Big Data – Big Deal?1
Edison Lim Jun Hao (Edison.lim.2013@sis.smu.edu.sg), 1st Year Student, Bachelor of
Science (Information Systems Management), Singapore Management University
Executive Summary
“Data has become a torrent flowing in every area of the global economy” (Mckinsey, 2011). With the
convergence of business and information technology, a myriad of information is available for firms and
its business operation. Businesses churn out an enormous amount of transactional data, capturing
trillion over bytes of information from its suppliers, consumers and business partners. Also, as more
devices are connected in the age of Web 3.0 (Internet of Things), devices such as smartphones, smartmeters, automobile, and machinery could now communicate over a network, giving rise to data that is
high in volume, velocity and variety. In a digital age, consumers and businesses create a burgeoning
amount of data trail as they go about their day.
Digital information is omnipresent and omniscient. What was once the interest of only the academia is
now becoming increasingly relevant for business leaders in every sector of the economy. The ability to
store, collect and analyse data through the use of computing technologies has given an abundance of
information. These information could be used to derive trends and intelligence which could give
companies an edge over its competitors.
Besides the valuable business intelligence that Big Data Analytics is able to provide, Analytics is also
a key technology driving the realisation of futuristic concepts such as Internet of Things, Smart Homes
and Autonomous Cars. In addition, Big Data Analytics can potentially be able to transform how
pharmaceutical companies and financial institutions work by unlocking capabilities that was
impossible in the past.
In this research paper, we will examine the fundamentals and origins of Big Data Analytics. With the
understanding of what is analytics, we will also discuss what constitutes the business intelligence
driving the advancement of Big Data Analytics. Given these intelligence, we will explore how Big Data
Analytics is gradually changing our economy today, and how it will revolutionise our society in the
future.
1. Introduction
Ronald Raegan2 once said, “Information is the oxygen of the modern age”. This provocative statement
made by the 40th President of the United States captured the essence of information and emphasized on
its importance in society. Since the dawn of time, information has been synonymous with development.
The availability of information could bring in great insights which is essential for progression. This
view is echoed in a statement made by Kofi Annan in 1997 to the United Nations, whereby Kofi Annan
argued amiably about how Information is central to development (United Nations, 1997). With the
knowledge that can be derived from Information, it aids to the development of a society by contributing
to engineering development and management efficiencies. Thus, information is the oxygen for modern
age, and is essential for the progress of society.
The prevalence of technology has given us an abundance of information in the form of data, and this
amount of information has been increasing exponentially. In 2004, the biggest data warehouse in the
world was owned by Wal-Mart, comprising of approximately 500 terabytes storage (as cited in IDA,
2012). This amount of data pales in comparison to the figures in 2009, whereby the data warehouse of
eBay was estimated to be about eight petabytes (as cited in IDA, 2012). With the rise of digitalization
and internet prevalence, enterprises has amassed a large amount of information about their consumers,
1
2
This paper was reviewed by Lim Jun Hao and Thiam Pei Shan
Ronald Raegan is the 40th President of the United States of America (1981 – 1989)
1
suppliers and operations (IDA, 2012). Yet, the growth of information is not showing signs of abating
and is expected to increase exponentially in the future due to the development of technologies such as
semantic web and artificial intelligence (Bort, 2014). According to the study by IDC Digital Universe
Study in 2011, 130 exabytes3 of data were created and stored in 2005, and this is projected to reach
7,910 exabytes of data by 2015 (John & Gantz, 2011).
With the huge amount of information at bay, the management of information amassed great interest
amongst business leaders as valuable insights and market trends can be obtained to improve efficiencies
and profit margins. Furthermore, as information is synonymous with development, the efficient
management of these burgeoning amount of information – otherwise known as Big Data Analytics, will
thus be a shaping force for the future.
What is Big Data Analytics?
In examining what is the context of Big Data Analytics, it is important to define what is ‘Big Data’ and
‘Analytics’.
By definition, Big Data refers to data whose size are beyond the ability that a conventional database
software is able to capture, manage and analyse (Mckinsey, 2011). These datasets comprises of
information which consumers and enterprises generate through various mediums such as Internet,
mobile phone and social media. In a digital world, we generate a huge amount of data, creating
approximately 12 terabytes of data in tweets alone each day (Gobble, 2013), and the rate of data
generation is showing no signs of slowing down. According to McKinsey (2011), 90% of the data in
the world today was created over the last two years and there will be 44 times more of it by 2020. The
large amount of data sets translates to a large amount of information to be tapped, point out to its
promising possibilities in the future.
To fully comprehend the possibilities of Big Data, it is important to note that Big Data is not just about
how much data we have. The volume of data, alongside with the velocity (frequency) of data
transmission and different varieties of data comes into equation to form the 3Vs of Big Data (IDA,
2012).
Figure 1: The 3Vs of Big Data (Reproduced from Datameer, 2014)
Though data has the potential to unlock valuable information, the tremendous amount of data is of little
value on its own. Furthermore, data storage is expensive and managing the data warehouse is an
engineering complexity which incurs high cost and a huge amount of skilled personnel. To improve the
cost effectiveness of housing such amount of information, it is imperative for firms to undertake
analytics to fully utilize the value of such data (Gartner, 2013) and derive valuable insights to contribute
to the firm.
