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Digital Systems and Technologies

Introduction to Big Data and MAS Holdings

Big Data is said to be the process of introducing different ways of analyzing, taking out information, and dealing with various types of data sets when the process becomes confusing or complex, through the usage of traditional methods of data processing. The first element of Big data is its size (Gandomi and Haider, 2015). Today, big data has become the center of attention for various academic authors. The other reason for their attention towards big data may also be fear. Technological advancements were first seen in the technical and academic backgrounds, where the facts and amalgamation was seen in various aspects of information enlistment, such as books. The address on big data is mainly dominated as well as influenced by the efforts regarding the marketing of the hardware and software developers, are mostly fixated on the predictive analytics as well as structured data. There are various definitions of big data, which had changed in recent times, and it has led to various confusions. The definitions provided differ due to various features such as time and the type of data. It can also be said that big data which is available nowadays, may not be of significance in the forthcoming days, as the storage capabilities will improve, and lead to an upsurge, which will lead to bigger data sets to be taken as inputs. The purpose of this report is to provide a detailed analysis on big data, its academic models, the theories pertaining to big data, as well as the ways in which the academics and practitioners have defined it. For this purpose, this paper will provide a detailed analysis on big data, its theories, academic models, the ways in which academics and practitioners have defined it, along with the challenges, purpose, benefits, the risks and costs associated with Big data. This paper will mainly focus on the big data of MAS Holdings, by providing information on the wider implications as well as the society on the business, along with the time which will be required to implement the various change management assets in MAS Holdings, followed by the concluding paragraph. The discourse on big data is focused on predictive analytics, therefore, the ways in which the analytics have captured the processes of business as well as government leaders, along with the state-of-practice of an embryonic industry will be provided in this paper.

Big Data

There are various academics, as well as practitioners who have defined Big Data in their own words. According to various researchers, Big Data can be defined as a term, which has an important role in the everyday life of individuals. It can be used in various fields such as commercial applications to conduction researches regarding multiple tasks. Big data can solve some of the most challenging issues in a short span of time, which is also possible in academics. In academics, it is visible in most of the disciplines, which are not limited to social sciences, psychology, humanities, geography, as well as healthcare (Salganik, 2019; Harlow and Oswald, 2016; Kitchin, 2013; Ewing, Kimmerly and Ewing-Nelson, 2016; Andreu-Perez, et al., 2015). Mikal, Hurst and Conway (2016) have stated that there is a possibility to increase the practice of big data assets in the everyday life of an individual, which can disclose the patterns both the individual as well as group performance in an organized way that assures the valuable submission of big data analytics. For example, in the healthcare sector, there are different developments, which have taken place namely the smarter hospitals, which make use of predictive analysis of the Electronic Health Records (EHR), which is helpful in identifying, the patients who are at an advanced risk of having a cardiac arrest or have a higher chance of health deterioration (Mertz, 2014).

Further, there are various attributes of Big Data, which include the three V’s that are; Velocity (high-speed processing), Volume (greater amounts) and Variety (assorted data) (Laney, 2001). However, as the years passed by, big data was readily available in larger quantities, where more definitions of big data were created and extended, regarding the three traditional attributes of the three V’s, which included Veracity, Value, Variability, including other qualities such as Exhaustivity, Extensionality, as well as Complexity (IBM, 2019; Ishwarappa and Anuradha, 2015; Fan and Bifet, 2013; Mayer-Schönberger and Cukier, 2013; Marz and Warren, 2015; Perry, 2017). However, big data is still encircled by the conceptual vagueness because of the presence of the mixed ways in which big data is used (De Mauro, Greco and Grimaldi, 2015).

Floridi (2012) has criticized the traditional ways of the definition of big data because the author considers it as meaningless and elusive and they do not properly define the term ‘Big Data’ and what it actually means. There are various scholars, in the social sciences sector, who have stated that the “V” attributes of the big data should be discarded because these characteristics mainly come from data sciences and analytics, which are seen as mechanical. Amid them, Lupton (2015) has stated that the V attributes must be replaced by the 13 ‘P features’ namely Perverse, Personal, Political, Predictive, Portentous, and many more. Further, Kitchin and McArdie (2016) have provided an argument stating that both the v-values and the p-values often are descriptive and do not provide any logical explanation for the term big data, however, they illustrate the ontological characters of the data, which have been provided. It has been said that the description of Big data is a significant aspect for the Institutional Review Boards (IRBs), as well as the supervisory figures globally, who find it difficult to regulate the Big Data research and the projects related with it, including Big Data analytics and methods (Vayena, et al., 2015; Kaplan, 2016). There are a few scholars who have promoted the fine line between the internal tension, while ensuring the secrecy of the investigation contributors, as well as the quality of the data sets, specifically related to the increased applied politics of the open data sources in an academic field (Daries, et al. 2014).

