our internal processes?”
 Datafication of external customer engagement and service delivery channels to ensure that
sufficient data is generated from which insights about customer behaviour and preferences
can be generated. The question to ask here is “how much data can we generate through our
customers’ use of engagement channels?”
 Making necessary management changes to nurture and support the adoption of sustainable
data-driven initiatives. Such changes were covered from the standpoints of data governance,
organizational strategy and culture. The questions here are: “Is there sufficient expertise in
the organization?”, “Is there sufficient knowledge of what is required to sustain the data
culture?”, “Is there an overall Data strategy guiding our collection of data (where and when to
collect)?”
Common Big Data pitfalls were discussed with advises from thought leaders on how they can be
avoided. Some criticisms were also analysed from various point of views.

25.
25 | P a g e
4.2 Recommendations
Organizations need to constantly re-assess their service delivery channels from a data generation
perspective. It is important to ensure that these channels leave trails of user interaction that are
being captured in data forms (datafication), for analysis. Customers should be encouraged or even
incentivized to use such channels that are mostly datafied (optimised to collect and report detailed
data on user interactions) so as to gain knowledge on customer behaviour and preferences.
Fortunately, these channels such as mobile, social media or online are usually more convenient for
the user and therefore should be positioned and communicated as so. It is a win-win eventually
because the more understanding an organisation has about how these channels are being used, the
better these channels can be developed to deliver more convenience and even more personalization
to the customers.
Radio streaming services threaten radio stations as the digital camera did to the camera film
business. Netflix to TV, PayPal to Credit card companies, and the list goes on. Digitally born
companies and business models have always had the data advantage. However, the key to
competitive advantage through data for successful data-driven companies will be in looking beyond
simply digitalising their operations, to ensuring thorough strategy-backed datafication and analytics
to mine actionable insights.
Smaller organizations should also keep in mind that lower cost analytic tools are now available and
will provide good value where properly applied. Data-driven companies become more customer
focused because they learn the most about their customers and can therefore meet their needs
more closely – an advantage that is not related to the size of a company. These data-driven
companies also have optimised value chains because of the continuous learning and adaptation that
is provided from the analysis of data generated by internal business processes and operations.
Furthermore, it is important to note that data does not need to become Big Data before insights can
be derived. Organisations should start small, using available data, and data that is easily accessible,
while nurturing and sustaining the data-driven culture through education, data strategies, and due
governance.
As Bell (2014) asserts, when organizations let data drive strategic decisions, instead of matching
competitor moves, innovation will rather bubble up from knowledge revealed through customer
behaviour and will be better inclined towards addressing the needs of customers. Furthermore, new
customer segments will be easy to spot, and the culture of creativity through customer insights will

26.
26 | P a g e
keep data-driven organizations ahead of the competition. Data-driven companies will have
advantages in forms of optimized business processes, new business models, new customer value,
enhanced delivery channels, increased customer satisfaction, and ultimately better chances at
organizational success.

