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WIA1001 INFORMATION SYSTEMS
(SEM 1 2018/2019)
PAIR ASSIGNMENT
BIG DATA AND DATA ANALYTICS
BRANDON TAN ZHIRONG (WID180007)
GOY SHUH XIAN (WID180018)
TUTORIAL: GROUP 2
LECTURER: DR. SURAYA HAMID
TABLE OF CONTENT
NO. CONTENT PAGE
1. INTRODUCTION
-DEFINING THE 3V’S
-BIG DATA ANALYTICS 3
4
2. ADOPTION
-THE THREE STAGES OF ANALYTIC ADOPTION 5-6
3. BENEFITS 6-7
4. CHALLENGES 8
5. CONCLUSION 9-10
6. REFERENCES 10
Introduction
Big data, this term could be heard almost anywhere in this era governed by the Internet of Things(IoT). So what exactly is this big data? The first thing that would cross our minds when it comes to big data is definitely the sheer size of the data. However, big data is not just about the size. In the past few years, big data has been described as closely related to the Three Vs, which stands for Volume, Variety and Velocity, as defined by Amir Gandomi and Murtaza Haider in the International Journal of Information Management.

1914525389255Defining the 3V’s
Figure 1:Relationship of Big Data with the 3V’s
Volume stands for the magnitude of the data. Currently, big data will go from a few terabytes to even petabytes. However, the amount of data generated has been doubling every three to four years. Therefore, the volume needed to be categorized as big data will definitely increase over the years as data increases and the storage capacities become larger and better. In other words, one petabyte of data might be considered as big data today, but it might not be considered as big data after 3 years.

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Variety stands for the different forms that data take. Data is divided into three main categories, which are structured, unstructured and semi-structured. Most of the data nowadays come in the form of unstructured data. Examples of unstructured data are text, images and audios. On the other hand, structured data only make up of about 5% of the big data, and they are mostly tabular data in spreadsheets and databases.

Velocity on the other hand, stands for the speed in which data are created and the speed that they are analyzed. With the abundance of technologies nowadays, data are created in large quantities in a very short amount of time, therefore, the analyzing of data must be able to match the speed in which data are created to be able to generate information with up-to-date value.

Big Data Analytic
Based on our findings, as far as big data goes, it does not yield any information if it is not analysed upon. What makes big data so valuable is the insights that we could gain from analysing it, insights which could help us in decision makings. In other words, big data without data analytic is worthless. Based on the article “Beyond the hype: Big data concepts, methods, and analytics” by Amir Gandomi and Murtaza Haider, in order to turn big data into such insights, there is a necessary five-stage process.

Figure 2: The five-stages of big data analytic
As referred to Figure 2, the analysing of big data is divided into two main process and further divided into five stages. The two main processes are data management and analytic. Data management refers to the process where big data is acquired and stored to prepare it for further analysis, while analytic is the process where big data is analysed into useful insights.
Through these processes, big data will be converted into useful information which can drive decision making for various situations, from methods to cater to consumer needs, or even decisions that will alter the company’s future.
Now that we understand what big data and data analytic is all about, let’s have a look on how are they implemented in industries.

Adoption
Big data can be utilized in various industries, whether its an international electronics company, or even a local retail shop. Big data can help any and every one of them in decision makings. However, depending on the culture of the company, the adoption of big data may vary. The adoption of big data can be divided into three stages, which are aspirational, experienced and transformed as defined by Steve LaValle, Eric Lesser, Rebecca Shockley, Michael S.Hopkins and Nina Kruschwitz n MIT Sloan Management Review.

The Three Stages of Analytics Adoption

Figure 3: The three stages of analytic adoption
Aspirational organization typically only utilizes data to cut cost. They do not have many people and technology to help them in their data analytic. Therefore, they do not know how to fully utilize their analytic, thus they only have limited use of their insights to guide their decisions.

Aspirational organization will evolve into experienced ones after they have gained some analytical experience and can collect, incorporate and take decision based on their analysis better. Experienced organization will try to expand their use of analytic into other aspects other than cost-cutting, such as growing their profit through improving customer experience.