Since Analytics is the discovery and communication of meaningful patterns in data (Wise, 2011) and
‘Big Data’ refers to large complex datasets, Big Data Analytics is thus the process of analyzing
3
1 exabyte is equivalent to 1 billion gigabytes (GB)
2
tremendous amount of data to explore unknown correlations, hidden patterns and other useful
information. With the valuable information at hand, businesses could potentially utilize it to reduce
inefficiencies, improve business value and enable new technologies that could lead to societal changes.
2. Historical Context
Analytics was first conceptualized by Frederick Winslow Taylor in the 19th century. In his capacity as
an industrial engineer, Taylor sought to increase the efficiency of Pennsylvania's Bethlehem Steel back
in 1898 (Bridgwater, 2013). With time management analysis on the worker’s performance and the effect
of tools placements, Taylor believed he could derive what an average worker could produce in peak
conditions and streamline its efficiency (Wall Street Journal, 1997; Brightwater, 2013).
Analytics continued to gain popularity in America during the industrial revolution. In 1908, Henry Ford
studied and analysed the pacing assembly lines during the production of Model T (Bridgwater, 2013).
At a time where data analysis was an alienated subject, Ford believed and popularized the idea of data
analytics, leading to a century of intensive development of analytics in maximising industrial efficiency.
However, analytics was initially limited to industrial research due to its complexity and a shortage of
specialized talents. Data collection process is tedious and mathematical calculations are daunting tasks
for a human being to perform. The lack of technology to overcome this problem of imprecision and
redundancy hence impeded the growth of analytics in the early 1900s.
The situation improved with the advent of Computers. The introduction of computers made statistical
calculations more precise and efficient. The increase in precision and efficiency improves the reliability
of analytics and was revolutionary in those time by changing how people perceive information. As
business operations becomes digitalized, information is stored in a Database Management System
(DBMS) which could be easily tabulated and analysed, a technology that is still widely used today.
As businesses utilizes computer systems for their operations, it gave rise to an abundance of data
stored in the servers. These data are valuable sources of information if it is analysed methodically.
Hence, the methodical analysis of computing data is the precursor to the fundamentals of Big Data
analytics that we know of today.
3. Current Situation
Over the last few years, the advancement of technology and the growing number of users on Internet
has resulted in a burgeoning amount of user-generated data. As people interact on social media
platforms and use the Internet for entertainment, education and work, the nature of these platforms
results in an enormous amount user-generated data which could be methodically analysed.
In addition, the convergence of information technology and business operations is ushering a new
economic system that is redefining the relationship between enterprises and its partners. The
connectedness of computer systems leads to the intertwining of multiple business verticals, and this
complexity makes it difficult for companies to manage through conventional methods. Hence, there is
a need for businesses to look for data-driven solutions in order to better manage their operations.
As such, the rise of these two trends gave rise to the demand for analytics. With the enormous amount
of data amassed from these trends, business leaders and institutions are increasingly aware of the value
that such information is able to provide.
Healthcare
In 2013, researchers at University of Pittsburgh made a remarkable medical breakthrough when they
discovered the genetic changes in the makeup of breast cancers. This was made possible by the use of
Big Data Analytics, in which researchers uses high-performance computing (HPC) to integrate clinical
data from electronic health records (EHRs) as well as genomic data for the patients and compared it
against tumour size, age and nodal status (Horowitz, 2013). This study revealed the possibility of
personalized treatment and medication by understanding the cellular structure of the human body.
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In addition, healthcare leaders can leverage on analytics to access the likelihood of disease outbreak
and manage the outbreak of a pandemic. For example, the Institute of High Performance Computing
(IHPC) in Singapore has formulated a stimulation model for epidemiological study. In the event of a
pandemic, the model can be used to access virulence of virus, providing authorities with the information
needed to formulate health advisories and activities (IDA, 2012).
In these two cases, analytics has played an indispensable amount of information that is essential for
institutions to advance their medical research. With a large information available from scientific
research over the years, computing technologies are required to sieve out valuable trends that are worth
noting. This saves medical professionals lots of time on research, therefore accelerating the
development of medicine.
Another success of Big Data Analytics in impacting healthcare lies with IBM Watson as Watson is able
to sieve through enormous amount of data and provide feedback based on the query. IBM’s new
intelligent supercomputer, Watson, has the ability to analyse and interpret human language. With this
user input, Watson can quickly process vast amounts of information to suggest options targeted to a
patient’s condition (IBM, 2012). Through the use of analytics, IBM Watson now has the ability to
suggest cancer treatment options, review treatments and authorize insurance claims (Henschen, 2013).
All these functionalities were previously impossible to do so due to the intrinsic complexity of data.
With the convergence of a super intelligent computer and complex analytics technologies, Watson could
make healthcare more accessible and efficient, therefore transcending medical expertise beyond
geographical boundaries. Though the accuracy of such medical information has not been certified for
practical usage, this breakthrough is an interesting technological innovation which medical
professionals could look forward to in the next few decades.
Figure 2: IBM Watson Computer (Reproduced from Russell, 2014)
Commerce
With the data collected in the enterprise’s servers and external sources of data such as Social Media,
data can be extracted to discover consumer patterns and organizational inefficiency. This process is
complex and requires elaborate efforts in information collection. With the information collected,
enterprises can hence perform analysis on it to derive meaningful trends. With the trends obtained,
businesses can hence apply it to marketing, sales, supply chain management, etc.