27.
27 | P a g e
5.0 References
Amazon (2013) Company overview. http://phx.corporate-ir.net/phoenix.zhtml?c=176060&p=irol-mediaKit
(Assessed 11th
November 2014)
Baines, D. (2013) 'Big Data: not just a lot more data', Prescriber, 24(13-16), 7-8
Baldwin, H. (2014) 'Ready for 'digital transformation'?', Computerworld, 9, 22-24
Barber, E. (2014) 'GM Lawyer Increases Death Toll From Recalled Cars', Time.com, 16th
September
Bell, B. (2014) How Data Will Drive Business Strategy in 2014.
http://www.forbes.com/sites/techonomy/2014/01/23/how-data-will-drive-business-strategy-in-2014/
(Assessed 20th
October 2014)
Bell, P. (2013) 'CREATING COMPETITIVE ADVANTAGE USING BIG DATA', Ivey Business Journal, 77(3), 4-8.
Berinato, S. (2014) 'With Big Data Comes Big Responsibility', Harvard Business Review, 92(11), 100-104
Brynjolfsson, E., Hammerbacher, J., & Stevens, B. (2011) 'Competing through data: Three experts offer their
game plans', McKinsey Quarterly, 4, 36-47.
Bughin, J., Livingston, J., & Marwaha, S. (2011) 'Seizing the potential of 'big data'', McKinsey Quarterly, 4, 103-
109
Cukier, K. (2010) Data, data everywhere. http://www.economist.com/node/15557443 (Assessed 17th October
2014)
Cukier, K., & Mayer-Schoenberger, V. (2013) 'The rise of big data: how it's changing the way we think about
the world', Foreign Affairs, 92(3)
Cumbley, R., & Church, P. (2013) 'Is "Big Data" creepy?', Computer Law & Security Review, 29(5), 601-609
Debortoli, S., Müller, O., & Brocke, J. (2014) 'Comparing Business Intelligence and Big Data Skills', Business &
Information Systems Engineering, 6(5), 289-289.
Dombrowski, J. (2014) 'Too much information', Mortgage Banking, 6, 54-55.
Dutcher, J. (2014) What Is Big Data?. http://datascience.berkeley.edu/what-is-big-data (Assessed 3rd
September 2014)
Friedman, U. (2012) 'Big data: A short history', Foreign Policy, 196.
Gantz, J.F., Reinsel, D., Chute, C., Schlichting, W., McArthur, J., Minton, S., Xheneti, I., Toncheva, A., &
Manfrediz, A. (2007), The Expanding Digital Universe. http://www.emc.com/collateral/analyst-
reports/expanding-digital-idc-white-paper.pdf (Assessed 15th October 2014)
Gartner, (2013) 'Gartner Identifies Top Vertical Industry Predictions for IT Organizations for 2014 and Beyond',
Business Wire (English), 10
George, G, Haas, MR, & Pentland, A. (2014) 'BIG DATA AND MANAGEMENT', Academy of Management
Journal, 57(2), 321-326.

28.
28 | P a g e
George, G., Haas, M.R., & Pentland, A. (2014) 'From the editors—Big data and management', Academy of
Management Journal, 57(2), 321-326
Global DataPoint (2013) 'Global IT spend set to hit $3.8tr in '14', Global DataPoint (London, England), 12th
October
Griffin, J. (2011) 'DATA GOVERNANCE DEFINED', Information Management (1521-2912), 21(3), 10-12
Grossman, D. (2014) Google to buy Nest Labs for $3.2bn. http://www.bbc.co.uk/news/business-25722666
(Assessed 17th October 2014)
Higgins, T. & Jeff, P. (2014) 'Another round of GM recalls announced', Times, The (Trenton, NJ), 24th
July
Hurwitz, J. Nugent, A., Halper F., & Kaufman M. (2013) Big Data for dummies. Oxford: John Wiley & Sons
Iyengar, R. (2014) Facebook Completes Its $22 Billion Purchase of WhatsApp.
http://time.com/3477028/facebook-whatsapp-19-billion-dollar-deal/ (Assessed 17th October 2014)
Jen Leo, S.T. (2009) 'Try booking it with Bing', Daily Times-Call, The (Longmont, CO), 28th
June, p. 8.
Jim, E. (2013) 'Do You Need Big Data Governance? Maybe.; Sunil Soares discusses his new book and the
unique governance implications of “emergent” big data', Information Management (USA), 25th
January
John Gantz, J., & Reinsel, D. (2012) THE DIGITAL UNIVERSE IN 2020: Big Data, Bigger Digital Shadows, and
Biggest Growth in the Far East. http://www.emc.com/collateral/analyst-reports/idc-the-digital-universe-in-
2020.pdf (Assessed 15th October 2014)
Laney, D. (2001) 3D Data Management: Controlling Data Volume, Velocity, and Variety.
http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-
Velocity-and-Variety.pdf (Assessed 11th
November 2014)
Leber, J. (2013) Amazon Woos Advertisers with What It Knows about Consumers.
http://www.technologyreview.com/news/509471/amazon-woos-advertisers-with-what-it-knows-about-
consumers/ (Assessed 11th
November 2014)
Lycett, M. (2013), ''Datafication': making sense of (big) data in a complex world', European Journal of
Information Systems, 22(4), 381-383
McAfee, A., & Brynjolfsson, E. (2012) 'Big Data: The Management Revolution. (Cover story)', Harvard Business
Review, 90(10), 60-68.
McAfee, A., & Brynjolfsson, E. (2012) 'Big Data: The Management Revolution. (Cover story)', Harvard Business
Review, 90(10), 60-68.
Moore, G. (2011) Systems of Engagement and The Future of Enterprise IT.
http://www.aiim.org/documents/content-management-future-history.pdf (Assessed 17th October 2014)
Nature (2008) 'Community cleverness required', 2008, Nature, 455(7209), 1-1
NetEnrich, (2014) 'NetEnrich Outlines Five Business Technology Predictions for 2015', Business Wire (English),
12
Nokia (2014). Our Story. http://company.nokia.com/en/about-us/our-company/our-story (Assessed 17th
October 2014)