The increase in the usage of media, social media, mobile devices, and the instrument systems, with the lower costs of storing data and computing services, has led to the creation of a never-ending requirement of computer generation actions as well as communication, which can also be called as ‘big data.’(Muller, Junglas and Brocke, 2016). Big data is said to be not only diverse, but is also greatly unsettling, especially the academic research purposes and practices, which are related to the data. Marchand and Pepper (2013) have stated that scholars as well as practitioners provide great focus on the big data’s technical surfaces, and do not provide much information on the people and their social, recognized and environmental facets. This creates a barrier in the information system of the big data. This also leads to the creation of the lack of a social harmony which is often required by the information system implementation initiatives, to be successful. The authors further state that the big data analytics as well as information system with their analytical workload, should consist of individuals who have proper knowledge regarding the cognitive and behavioral sciences, who must have the ability to be able to understand how other individuals recognize the problems, analyze the available data in generating ideas, information as well as solutions. The individuals must also be well aware of the furthermost imperative strengths of the information system discipline.

The Big Data Analytics which can be said to be a new method of using big data sources as well as advanced technologies, is seen in different disciplines of social sciences such as Sociology and Economics. These disciplines have been successfully been able to include the big data analytics into their system of scientific inquiry. The four V’s of big data change the perception of an individual can store and manage the data, which is available. It can be seen in technical terms that the data’s velocity, volume as well as variety in different organizations, have seen changes in their information technology departments to equally distribute their available storage facilities, and consider these aspects to undertake huge quantities of shapeless data (Dean and Ghemawat, 2008). In terms of technology, there are both NoSQL, as well as SQL systems, which include Hadoop and Spark. These systems have a better chance at handling larger volumes as well as options of undistributed data, where the businesses mainly use in-memory databases to make complete use of the rapidity of the real-time submissions (Heudecker, 2013). Using different sources of data, come with a variety of data quality as well as their credibility concerns that are required for proper data management, data presentation and for the management of knowledge. Further, the volume and velocity of the data have led to the IT departments to change their corporal on-premises data centers into the cloud-based infrastructure-as-a-service (IaaS), platform-as-a-service (PaaS), as well as database-as-a-service (DBaaS). These methods are properly suited for meeting an organization’s storage requirements as well as its elastic computing system (Buytendijk, 2014). There has been a constant trend regarding the Iaas, Paas, DBaas, where various works of technology are dependent on the value chain’s information stage, where the cloud and the accessed software-as-a-service (SaaS) have been running (Buytendijk, 2014). The big data analytics also provide assistance to the scientists to be able to use the enterprise machine learning that includes adjustable scalable online programs, along with the Hadoop platforms, which are able to undertake both the speed and amount of information available at a great speed (McAfee and Brynjolfsson, 2012).

The big data information value chain has huge insinuations for the Information Systems investigation. The various issues related to the information system are connected to the knowledge, which is available from big data. There are various effects of these issues, which refer to sense making, which can be defined as the process of gaining knowledge from the information which is available from the big data (Lycett, 2013). For example, when organizations gather and store huge amounts of customer data, than the previous times, their privacy, ethical considerations as well as the security of their data are brought to the front position. However, with the creation of NoSQL and Hadoop systems, there is creation of data lakes and a respect of the in-memory process and databases. For this process to be complete, big data modeling formalisms as well as integration artifacts are significant. Social listening platforms, along with the Internet of things (IoT), leads to the creation of the novel forms of analyzing the data, based on the user-generated content, along with the sensor-based data, which are full of information, feelings, positions, thoughts, topographical placing, and many more (Abbasi, Sarker and Chaing, 2016).