4
The framework below shows how firms may advance their corporate interests through the use of
analytics.
Figure 3: Suggestion on how firms can deploy analytics across business units 4
Information Collection
With the growing number of users on Social Media platforms, more information on people’s social
behaviour and preference can be obtained. In 2013, Facebook reached 1.06 billion active users on its
social network (Tam, 2013). Yet, the increasing number of social network users is showing no signs of
slowing down. In 2014, Facebook announced plans to acquire internet messaging application,
WhatsApp (Shih, 2014). This acquisition will add another one million users to the social network every
day, allowing more social information such as phone numbers and text messages to be analysed for
trends (Telegraph, 2014; Shih, 2014). The increasing number of users, alongside with the more usage,
will thus give enterprises more information which they can collect and analyse.
Simultaneously, as business operations are increasingly digitalized, enterprises has amassed a
burgeoning amount of data such as customer information, sales records and supply statistics. For
instance, in 2004, Wal-Mart has accumulated 500 terabytes of information from its sales customer and
operations alone (IDA, 2012). These data translates to great value if it is methodically structured and
analysed to derive trends which will help enterprises remain competitive.
Business Intelligence
In order to derive value from information, companies often use Predictive Analytics to perform trend
analysis. According to IDA (2012), predictive analytics is a set of analytical and statistical techniques
that are used to uncover patterns and relationships within large volumes of data. These predictive
analytics, evident in the software of Spotify and Amazon, could guess the user input with high certainty
by tracing the history records.
Though Predictive Analytics is useful for user-generated data, it may not be applicable to all data sets.
Hence, other forms of Analytics such as Graphical Analytics could be used in conjunction with
Predictive Analytics on datasets with three-dimension parameters through a graphical interpretation.
Though there are much more varieties of analytics, for brevity purposes, we will discuss how enterprises
uses predictive and graphical analytics to derive value.
4
The framework is an original work of the author
5
Applications
i.
Marketing
Big Data Analytics has the capability of obtaining customer’s assessment of branding and
marketing strategies. This is achievable via social network, in which users often tweets or post about
their experience in a store. Messages such as tweets and status updates could give enterprises an
idea of how well their product is perceived in the general public and identify market opportunities.
For instance, supposed a message such as “Today, I went to ABC café to get a cup of coffee, but
they do not have the option to add more shots to my latte. Definitely not coming back here
anymore”, it could reflect consumer dissatisfaction and consumer preference. These information
could help greatly with the business in identifying market opportunity and consumer needs.
Furthermore, companies are increasingly concerned about the proliferation of mobile and social
media as information circulated on these platforms could have an effect on potential customers.
With social media, information can be spread faster and hence, managing the social media becomes
an increasing concern to firms looking to upkeep its corporate image. Therefore, there is a need to
handle consumer expectations by collecting these information and look for signs of dissatisfaction.
One of such example of how businesses can enhance their marketing efficiencies is through
Radian6, a social media monitoring tool by Salesforce.com. Radian6 provides companies with an
event stream of active conversations happening over 650 million sources (Salesforce, 2014). With
such information, companies will gain a better picture of its branding and positioning and manage
itself through strategic planning.
ii.
Sales
In a research statement by Aberdeen, it is observed that companies who invest in Analytics tend to
outperform those who do not put in the necessary investment on key sales matrixes (Kucera, 2012).
Indeed, Sales team can leverage on analytics in ways consonant to marketing’s deployment.
Through the social media event streams, companies can obtain potential sales lead and discover
targeted demographic.
In addition, using analytics to understand the potential lead before being handed off to sales team
could significantly improves sales volume (Columbus, 2013). As it is noted that consumers are
heavily influenced by their peers (John Gantz, 2011), it is imperative for enterprises to note the
relationship of potential lead to its connections. Similar to how LinkedIn works in deciding the
“degrees” of connection, graphical analytics can analyse the relationship of potential lead to
existing customers. By understanding the customer, it could help the sales team to build better
rapport with the potential lead and understand his needs, and this translates to better sales
productivity and performance (Columbus, 2013).
Analytics is also widely used in e-Commerce, where companies can leverage on e-commerce’s
inherent advantage in data collection of information to tailor unique experience for their customers.
In e-commerce, companies are able to track and predict purchases for every user who is logged in.
By drawing correlation between past purchases through analytics, businesses could identify
purchase patterns and predict the next purchase. These recommendation could save time-constraint
consumer plenty of valuable time, which thus increase its likelihood of purchase (Press, 2014). For
instance, Amazon is able to know what items its customers have considered, something that retail
outlets are incapable of doing. As a result, Amazon has been deploying such analytics for a
sometime by recommending customers products which they are likely to purchase. With this
technology, it not only increases sales in online stores, but also forges a stronger customer
relationship by providing an easy and customized consumer experience.
Finally, analytics is vital for sales as it manages customers’ expectations. Analytics can be used to
observe public sentiments on services and product, which gives companies valuable insights on
customer experience. Amazon has attempted to increase service levels by reducing the shipping
time through predictive analytics (Marr, 2014). In what Amazon terms as Anticipatory Shipping,
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Amazon is able to ship out a product even before the customer decides to purchase it. Through such
technologies, it can reduce shipping time and enhance customer experience. Hence, the prevalence
of analytics could potentially shape and change the way companies build relationship with clients
and conduct sales for their products.
iii.
