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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, smart-meters, 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 This paper was reviewed by Lim Jun Hao and Thiam Pei Shan 2 Ronald Raegan is the 40th President of the United States of America (1981 – 1989)

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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)

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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.

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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 units4

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

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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 time-consuming 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. Healthcare and Biomedical Industries 2. Government 3. Commerce 4. 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 flash-flood 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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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.

Valuable Insights for Firms

Speech Analytics

Social Media

Analytics

Geospatial Analytics

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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.

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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

15

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.

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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.

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.

Still unfamiliar

with Big Data17%

Researching or Sourcing information

50%

Implementing or have implemented Big data Solutions

33%

PERCEPTION OF BIG DATA ANALYTICS

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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.

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References Barnato, K. (14 January, 2014). Google's changes to Gmail provoke more privacy fears.

Retrieved 2 March , 2014, from CNBC: http://www.cnbc.com/id/101326350

Basu, R., Archer, N., & Mukherjee, B. (25 January, 2012). Intelligent decision support in

healthcare. Retrieved 26 February , 2014, from Analytics Magazine:

http://www.analytics-magazine.org/januaryfebruary-2012/507- intelligent-decision-

support-in-healthcare

Bort, J. (19 February, 2014). It's better to be a software developer than a doctor. Retrieved

26 February , 2014, from Business Insider: http://www.businessinsider.sg/be-a-

software-programmer-not-a-doctor-2014-2/#.Uw2IM_mSzKw

Boyd, C. (2012). 1st Industry Trend Report. Retrieved 2 March , 2014, from Big Data

Insights Group:

http://www.thebigdatainsightgroup.com/site/sites/default/files/The%201st%20Big%2

0Data%20Insight%20Group%20Industry%20Trends%20Report_0.pdf

Boyd, D. (21 September, 2011). Six Provocations for Big Data. Retrieved 26 February ,

2014, from Social Science Research Network:

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1926431

Bridgwater, A. (23 August, 2013). Big data analytics from Henry Ford to 2014. Retrieved 20

February , 2014, from CWDN: The Computer Weekly Application Developer

Network: http://www.computerweekly.com/blogs/cwdn/2013/08/big-data-analytics-

from-henry-ford-to-2014.html

Cecere, L. (24 September, 2013). The Role of Analytics in the Race for the Supply Chain of

the Future. Retrieved 22 February , 2014, from DataInformed: http://data-

informed.com/role-analytics-race-supply-chain-future/

Columbus, L. (22 October, 2013). Finding & Closing More Quality Sales Leads Using

Predictive Analytics. Retrieved from Forbes :

http://www.forbes.com/sites/louiscolumbus/2013/10/22/finding-closing-more-quality-

sales-leads-using-predictive-analytics/

Datameer. (2014). What is Big Data? Retrieved 21 February, 2014, from Datameer:

https://www.datameer.com/product/big-data.html

Ernst & Young. (February, 2014). The role of data analytics in fraud prevention. Retrieved

18 March, 2014, from Ernst and Young publications:

http://www.ey.com/Publication/vwLUAssets/EY_-

_Forensic_Data_Analytics/$FILE/EY-Data-Analytics-The-role-of-data-analytics- in-

fraud-prevention.pdf

Esri. (n.d). Esri Maps for IBM Cognos. Retrieved 22 February , 2014, from Esri:

http://www.esri.com/software/location-analytics/esri-maps-for- ibm-cognos

Evans, D. (April, 2011). The Internet of Things: How the next evolution of Internet is

changing everything . Retrieved 15 Febuary , 2014, from Cisco:

http://www.cisco.com/web/about/ac79/docs/innov/IoT_IBSG_0411FINAL.pdf

19

Ferguson, D. (8 June, 2013). How supermarkets get your data – and what they do with it.