When related to the predictive studies, there have been various researchers who have stated that data mining and machine learning algorithms are proper ways to save the data. As these methods are mainly related to the data driven nature, they play important roles when individually going through the data, which is available that are found in huge volumes, in a greater variety and speed. However, this method does not work properly with the traditional scientific methods (Breiman, 2001; Shmueli, 2010). There are various algorithms of the Big Data Analytics (BDA), which have been created for the practical applications, which include credit risk scoring as well as providing recommendations for different products to individual customers. Various information researchers have stated that the research can begin with data or the data-driven findings, and not only the data, which is provided (Muller, Junglas and Brocke, 2016).

MAS Holdings

MAS Holdings is an apparel manufacturer based out of Sri Lanka, with 53 manufacturing facilities spread globally in 16 countries. It further has its design locations in various centers with around 99,000 employees in the organization. It has a business worth USD 2 billion and is also recognized as the world’s best delivery solution provider in the apparel and textile manufacturing industry (MAS, 2020). The business has a varied range of portfolio, which includes having business in the IT sector, along with its personal brands and industrial parks. There are various aspects which the organization has to keep in check, to be able to keep its business running smoothly and maintain its big data.

Challenges faced: There has been an increase in the competition levels around the world apparel market, which generates pressure and creates challenges for the Sri Lankan apparel industry. There has been a noteworthy decline in the amount of apparel imports from the foremost traders globally. The US and the EU markets are the main importers of apparel from Sri Lanka, where there has been a significant amount of reduction in the apparel imports, which is visible in the table below (WTO, 2014):

Further, the US apparel markets and the trends generated the ‘Made in US’ tag, which saw an increase of 6.2% from 2012 to 2013 (Dilanthi, 2015). The US reduced its imports from Sri Lanks, to avoid paying the extra tarrifs put on the imports. Additionally, the EU increased its taxes on the imports, which made the buyers in the country reduce their buying capacities of imported goods (Dilanthi, 2015). This led to a decrease in the imports of Sri Lankan apparel products. Earlier, the labour markets of Sri Lanks was a famous place for the foreign investors due to the availability of cheap and skilled labour. However, Kelegama (2005), Palansooriya (2009) and Omar and Cooray (2005), as quoted by Lanarolle et al (2014) have stated that the productivity of the poor labour, along with the higher amount of lead time act as obstacles for the apparel industry.

In addition, there is no proper regional discrepancy of the clothing market. The Sri Lankan market has a deprived divergence in relation to the apparel marketplace. The USA and the EU markets have accounted for 42.9% and 45.9% of the entire clothing exports in the year 2013 (Central Bank Reports, 2014). However, the manufacturers in Sri Lanka are the best manufacturers in the country itself, not in the world. There is lack of backward integration in the value chain of the Sri Lankan apparel industry. It mainly makes use of the forward integration methods. For Sri Lanka, China is both a supplier and a competitor, which leads to the creation of a challenging situation on a global scale (Dilanthi, 2015).

Further, the living wage issue is a never-ending problem for the apparel industry, especially MAS Holdings (Dilanthi, 2015). Even though MAS holdings and the other manufacturers in the Sri Lankan apparel industry are successful in meeting the conventional norms and regulations, there are various concerns regarding the wages of the workers, which are raised by the media of the country. They state that the wages which the workers receive are very low, and these matter because the code of conduct are different than the national laws that have been set by the Government (DiscoverSociety, 2016). The manufacturing sector of NAS Holdings may face issues related to the growing data, as the manufacturing facilities have a huge number of devices. These devices constantly generate data, which require various processes to be kept in mind such as the protocols to be used, storage requirements, the display form size as well as the amount of data, which is being received. Dealing with the amount of big data is a compound process, and since most of the manufacturers in the industry are unwilling to consider this aspect, it generates another problem, which is called analytics (Leroux, 2020).

For the success of a business, both its tangible as well as intangible assets are significant. The intangible products consist of insurance, repairing, travel, investment, banking, healthcare and various other elements, whereas, the tangible products can be seen as well as touched, such as the products produced by the company. Frequently, the products can be seen and touched before buying (Levitt, 2020). These intangible products can either lead to the success of downfall of the product and the enterprise (Levitt, 2020). MAS Holdings, to promote its tangible products in the market used the Human Factors/ Ergonomics (HFE) methods to improve the negative aspects of the workplaces. There are various methods, which are introduced by the organisation, such as the strategies by the Toyota Production System, that is, the 5S, Kaizen Six Sigma, and many more. It has also introduced the MAS Operating System, which ensure the proper development and training of its employees. This ensures that the employees of the organisation are satisfied with their jobs, which brings success to the company, both in its tangible as well as intangible assets (Abeysekera and Illankoon, 2016).