Supply Chain Management
By analysing the sales history, companies can utilize predictive analytics to ensure the right items
are on stock and anticipate peak periods of sales (Marr, 2014). This is important as it maintains
business operations and maximises profits for firms. In addition, analytics is vital in supply chain
management as it systematically assesses inefficiency in the chain. For example, Logitech
experienced complexities in its manufacturing of ‘Ultimate Ear’ earphones during the initial stages
of production. Due to the design of the earphones, ‘Ultimate Ear’ is both expensive and timeconsuming to manufacture (Cecere, 2013). However, through the use of analytics, Logitech
managed to identify phases of redundancy in its production lines and make necessary rectifications.
iv.
Fraud Detection
Business information is increasingly being managed by computer systems. The pressure to remove
inefficiencies and integrate supply chains meant that many companies are heavily dependent on IT
systems to support their business processes. This reduces the level of human intervention, which
traditionally acted as a form of fraud control (Ernst & Young, 2014). As a result, by placing reliance
on automation, companies are exposed to fraudulent practice. To counter such threats, companies
can turn to analytics for fraud detection through its methodical and efficient tracking system.
By analysing huge financial transaction data for anomaly and inconsistencies, datasets will help
banks to identify possible cases of fraudulent transactions, account inconsistencies and money
laundering. In the financial sector, analytics is useful in understanding money pathways. As money
transfer between bank accounts may require several intermediate bank accounts, graph analytics
can be used to decipher the relationships between different account holders (IDA, 2012). By making
it possible to spot anomaly patterns, analytics minimizes organisation’s exposure to fraud, and
hence enhances the security and integrity of businesses.
The use of analytics in detecting fraudulent practice has already been implemented in major
financial organisations. In order to confront financial risks, Visa has introduced analytics to
discover vulnerabilities in 2011. By 2013, Visa reported that the analytics programme has identified
$2 billion in potential fraud opportunities (Rosenbush, 2013). Therefore, analytics offers enterprises
the solution to negate fraudulent risks. In addition, by acting as a security mechanism within
computing systems, analytics gives companies the assurance in integrating business operations,
hence expediting the development of a highly integrated and connected business infrastructure.
v.
Strategic Management
In accessing feasibility of possible business expansions, firms are often faced with high risks and
low certainty due to inadequate knowledge of the markets. However, analytics has reduced some
of these risks through provision of trend analysis using diverse sources. Esri, a map analytical tool,
provides such information to business executives looking to expand their operations. With its
topological interface, Esri can note down data concentrations which signifies populated area with
high potential for possible business expansions (Esri, n.d).
In addition, by gathering information from enterprise servers and social media, analytics systems
are able to produce event streams from systems to indicate consumer preference. This is significant
as event streams provide more detailed and complete views of a business because the information
is at a finer level of granularity (W. Roy Schulte, 2013), hence this gives executives more
information to make strategic decisions for the firm.
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4.
Current Challenges for Big Data Analytics
Cost Effectiveness
Firms often see the potential of big data analytics in bringing value to their business capacity. However,
the perceived costs are too high for some firms to adopt analytics (Cecere, 2013). As industry is still in
its infancy, talents are scarce and thus expensive to hire (Boyd D. , 2011).
Furthermore, Big Data Analytics often require new database management system to manage
unstructured data. This process is a major dilemma for companies as it meant significant IT
infrastructure changes to their company. Due to the intricate connection between IT and business
operations, changing a database hardware will result in a radical change in the entire business systems
for firms (Boyd D. , 2011). The complexity and high cost incurred in the change in infrastructure is a
factor of consideration for firms to utilize Big Data Analytics in their business operations.
Difficulty in extracting information
Though analytics plays a huge role in helping firms to maximise profits and streamline operations, the
process of generating such trends is immensely challenging technically and legally. Though
theoretically, constructing algorithms may sound easy as it involves the application of knowledge which
we have already procured. However, in reality, the formulation of algorithms for Analytics is a common
dilemma amongst businesses. This is because understanding consumer behaviour is an uphill task as it
is affected by multiple variables. For example, Netflix offered a $1 million prize to any team in the
public that could query its information about users and build a recommendation system that is more
suitable for its users than the one it already had (Naone, 2011). In doing so, Netflix hopes to get a
ground-up perspective of its recommendation algorithms as understanding what constitutes a good
algorithm is an art of sophistication.
Though analytics holds the potential of finding inefficiencies within supply chains, it is often difficult
to connect nodes and construct correlations amongst complexity (Kucera, 2012). As supply chains
become more tangled with far-flung suppliers, business verticals intertwined and that made information
extraction an uphill task.
Scarcity of storage
Data is now being generated and collected in huge volumes, at high speeds, and in all kinds of varieties
- not only numbers, but also SMS messages, photos and videos (Miller, 2013). To manage this increase
in information rendered, enterprises struggle with the development of analytics technology due to
storage scarcity. Such storage solution is both technical and expensive to implement, and hence it
remains as a problem for firms looking to advance into the field of Big Data analytics.
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5.
Future Possibilities
Due to the intrinsic interdependency nature of data, future of Big Data is dependent upon future
information technology advancement. Over the next few decades, we can expect a few advancements
which can significantly affect how we perceive data.