Retrieved 24 March, 2014, from

http://www.theguardian.com/money/2013/jun/08/supermarkets-get-your-data

Gartner. (31 July, 2013). Hype Cycle for Big Data, 2013. Retrieved 21 February , 2014, from

Gartner :

http://my.gartner.com.libproxy.smu.edu.sg/portal/server.pt?open=512&objID=260&

mode=2&PageID=3460702&resId=2574616&ref=g_portalfromdoc&content=html

Gobble, M. M. (February, 2013). Big Data: The Next Big Thing in Innovation. Research-

Technology Management, Vol. 56, No. 1. Retrieved 21 February, 2014, from

http://www.questia.com/library/journal/1G1-315069436/big-data-the-next-big-thing-

in-innovation

Google. (2014). Self-Driving Car Test: Steve Mahan. Retrieved 2 March , 2014, from

Google: http://www.google.com.sg/about/jobs/lifeatgoogle/self-driving-car-test-steve-

mahan.html

Henschen, D. (2 November, 2013). IBM's Watson Could Be Healthcare Game Changer.

Retrieved 22 February, 2014, from Information Week:

http://www.informationweek.com/software/information-management/ibms-watson-

could-be-healthcare-game-changer/d/d- id/1108608?

Horner, P., & Basu, A. (February, 2012). Analytics & the future of healthcare. Retrieved 19

February, 2014, from Analytics Magazine: http://www.analytics-

magazine.org/januaryfebruary-2012/503-analytics-a-the-future-of-healthcare

Horowitz, B. T. (June 21, 2013). Big Data Project at UPMC Reveals Patterns in Breast

Cancer Tumors. Retrieved 20 February, 2014, from eWeek:

http://www.eweek.com/database/big-data-project-at-upmc-reveals-patterns- in-breast-

cancer-tumors

IBM. (January, 2012). The Value of Analytics in Healthcare. Retrieved 21 February , 2014,

from IBM: http://www-935.ibm.com/services/us/gbs/thoughtleadership/ibv-

healthcare-analytics.html

IDA. (30 November, 2012). IDA Technological Roadmap: Internet of Things. Retrieved 15

February , 2014, from IDA:

http://www.ida.gov.sg/~/media/Files/Infocomm%20Landscape/Technology/Technolo

gyRoadmap/InternetOfThings.pdf

IDA. (30 November, 2012). IDA Technology Roadmap: Big Data. Retrieved 15 February ,

2014, from IDA:

http://www.ida.gov.sg/~/media/Files/Infocomm%20Landscape/Technology/Technolo

gyRoadmap/BigData.pdf

John Gantz, D. R. (June, 2011). Extracting Value from Chaos. Retrieved 15 February , 2014,

from IDC Iview: http://www.emc.com/collateral/analyst-reports/idc-extracting-value-

from-chaos-ar.pdf

Kucera, T. (January, 2012). Aberdeen Group Research. Retrieved 22 February , 2014, from

Predictive Analytics for Sales and Marketing:

20

http://spotfire.tibco.com/~/media/content-center/articles/aberdeen-sales-marketing-

analytics.pdf

Kuchinskas, S. (18 6, 2013). Telematics, V2V and autonomous vehicles. Retrieved from

Telematics Update: http://analysis.telematicsupdate.com/v2x-safety/telematics-v2v-

and-autonomous-vehicles

Marr, B. (4 February, 2014). Amazon: Using Big Data Analytics to Read Your Mind.

Retrieved 22 February , 2014, from SmartData Collective:

http://smartdatacollective.com/bernardmarr/182796/amazon-using-big-data-analytics-

read-your-mind

Mckinsey. (May, 2011). Big data: The next frontier for innovation, competition, and

productivity. Retrieved 2014 February, 2014, from McKinsey and Company: Insights

and Publications:

http://www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_f

or_innovation

Miller, S. (18 September, 2013). Making Data relevant to Business. Retrieved 15 February ,

2014, from Business times:

http://www.smu.edu.sg/sites/default/files/smu/news_room/smu_in_the_news/2013/so

urces/sept18/bt_20130918_1.pdf

Naone, E. (22 August, 2011). The new Big Data. Retrieved 15 February , 2014, from MIT