When an organisation has value, the investors are more interested in investing as it increases their wealth. For this reason, the organisation should search, understand as well as improve the factors which will increase the value of the enterprise. The intangible assets, such as patents, trademarks, copyrights, play a significant role in driving the wealth of the stakeholders. It also provides the organisation with competitive advantage over other manufacturers. Investing in the research and development (R&D), marketing as well as advertising products, can act as a source of attracting the customer, which will help boost the sales of the organisation. The organisation will be able to generate unlimited resources with the help of the human’s ability to produce goods, services as well as improve the production processes (Alsinglawi and Aladwan, 2018).

Implementation and Change Management Processes: For a company to be successful in its business operations, it must involve various processes and involve the change management processes. These processes involve making use of an energy monitoring sheet, on a weekly basis to be able to note the amount of consumption of energy, which will act as a relevant energy performance indicator for the aspects that require energy consumption (Clean Energy Ministerial, 2018). This monitoring sheet will be able to assess the actual amount of energy of data consumed against the target, which was set. The effectiveness of the action plans introduced by the organisation can be kept in check through the action plans that have been introduced, as the energy monitoring sheet. There are different processes of the organisation, which have various energy requirements, and they must be directed under different conditions. These conditions must be mentioned in the Standard Operations Procedures (SPOs), Operational Control Procedures (OCPs), along with the work instructions, so that the business can work smoothly and the effectivity of the energy performance is maintained (Clean Energy Ministerial, 2018). Further, to maintain the improvements which have been taking place in the organisation, it must introduce the Change Management Policy to make sure that there is no carbon emission. There must be development processes including the usage of personal experience, communication and training. There must be employee engagement, where they will be divided into small groups to create energy improvement projects. Further, full factory awareness programs should be introduced, which must be conducted by experts in the consulting energy management.

Further, the company allows all its stakeholders to freely lodge their complaints regarding their grievances and concerns. In the financial year 2018-2019, the company received 50 complaints, out of which 48 complaints were resolved. This attracts the stakeholders as they have the assurance of their grievances being solved. The customers as well as the employees will be positively affected by it (MAS. 2019).

Time: The time, which will be required by the organisation to implement the assets depend on the life of the asset. The expected usage of the asset completely depends on the type of asset. For example, the copyright asset of the company, has a legal life of approximately 50 years; however, it can only be used for a maximum of 10 years, where the repayment of the copyright will be 10 years. Further, the other intangible assets, which are connected with other physical assets that are technologically obsolete, should be considered as repaid, as they will only increase the expenses of the company and the time allotted to other intangible assets (CFI, 2020). The timetable for MAS Holdings has been provided below:

  1. First, the company has to assign a valuator, where he/she has to assign a proper weight on each innovation which will take approximately 7 days.
  2. Then, the valuator has to compare the company with the benchmark, which has been set in the global market, which will take another 7 days.
  3. Further, to calculate the value of all intangible assets, the valuator has to consider different indicators such as the account value, risk factors, as well as the total value of the intangible available, which will take another 5 days (Author’s creation).

Cost: The total amount of costs involved in the running of the business and handling of the intangible assets have been provided below:

Table 1: Intangible assets of MAS Holdings

 

Risk Factor

Weight

Ranking

Weight X Ranking

Value of the Intangibles

Discount Rate

Total Intangible value

Initial accounting value

               

4,000,000

HC

               

Labour Skills/Educational Level

0,6

8%

93%

0,09

1,700,000

     

Distribution Channel

0,5

4%

89%

0,07

3,500.000

     

Training structure

0,6

5%

112%

0,05

70,000

     

Teamwork

0,7

3%

99%

0,06

89,000

     

Consumer relation

0,8

6%

130%

0.09

1,000,000

     

Employee relation

0,9

5%

80%

0,02

1,000,000

     

Innovative thinking/R&D

0,7

7%

98%

0,05

75,000

     

Marketing

0,5

6%

96%

0,06

89,000

     

IC

               

Knowledge transferring

0,9

7%

150%

0,08

97,000

     

Database systems

0,7

8%

120%

0,07

3,400,000

     

Information networks

0,8

8%

110%

0,06

2,300,000

     