Figure 4: Hype Cycle for Big Data, 2013 (Reproduced from Gartner, 2013)
In the graphical representation above, it shows the multitude of possibilities that Big Data Analytics
could bring to reality. For interest of brevity and focus, we will discuss the possibilities of Big Data
Analytics by segmenting them by sectors as listed:
1.
2.
3.
4.
Healthcare and Biomedical Industries
Government
Commerce
Consumer Lifestyles: Internet of Things and Automation
In the discussion of future applications of analytics in this section, it is not uncommon to note that some
applications are applied today. For example, fraud detection analytics is an application that has been
used by some organisations on selective accounts. However, as analytics is still a relatively new
technology, many of the applications we used today are still at its infancy and are mostly research
project that has not been implemented fully for industrial and consumer use. In addition, future
applications may include the convergence of different variety of analytics. For instance, social analytics
may be evident in some social networks for selective data. Yet, the potential can be expanded to include
voice analytics through phone calls and text message analytics, which is significant because the increase
in sample size will raise the accuracy of analytic calculations.
Therefore, in the discussion of future applications of analytics, we are keen to explore what analytics
could ultimately do without engineering and technological constraints, what we could achieve when we
have fully harnessed its potential, and also explore what are the implications it could have on society.
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HEALTHCARE AND BIOMEDICAL INDUSTRIES
Healthcare organisations around the world are challenged by pressures to reduce costs, improve
efficiency, better utilize its resources and to become more patient centric. However, the industry is
increasingly challenged with entrenched inefficiencies and suboptimal clinical outcomes (IBM, 2013).
The adoption of analytics can help healthcare organisations harness Big Data to create value and
increase healthcare service levels.
i.
Pharmaceutical Research and Development
Drug discovery and research is a interdisciplinary activity that requires scientists to
integrate information across various organisations and scientific databases (IBM, 2012).
The development of pharmaceutical products is a heavy investment in time and money.
However, one of the most perplexing question for pharmaceutical industries is to consider
which product to development and plan allocation of research money (Horner & Basu,
2012). One of the challenges faced by pharmaceutical companies is finding adequate
patients for clinical trials. As drug testing requires elaborate trials, patients are key to the
success of drug development. Product development will fail if insufficient data is gathered
due to the inadequate trial subjects.
As a result, pharmaceutical companies can leverage on analytics for evaluate feasibility of
drugs involvement. By computing the data extracted from multiple hospital database,
analytics can provide an accurate statistical analysis for the demand of drugs and access the
subjects available for clinical trials (Horner & Basu, 2012).
Therefore, the use of analytics has the potential to streamline strategic decision and
maximize efficiency. By reducing the time spent on deciding what drugs to research on,
pharmaceutical companies will be able to develop products in a shorter period of time,
therefore giving patients the medication they require before their conditions deteriorate.
ii.
Clinician Decision Support System (CDSS)
Big Data analytics can be implemented to advance Clinician Decision Support System
(CDSS). CDSS is a computer program that assists clinicians to make a better decision by
providing empirical evidences derived from patients’ data (Basu, Archer, & Mukherjee,
2012). With the advancement of analytics technology, these systems will soon be capable
of analysing patients’ records and compare them against official medical guidelines. In
doing so, analytics can trigger an alarm upon spotting anomalies and inconsistencies. By
alerting on potential errors such as adverse drug reactions, it is able to give physicians
adequate time to rectify the problem, and thus could reduce clinical fatalities due to errors
in prescriptions.
Furthermore, the development of image and video analytics will also significantly increase
the efficiency of clinicians. With analytics, medical images such as CT Scans and X-rays
can be analysed swiftly, hence shortening the time taken to perform pre-diagnosis. In
addition, Analytics could go into details about the image and analyse trends which the
clinician might have missed, hence increasing the accuracy of diagnosis whilst saving time.
Thus, the advancement of analytics could potentially transform the way medical professionals work by
cutting inefficiencies and improving accuracy of diagnosis. By reducing inefficiencies, Analytics could
increase the time physicians interact with their patients. This in turns realizes the potential which thus
raises healthcare services level considerably.
GOVERNMENT
Governmental agencies are affected by the advent of Big Data in a similar fashion as how commerce is
affected. With more technologies such as speech-to-text analysis (phone calls) and Internet monitoring,
analytics can bring value to governmental organisations in areas such as crime prevention and policy
making.
i.
Public Policy Planning and Management
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With the development of text analytics and voice analytics, governmental organisations may soon be
able to collect information from the public via telecommunications. Such development will significantly
enhance the scope and quality of Big Data Analytics as the sample size of collection grows larger
(Young, 2013). Similar to how Big Data Analytics has transformed the world of commerce by social
monitoring, the future of analytics could be so precise that governmental officials will have the
confidence in relying it for policy making (Datameer, 2014). This is possible as the future of analytics
could include data collection from multiple sources, and thus this increases both the breadth and depth
of analysis. Given the more holistic analysis possible from diverse sources of information, the precision
of Big Data Analytics will soon reach a precision that makes it suitable for use in the political sphere.
Other than providing more information for public policy planning, analytics is also able to help
government predict and manage adversities better. With a mixture of Complex Event Processing (CEP)
and Predictive Analytics, weather forecasts can be made more precise and this is significant for
countries to prepare themselves for weather adversities. For instance, with better forecasts on the
probability of flash-floods in Singapore, ministries will be able to implement precautionary measure by
conducting more routine checks for drain congestions which might otherwise be the cause of a flashflood in Singapore.
ii.