29.
29 | P a g e
Parmar, R., Mackenzie, I., Cohn, D., & Gann, D. (2014) 'The New Patterns of Innovation', Harvard Business
Review, 92(1/2), 86-95.
Press, G. (2013) A Very Short History Of Big Data. http://www.forbes.com/sites/gilpress/2013/05/09/a-very-
short-history-of-big-data (Assessed 9th October 2014)
Press, G. (2014) 12 Big Data Definitions: What's Yours?.
http://www.forbes.com/sites/gilpress/2014/09/03/12-big-data-definitions-whats-yours (Assessed 9th October
2014)
Rijmenam, M.V. (2013) DataFloq, How Amazon Is Leveraging Big Data. https://datafloq.com/read/amazon-
leveraging-big-data/517
Rijmenam, M.V. (2014) Three Use Cases of How General Motors Applies Big Data to Become Profitable Again.
https://datafloq.com/read/three-use-cases-general-motors-applies-big-data-be/257 (Assessed 13th
November
2014)
Saporito, P. (2013) 'The 5 V's of Big Data: value and veracity join three more crucial attributes that carriers
should consider when developing a Big Data vision', Best's Review, 7, 38-38
Singh, S. (2014) Could Big Data In Cars Have Saved Mary Barra From Embarrassment In Senate Hearing?
http://www.forbes.com/sites/sarwantsingh/2014/04/02/big-data-in-cars-could-it-have-saved-mary-barra-
from-embarrassment-in-us-senate-hearing/ (Assessed 14th
November 2014)
SINTEF (2013) Big Data, for better or worse: 90% of world's data generated over last two years.
www.sciencedaily.com/releases/2013/05/130522085217.htm (Assessed 13th
November, 2014)
Spann, M. (2013) 'Interview with Jorg Lubcke on 'Digitalization of Business Models in the Media Industry'',
Business & Information Systems Engineering, 3, 199-200
Sucha, M. (2014) 'Beyond the Hype: Data Management and Data Governance', Feliciter, 60(2), 26-29.
Turner, V., Gantz, J.F., Reinsel, D., & Minton, S., (2014) The Digital Universe of Opportunities: Rich Data and
the Increasing Value of the Internet of Things. http://idcdocserv.com/1678 (Assessed 17th October 2014)
Young, A. (2014) GM Recall Senate Hearings: Senators Propose Criminalizing What GM Has Admitted Doing,
Here’s The Full Text Of The ‘Hide No Harm’ Act. http://www.ibtimes.com/gm-recall-senate-hearings-senators-
propose-criminalizing-what-gm-has-admitted-doing-heres-1630300 (Assessed 14th
November 2014)