Product/Technology process

0,9

9%

99%

0,04

24,000

     

OC

               

Corporate structure

0,6

6%

130%

0,06

1,000,000

     

Corporate vision and mission

0,7

7%

110%

0,08

75,000

     

Leadership

0,7

5%

130%

0,09

3,500,000

     

Opportunity to grow

0,8

8%

120%

0,05

900,000

     

Strategy

0,7

6%

97%

0,07

500,000

     

Goodwill

0,6

9%

99%

0,04

600,000

     

External Influences

               

Competitive/ regulative environment

0,7

5%

100%

0,06

900,000

     

Reputation

0,8

7%

120%

0,08

860,000

     

Total Risk

14.2

             

Average Risk

7.1

             

Total weight

 

129%

           

Weighted RIV multiplier

     

1.27

       

Total worth intangibles

       

21,679,000

     

Total intangible value

           

20,679,000

 

Total Corporate Value

             

24,679,000

(Source: Author’s Creation)

Risks Involved: There are various risks involved for an organisation, which it has to mitigate to be able to run smoothly. There are risks which may have a severe impact on the business, whereas, there are risks which may not hamper the business. For MAS holdings, operational as well as the implementation risks must be kept in check, as there have been various studies, which depict that the incidence of operational risks is higher, which is 5 to 20 times a year. The workers refusal to work, breakdown of the processes, mechanical failure, as well as low production levels affect the business negatively (Barua, Kar and Mahbub, 2018). Further, when the workers as well as the chief, are not able to deliver the final products in the given timeframe, the production cost increases as well as leads to a fall in the reputation of the organisation. To overcome these risks, a weekly target must be set for the employees, the measurements as well as the designs of the clients should be set. Proper workforce should be employed, and the quality control checking should be done for every product (Barua, Kar and Mahbub, 2018).

Ethical Implications: There are various ethical implications regarding the big data of MAS Holdings. The profiling of the individuals can result in discrimination as a type of unethical social impact. This can be a result of dividing the individuals into different groups, which can either be intentional or unintentional, based on their race, sex, religion, gender, social and economic status, and various other factors (Newell and Marbelli, 2015). Ethical problems may arise in the value chain regarding the big data, which can lead to the final owner of the data, using it for a reason which was different than its initial intention. In addition, the monitoring of the individual’s behaviour can lead to the creation of personalized good and services, which mean that the individual is no longer open to all the products and services (Zuboff, 2015).

Implications: The modern society has created various technologies as well as techniques measure, identify and influence individuals. The data evaluation strategies are different in terms of various challenges and implications. Further, with the introduction of mobile devices, the creation as well as usage of the content and data has become simpler. For this reason, social science is an important aspect and plays a significant role in the Big Data discourse. Through mobile phones and wearables, the personal behaviour as well as their functions, have become available for informatization. In addition, there are different practices of the behaviour tracking of the individuals and their profiling provides a challenge to the traditional methods of maintaining privacy. Thus, this poses as a challenge to the present state and central governments against the multinational companies in the digital aspects (Kappler, et al., 2018).

Conclusion on Big Data and MAS Holdings

Thus, it can be said that Big Data plays an important role nowadays in the lives of individuals as well as various organisations. This paper has provided a detailed analysis on big data, which mainly focusses on MAS Holdings, an apparel manufacturer in Sri Lanka. For this reason, this paper has provided information on the various aspects of big data, which MAS holdings should consider such as, the issues which the company faces, the risks associated, the costs involved for intangible assets the ethical implication, along with the wider implications with society, and the time involved to make use of the intangible assets. Therefore, Big data, in today’s world, is significant in collecting a huge amount of data and keeping it secure for future use, and for analysing the behaviour of different individuals.

Reference List for Big Data and MAS Holdings

Abbasi, A., Sarker, S. and Chaing, R. H. L. 2016. Big data research on informative systems; Towards an inclusive research agenda. Journal of the Association for Information System, 17(2), pp. 1-32.

Abeysekera, J. and Illankoon, P., 2016. The demands and benefits of ergonomics I Sri Lankan apparel industry: A case study at MAS Holdings. Work, 55(2), pp. 255-261.

Alsinglawi, O. and Aladwan, M., 2018. The effect of invisible intangibles on volatility of stock prices. Modern Applied Science, 12(11), pp. 290-200.