Fraud Detection and Law Enforcement
As mentioned in Section 3, Complex Event Processing (CEP) holds the capabilities of detecting frauds
by analysing the various pathways of fund transfer. This technology is significant to tax authorities to
detect and act against potential frauds.
As the capabilities of Big Data management expands, authorities will be soon able to automatically
collate and analyse huge volumes of data from an array of sources including Currency Transaction
Report (CTR), Negotiable Instrument Logs (NILs) and Internet-based activities and Commerce’s
transactions (IDA, 2012). By integrating these information with CEP engines in real-time, alerts can be
triggered swiftly once an anomaly is detected. With such technology, officials are able to respond
quickly to potential frauds and take necessary actions against them. This not only reduces time taken to
detect fraudulent practices, but also improves the accuracy of detection. Hence, analytics has the
capability to make improve fraud detection process for tax authorities, which is essential for government
officials in constructing a robust legal framework.
COMMERCE
The use of analytics has the potential to revolutionize the scene of commerce by changing how we
work. By providing capabilities that were previously unimaginable such as analysing SMS messages
and social media monitoring, Big Data Analytics can bring in new revenue stream for firms to remain
competitive.
Today, we can see some early trends of commerce utilizing analytics to attract consumers and engage
them. For example, Amazon tracks purchases and recommend item that the consumer is likely to
purchase. Also, Spotify tracks the song a user listens to and recommends songs that the customer may
like (John Gantz, 2011). This increases the interaction between businesses and its customers, and hence
enhances customer engagement. However, the current form of analytics is met with limited success as
the scope of information collection is limited. For instance, for Amazon to track your purchases, the
customer must first be a customer and have registered with Amazon. The information obtained from
web browsing behaviour is not sufficient for effective analytics, and thus, there is an increasing need
for firms to widen the scope of information collection in order to cater to different consumer needs.
With the possible realisation of an ‘integrated network of systems’ (Gobble, 2013), enterprises could
theoretically monitor communications such as telecommunications (Voice-to-text analytics), social
media (Social Media Analytics) and location mapping (Geospatial Analytics).
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Social
Media
Analytics
Geospatial
Analytics
Speech
Analytics
Valuable Insights for Firms
Figure 5: Relationship showing how Analytics derive value for firms
This multi-media form of analytics widens the scope of information collection and offers businesses
with more data to derive valuable market trends. With the trend analysis retrieved, businesses could
then use these information to engage the customer more effectively (Kucera, 2012). For instance, with
the use of information obtained from purchases and social media, Tesco5 is experimenting with the use
of predictive analytics to track consumers and target specific advertisements with vouchers to maintain
customer loyalty (Ferguson, 2013). From an enterprise’s point of view, this is a significant opportunity
for firms to maintain consumer base in an increasingly competitive market with multiple competitors.
CONSUMER LIFESTYLES
i.
Internet of Things (Web 3.0)
According to Cisco's Internet Business Solutions Group (IBSG) (Evans, 2011), 50 billion devices will
be online and linked to the Internet by 2020. Also, Gartner reported that approximately 230 billion
devices will be connected in the era of Web 3.0 (as cited in IDA, 2012). Though the approximations in
projections differs significantly, the two sources both indicates an increase in the number of devices
connected. With the rise of connectivity, the use of sensors will enable devices to connect with each
other. The wide range of actuators and sensors will transmit huge amount of data that is high in velocity
and combined volume which needs to be processed. To maintain semantic web and its technologies, the
use of analytics is indispensable as such information is too huge for conventional database systems to
manage (IDA, 2012).
The Web 3.0, or more commonly known as Internet of Things (IoT), is a technology advancement that
will be developed with the prevalent use of devices. In theory, the Web 3.0 brings in exciting innovation
such as smart highways, smart grids and an automated home. With the use of Web 3.0, the
communication between devices can potentially transform the way we live in the future.
The development of Internet of Things will drive key innovations such as Smart Home and Smart Cities.
These innovations, made possible by the semantic web, will potentially revolutionise how consumers
live by offering great convenience over control of surroundings.
5
Tesco is a British multi-national general merchandise retailer that is headquartered in England, United
Kingdom.
12
However, as the semantic web involves a huge network comprising billions of devices, it is essential
for a technology to manage the flow of information. Through the use of Big Data Analytics, information
from Web 3.0 that are high in volume, velocity and variety can be quickly processed and analysed (IDA,
2012). Hence, the development of Web 3.0 and advancement of analytics are technologies that
complements one another.
Figure 6: Web 3.0 enables innovation such as Smart Home (Reproduced from Sunshine, 2014)
The Internet of Things, with analytics as a key supporting technology, takes information from sensors
located in all kinds of consumer goods to develop actionable application and insights. For example,
through the use of small sensors installed in car that transmit traffic information autonomously, a more
efficient traffic management system could be developed in the near future (IDA, 2012). Without the use
of analytics, the management of all these information from millions of cars around the world is
technically impossible given our conventional database and fixed schemas. Therefore, as technology
advances to make the Internet of Things a reality, there will be a surge in demand for Big Data Analytics
to make sense of all these information.
ii.