Technology Review: http://www.technologyreview.com/news/425090/the-new-big-

data/

Press, G. (February, 2014). Got Big Data? Want an ROI? Retrieved 22 February , 2014, from

gPress: http://yottamine.com/wp/wp-content/uploads/2014/02/Got-Big-Data-Want-an-

ROI.pdf

Raffensperger, L. (30 July, 2013). Networked Cars Are Coming, But Their Hacks Are

Already Here. Retrieved 24 March, 2014, from Discover Magazine:

http://blogs.discovermagazine.com/d-brief/tag/cars/#.UzESxvmSzKw

Rosenbush, S. (11 March, 2013). Visa Says Big Data Identifies Billions of Dollars in Fraud.

Retrieved 22 February , 2014, from Wall Street Journal:

http://blogs.wsj.com/cio/2013/03/11/visa-says-big-data- identifies-billions-of-dollars-

in-fraud/S

Rosoff, M. (8 September, 2011). Twitter Has 100 Million Active Users -- And 40% Are Just

Watching. Retrieved 2 March, 2014, from Business insider:

http://www.businessinsider.com/twitter-ceo-dick-costolo-2011-9?IR=T&

Russell, R. (28 January, 2014). Is IBM Getting Desperate? Retrieved 22 February , 2014,

from Forbes: http://www.forbes.com/sites/robrussell/2014/01/28/is-ibm-getting-

desperate/

Shih, G. (February 19, 2014). Facebook to buy WhatsApp for $19 billion. Retrieved 22

February , 2014, from Reuters: http://www.reuters.com/article/2014/02/19/us-

whatsapp-facebook-idUSBREA1I26B20140219

21

SMU. (n.d.). Approved LARC Projects. Retrieved 26 February , 2014, from LARC:

http://larc.smu.edu.sg/approved- larc-projects/

Sunshine, W. L. (2014). 5 Smart Grid Pilot Programs. Retrieved 24 March, 2014, from

about.com: http://energy.about.com/od/Grid/tp/5-Smart-Grid-Pilot-Programs.htm

Tam. (January 30, 2013). Facebook by the numbers: 1.06 billion monthly active users.

Retrieved 22 February , 2014, from Cnet: http://news.cnet.com/8301-1023_3-

57566550-93/facebook-by-the-numbers-1.06-billion-monthly-active-users/

Telegraph. (February 19, 2014). Facebook buys WhatsApp: Mark Zuckerberg explains why.

Retrieved February 22, 2014, from Telegraph Uk:

http://www.telegraph.co.uk/finance/newsbysector/mediatechnologyandtelecoms/digit

al-media/10650340/Facebook-buys-WhatsApp-Mark-Zuckerberg-explains-why.html

United Nations. (23 June, 1997). 'IF INFORMATION AND KNOWLEDGE ARE CENTRAL

TO DEMOCRACY, THEY ARE CONDITIONS FOR DEVELOPMENT', SAYS

SECRETARY-GENERAL. Retrieved 29 March , 2014, from United Nations Press

Release SG/SM/6268:

http://www.un.org/News/Press/docs/1997/19970623.sgsm6268.html

Wall Street Journal. (13 June, 1997). Frederick Taylor, Early Century Management

Consultant. Retrieved from

http://www.cftech.com/BrainBank/TRIVIABITS/FredWTaylor.html

Wise, L. (14 February, 2011). Expanding BI by Thinking Outside of the Box. Retrieved 15

February, 2014, from Dashboard Insight:

http://www.dashboardinsight.com/articles/business-performance-management/data-

analysis-and-unstructured-data.aspx

Young, M. (17 April , 2013). Automotive innovation: big data driving the changes. Retrieved

from Big Data Insights Group :

http://www.thebigdatainsightgroup.com/site/article/automotive-innovation-big-data-

driving-changes