Andreu-Perez, J., Poon, C. C., Merrifield, R. D., Wong, S. T. and Yang, G-Z. 2015. Big data for health. IEEE journal of biomedical and health informatics, 19(4), pp.1193–208.

Annual Report., 2014. Central Bank of Sri Lanka, Colombo, Sri Lanka. [Online] Available at: < http://www.cbsl.gov.lk/pics_n_docs/10_pub/_docs/statistics/other/econ_&_ss_2014_e.p df> [Accessed on: 4 September 2020]

Barua, S., Kar, D. and Mahbub, F., 2018. Risks and their management in readymade garment industry: evidence from the world’s second largest exporting nation. Journal of Business and Management, 24(2), pp. 73-103.

Breiman, L. 2001. Statistical modeling: the two cultures. Statistical Science

Buytendijk, F. 2014. Hype cycle for big data, 2014. [Online] Gartner. Available at: <https://www.gartner.com/doc/2814517/hype-cycle-big-data> [Accessed on: 4 September 2020]

Clean Energy Ministerial, 2018. Global Energy management system implementation: Case study. [pdf] Available at: <https://www.cleanenergyministerial.org/sites/default/files/2018-05/CEM_EM_CaseStudy_MAS_KREEDA_Sri_Lanka.pdf> [Accessed on 4 September 2020]

CFI., 2020. Amortization of Intangible assets. [Online] Available at: <https://corporatefinanceinstitute.com/resources/knowledge/accounting/amortization-of-intangible-assets/> [Accessed on: 4 September 2020]

Daries, J. P., Reich, J., Waldo, J., Young, E. M., Whittinghill, J., Ho, A. D, et al. 2014. Privacy, anonymity, and big data in the social sciences. Communications of the ACM, 57(9), pp.56–63.

De Mauro, A., Greco, M. and Grimaldi, M. 2015. (ed). What is big data? A consensual definition and a review of key research topics. AIP conference proceedings. AIP.

Dean, J. and Ghemawat, S., 2008. MapReduce: Simplified data processing on large clusters. Communications of the ACM, 51(1), pp.107-113.

Dilanthi, S. 2015. A review on the current profile and challenges of the Sri Lankan Apparel Industry. International Journal of Management, Accounting and Management, 2(10), pp. 1261-1277.

DiscoverSociety., 2016. Living wages: recurring problems of ethical fashion in Sri Lanka? [Online] Available at: <https://discoversociety.org/2016/07/05/living-wages-recurring-problems-of-ethical-fashion-in-sri-lanka/> [Accessed on: 4 September 2020]

Ewing, E. T., Kimmerly, V. and Ewing-Nelson, S. 2016. Look Out for ‘La Grippe’: Using Digital Humanities Tools to Interpret Information Dissemination during the Russian Flu, 1889–90. Medical history, 60(1), pp.129–31.

Fan, W. and Bifet, A. 2013. Mining big data: current status, and forecast to the future. ACM sIGKDD Explorations Newsletter. 14(2), pp. 1–5.

Floridi, L. 2012. Big Data and Their Epistemological Challenge. Philosophy & Technology, 25(4), pp.435–7.

Gandomi, A. and Haider, M. 2015. Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), pp. 137-144.

Harlow, L. L. and Oswald, F. L. 2016. Big data in psychology: Introduction to the special issue. Psychol Methods, 21(4)., pp.447–57.

Heudecker, N. 2013. Hype cycle for big data, 2013. [Online] Gartner. Available at: <https://www.gartner.com/doc/2574616/hype-cycle-big-data> [Accessed on: 4 September 2020]

IBM. 2019. What is big data?—Bringing big data to the enterprise [Online]. Available at: <http://www-01.ibm.com/software/data/bigdata/> [Accessed on: 4 September 2020].

Ishwarappa. and Anuradha, J. 2015. A Brief Introduction on Big Data 5Vs Characteristics and Hadoop Technology. Procedia Computer Science, 48, pp. 319–24.

Kaplan, B. 2016. How Should Health Data Be Used?: Privacy, Secondary Use, and Big Data Sales. Cambridge Quarterly of Healthcare Ethics, 25(2), pp.312–29.

Kappler, K. E., Schrape, J-F., Ulbricht, L. and Weyer, J., 2018. Societal Implication of big data. Kunstliche Intelligenz.