Driverless Car
Big Data analytics, along with the introduction of Internet of things, will soon make driverless cars a
reality. Today, Google is currently working on a project on driverless car which utilizes autonomous
technology to make driving safer, enjoyable and efficient (Google, 2014). Even though a prototype has
been developed by Google, researchers estimate that such a car will only be available for mainstream
usage by 2040 (Young, 2013). However, given the complexities of driving which involves analysing
inputs from multiple sensory devices, the development of driverless cars hinges onto the development
of analytics to engineer new technological breakthroughs so as to make autonomous driving more
dependable.
The driverless car utilizes two key technologies – analytics and semantic web. By using an army of
advanced sensors known as Advanced Driver Assist Systems (ADAS), information can be gathered and
analysed to enable cars to operate autonomously and react to changes in the environment (KPMG,
2012). This signifies the importance of analytics in making the operation of google driverless cars
possible.
13
Figure 7: With the use of Sensors and Analytics as its 'brain', driverless car could change the way we commute (Reproduced
from Raffensperger, 2013)
In addition to automating driving, analytics also has the potential to change how we maintain our
automotive vehicles. A formerly-Swedish, now Chinese owned car maker Volvo, is carrying out
research in exploring the possibility predictive maintenance analysis. Through the use of hundreds of
sensors placed around the engines, vibration and noise frequencies of individual components are
recorded and analysed (Young, 2013). Such system could alert drivers of the need the send the car for
servicing upon detection of abnormality within the machine. With a better knowledge of the
serviceability of the car, drivers can thus ensure the road worthiness of their vehicles on road, thus
reducing the likelihood of accidents that results from faulty vehicle components.
The use of analytics is also essential for ensuring the safety of driverless cars. For the concept of
driverless cars to work safely, it is essential for cars to work as a collective instead of an individual
machine. By installing multiple communication devices in cars, dedicated Short-Range Communication
(DSRC) can make vehicle to vehicle (V2V), vehicle to infrastructure (V2I), possible (Kuchinskas,
2013). With the establishment of communications, geospatial analytics can be applied to determine and
track the car’s location and determine the safest possible route based on weather conditions. Also,
predictive analytics may be applied to determine the possibility of collision. Such information is vital
for ensuring safety as vehicles can be automated to take precautionary measures upon determining such
risks.
The development of analytics will accelerate the development of driverless cars, making autonomous
vehicles that is fast, safe and efficient a reality.
6. Big Data Analytics in Singapore
The Infocomm Development Authority of Singapore (IDA) aims to develop Singapore into an
analytics hub and has been active in various initiatives to advance research in this area. With IDA’s
“Internet of Knowledge” efforts, IDA accelerates demand for analytics and provides seeding for early
adoption of analytics in various industries. This is achieved through development industry and
manpower capabilities, establishing scalable and secure data exchange platforms and formulation of
suitable data policies (IDA, 2012). As a result of these initiatives, various institutes is researching on
various sectors of data analytics to prepare Singapore for future challenges in infocomm industry.
SMU Living Analytics Research Centre (LARC)
The Singapore Management University (SMU) Living Analytics Research Centre (LARC) is a joint
project between SMU and Carnegie Mellon University. With research grants of $26 million from the
14
National Research Foundation, LARC seeks to advance the national’s effort in developing business
analytics (SMU, n.d.). LARC mainly focuses of combining technologies in Big Data (Statistical
Machine Learning and Large-Scale data mining) with behavioural and social network researches. Some
of the more notable projects are analysis in retail banking and information goods consumption (SMU,
n.d.). Through these researches, LARC aims to develop the analytics capabilities of Singapore, yet at
the same time advances social science researches about consumer behaviour.
7. Implications of Big Data Analytics on other industries
This section will look at what the rise in Big Data Analytics will mean for society and other industries.
It will provide some insights on how Big Data Analytics will shape the future.
Economic Impacts – Major IT Infrastructure reformation
As Big Data is great in volume, high in velocity of data transmission and possesses great variety,
traditional database may no longer be able to handle the demands of Big Data due to technical
limitations. As the nature of data expands in variety and transmit in great volume and velocity, there is
a pressing need for a new database management system that is flexible to handle data today.
Recently, the introduction of a NoSQL Database Management System6 (hereafter known as “NoSQL
DBMS”) offers solution by providing a flexible database platform. NoSQL DBMS does not have a
fixed schema and are non-relational, hence it permits more flexible usage and allows high-speed access
to the various data collected (IDA, 2012). Though NoSQL DBMS is still at its infancy today, it is
projected that NoSQL DBMS will be the de facto standard in data warehouses in the future (IBM,
2012).
However, as majority of data warehouses are built on traditional database management system
(hereafter known as “RDMS”) today, it will require a major hardware replacement and system upgrade
to change to NoSQL DBMS. Other than the high cost that will incur in performing system upgrades,
such reformation will render the existing hardware and software as irrelevant.
Thus, the rise of Big Data Analytics will force enterprises to reconfigure their IT infrastructure in order
to prepare for technologies and new capabilities that comes from the rising phenomenon of Big Data.
Social Impact – Jobs in IT
The rise of Big Data Analytics translates to the increase in demand for data scientist to manage and
analyse the abundance of information. The rising popularity of Big Data Analytics leads to the increase
of highly analytical and specialized jobs in data analytics, while at the same time threatening the jobs
of millions of traditional database engineers.