Kelegama, S. 2014. What will it take Sri Lanka to Build a World Class Export Economy? CMA Annual Conference, Institute of Policy Studies of Sri Lanka, 19th March 2014.

Kitchin R. 2013. Big data and human geography: Opportunities, challenges and risks. Dialogues in human geography, 3(3), pp.262–7.

Lanarolle, W. D. G., Perera, T. and Yapa, T.S.D., 2014. Performance Improvement Best Practices Applicable to the Sri Lankan Apparel Industry, Third International Conference on Management and Economics, Faculty of Management and Finance, University of Ruhuna, Sri Lanka, 26-27 February 2014.

Laney, D. 2001. 3D data management: Controlling data volume, velocity and variety. META group research note. 6(70), p.1.

Leroux, M. 2020. 4 big challenges faced by manufacturers. [Online] Available at: <https://new.abb.com/cpm/production-optimization/big-data-analytics-decision-making/4-big-data-challenges-faced-by-manufacturers> [Accessed on: 4 September 2020]

Levitt, T. 2020. Marketing Intangible Products and product intangibles. [Online] Harvard Business Review. Available at: <https://hbr.org/1981/05/marketing-intangible-products-and-product-intangibles> [Accessed on: 4 September 2020]

Lupton, D. 2015. The thirteen Ps of big data 2015. [Online] Available at: <https://simplysociology.wordpress.com/2015/05/11/the-thirteen-ps-of-big-data/> [Accessed on: 4 September 2020]

Lycett, M. 2013. Datafication: Making sense of (big) data in a complex world. European Journal of Information Systems, 22(4), pp.381-386.

Marchand, D. A. and Pepper, J., 2013. Why IT fumbles analytics. [Online] Harvard Business Review. Available at: <https://hbr.org/2013/01/why-it-fumbles-analytics> [Accessed 4 September 2020]

Marz, N. and Warren, J. 2015. Big Data: Principles and best practices of scalable real-time data systems. New York: Manning Publications Co.

MAS. 2019. 24th Annual Report. [pdf] Available at: <https://mas.co.in/pdf/annua-report-201923082019013929.pdf> [Accessed on: 4 September 2020]

MAS. 2020. Overview. [Online] Available at: <https://www.masholdings.com/overview.html#overview> [Accessed on: 4 September 2020]

Mayer-Schönberger, V. and Cukier, K. 2013. Big data: A revolution that will transform how we live, work, and think: Houghton Mifflin Harcourt.

McAfee, A. and Brynjolfsson, E., 2012. Big data: The management revolution. [Online] Harvard Business Review. Available at: <https://hbr.org/2012/10/big-data-the-management-revolution/ar> [Accessed on: 4 September 2020]

Mertz, L. 2014. Saving Lives and Money with Smarter Hospitals: Streaming analytics, other new tech help to balance costs and benefits. IEEE pulse. 5(6), pp. 33–6.

Mikal, J. and Hurst, S. 2016. Conway M. Ethical issues in using Twitter for population-level depression monitoring: a qualitative study. BMC medical ethics. 17(1), p.22.

Muller, O., Junglas, I. A. and Brocke, J. V. 2016. Utilizing big data analytics for formation systems research: Challenges, Promises and guidelines. European Journal of Information Systems, pp. 1-14.

Newell S. and Marbelli, M. 2015. Strategic Opportunities (and challenges) f algorithmic decision-making: A call for action on the long-term societal effects of ‘datification’. The Journal of Information Systems, pp.1-12.

Perry, J. S. 2017. What is big data? More than volume, velocity and variety. [Online] Available at: <https://developer.ibm.com/dwblog/2017/what-is-big-data-insight/> [Accessed on: 4 September 2020]

Salganik, M. 2019. Bit by bit: Social research in the digital age. Princeton University Press.

Shmueli, G. 2010. To explain or to predict? Statistical Science, 25(3), pp.289–310.

Tim, K. 2016. Intellectual property and intangible assets: alternative valuation and financing approaches for the knowledge economy in Luxembourg, European Institute for Knowledge & Value Management, 3.

Vayena, E., Salathé, M., Madoff, L. C. and Brownstein, J. S. 2015. Ethical challenges of big data in public health. Public Library of Science.

Zuboff, S. 2015. Big other: surveillance capitalism and the prospects of an information civilization. Journal of information technology, 30.

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