By 2018, USA alone could face a shortage of 190,000 people with analytics skills and 1.5 million
trained employees with knowledge of Big Data Analytics to make effective decisions (Mckinsey, 2011).
Demand for skilled analysts, also known as Data Scientist, is expected to increase by a further 24.5%
by 2020 following the rise of Big Data (Bort, 2014), therefore leading to creation of new jobs for
specialized analysts.
Though the demand for specialized IT personnel are high and salary is lucrative, some existing
engineers may find it difficult to re-master new skillsets to stay relevant. As previously discussed, the
advent of Big Data will lead to a change in the database management structure for majority of data
warehouses around the world. Since the older database systems is slowly phased out with the advent of
new systems, the skillsets of traditional database engineers will also become increasingly irrelevant.
Hence, unless existing engineers upgrade their skillsets to stay current with technological
advancements, their jobs will be at risk of being obliterated.
6
NoSQL means No Structured Query Language. Structured Query Language is a language that is synonymous
with RDMS. By terming it NoSQL, it indicates that the new database is non-dependent on SQL. This technical
detail is not required for the comprehension of subject on databases.
15
Socio-Economic Impact – Role of Intelligence in organisation
Before the advent of Big Data analytics, information is often not utilized to derive value and enterprise
often struggled with the rationale of storing data which is not cost efficient. Previously, data and
information was only made available through the conduct of surveys and market research. These
research methodologies are not only time-consuming, but also require heavy investment to initiate
without promises of results (IDA, 2012). Today, due to the availability of affordable analytics, any
company with sufficiently large data sets can become a key player in using information to advance its
corporate interest.
In a survey conducted by the Big Data Insights Group (Datameer, 2014), many companies are seeing
the value of Big Data analytics to their organisation.
PERCEPTION OF BIG DATA ANALYTICS
Still unfamiliar
with Big Data
17%
Implementing or
have implemented
Big data Solutions
33%
Researching or
Sourcing information
50%
Figure 8: Statistics on Perception of Big Data Analytics, reproduced from Boyd C. , 2012
From the statistics obtained, we concur that there is an increasing awareness of the value of Big Data
for organisations. With only 17% of the company within the sample size of 300 that is not familiar and
not developing Big Data Analytics, it indicates that market demand for such technology is high.
The advancement of Big Data Analytics has changed the way enterprises viewed information. In the
recent years, some enterprises has shifted their IT expenditure to data analytics. With heavy investments
by enterprises into such new technology for information, it changes the work nature for the role of Chief
Information Officer (CIO). It is projected that the role of CIO will change drastically in these few years
as they move from an operational position to one that requires heavy analysis and maximising firm’s
value. This shifts in thinking creates multiple job opportunities for talents with diverse, analytical
skillsets.
16
8. Considerations of Big Data Analytics
Data Privacy
With analytics being highly dependent on the information obtained, there is a growing concern of what
information is obtained, analysed and monetized. As business value derived from analytics is highly
lucrative, the corporate interest in obtaining more information for their analysis is significant. Google,
for instance, has been under public scrutiny over its controversial data collection methods from its
Google Drive and Gmail services (Barnato, 2014). Some information such as medical records and
financial records remained as highly sensitive data, and individual may not find it comfortable that firms
are using these information to advance their corporate interest.
Even though countries has passed data protection acts to ensure the integrity of information, the lines
between what can be used for analytics and what should not be used is often blurred. Singapore has
passed the Personal Data Protection Act (PDPA) to maintain the integrity of information by requiring
companies to give a detailed report of what and how they are going to use the information. However,
due to administrative complexities, such regulations are often hard to enforce and therefore data privacy
remains a key concern in the development of analytics.
Data Biasness
Though analytics is highly methodical and seems to be an indicative of the market trends, information
may often be manipulative and hence is not indicative of the actual reality. This is because the
systematic approach toward data collection in order to anticipate the randomness in data sampling and
minimize bias is not apparent in the collection of Big Data information (Boyd D. , 2011). These
information thus becomes incomplete and distorted, which may lead to skewed conclusions.
For instance, we can consider the use of twitter as a way to analyse market trends. There is an inherent
problem in using Twitter as a data source as 40% of the users are merely “listening” without
participating to the data (Rosoff, 2011). This may suggest that information that are recorded by Twitter
carries an inherent bias as it comprises of only data from a certain type of people (probably people who
are more vocal and participative in social media), and hence is not a good indicative of the general
population size. Even though greater social participation may minimize the margin of error in the
precision of data, the nature of information collection from analytics will always carry an inherent bias
due to inconsistencies in data collection (Boyd D. , 2011). Hence, the inherent biasness of analytics due
to its data collection method is a persistent setback of big data analytics.
9. Conclusion
The rise of Big Data Analytics is a disruptive technology which has the potential to revolutionize the
world by changing how people and enterprises view information. Furthermore, aside from deriving
value and reducing inefficiencies for businesses, Analytics also forms important keystones in the
development of new technologies such as semantic web and automation. In addition, analytics has the
potential to increase efficiency in industries, therefore lowering the energy consumption and hence
leading to sustainable development.
In conclusion, Big Data Analytics is a rising technological trend that will eventually impact all aspects
of society, and hence it is up to companies and individuals to harness its potential and utilize it for
growth.
17
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