Whereas for transformed organization, they already have the experience and expertise in data analytic, and they will utilize their expertise in this field to compete with their peers through the use of insights. They utilize their analytic mostly on growing revenue and profit in this stage.

Therefore, we can see that different stages of adoption of big data analytic utilizes them in different ways. The question is, why would these organizations try to adopt big data analytic, even though it will take so much effort and money?
Benefits
Based on our research on big data and data analytic using the TDWI research report on “Big Data Analytic Fourth Quarter 2011” by Philip Russom and “Big Data, Analytic and the Path From Insights to Values” by MIT Sloan Management Review by Steve LaValle, Eric Lesser, Rebecca Shockley, Michael S.Hopkins and Nina Kruschwitz, there are plenty of potentials of implementing big data and data analytic in various fields such as business , telecom and etc. The main benefits that can be obtained with the use of big data and data analytics are the vast amount of volume it has, real-time data processing and great variety sources of data it originates from.

Figure 5 above is a survey conducted by TDWI Research about the benefits would ensue if organisation implemented some form of big data analytic based on 1635 responses from 325 respondents, 5 responses per respondent on average.

There are three main kinds of benefits resulted from implementation of big data analytics namely consumer relations , business intelligence and other specific analytic applications.
Consumer Relations
It is ranked at the highest in the benefits resulted from big data analytic includes better targeted social influencer marketing (61%), segmentation of customer base (41%), and recognition of sales and market opportunities (38%), developing definitions of churn and other customer behaviours (35%), as well as an understanding of consumer behaviour from clickstreams (27%).
Business Intelligence(BI)
In the aspect of BI big data analytics can be resulted in more numerous and accurate business insights (45%), understanding of business change (30%), better planning and forecasting (29%), and the identification of root causes of cost (29%).
Specific Analytic Applications
Specific analytic applications such as for the detection of fraud (33%), the quantification of risks (30%), or market sentiment trending (30%), automate decisions for real-time business processes (37%).
The ‘other’ in figure 5 might include possible benefits such as customer loyalty, service experience optimization, healthcare delivery optimization, and supplier performance based on cost and quality.

With the implementation of Big data analytics, economics will definitely grow tremendously. Also, decision-making will be an easy and quick task to be done. Thus, the efficiency of work will be higher than never before.
Challenges
28575589280Although there are many potentials that can be sought from big data and data analytic, there are still some challenges need to be overcome to implement big data and data analytics .Figure 6 above is a survey conducted by TDWI Research about the top potential barriers to implementing big data analytics in respondents’organisations based on 1153 responses from 325 respondents , 3.5 responses per respondent on average. Figure besides figure 6 is a survey conducted by MIT Sloan Management Review about the impediments to becoming more data driven .Lack of Skills Sets And Understanding For Big Data And Data Analytics
Without a doubt , it is one of the main barriers whom the organisations are currently facing nowadays. This is evident when inadequate staffing or skills for big data analytics (46%) and lack of understanding of how to use analytics to improve business (38%) both ranked at the top of their respective surveys. Also, other skill-related barriers include the difficulty of architecting a big data analytic system (33%) and current data warehouse modeled for reports and OLAP only (22%) in figure 6 and lack of skills internally in the line of business (28%) and ability to obtain the data (24%) in the figure on the right.
Business Sponsorship or Support
Undeniably, a lack of business sponsorship or support has become one of the greatest obstacles to overcome to big data and data analytics. This is shown when lack of business sponsorship (38%) and a lack of a compelling business case (28%) and overall cost (42%) are placed on the top in figure 6 and in another figure , it also shows lack of executive sponsorship (23%). Based on these two figures, it has proven that support from business is needed to implementation of big data and data analytics.

Conclusion
In this paper, we search, select, review and write about the current research trend of big data and data analytics. The objective of this paper is to let the people to be exposed to big data and data analytics and to be aware of the importance of it. As mentioned earlier, barriers still exist to hinder our action to implement big data and data analytics to our business. Now, here comes the crucial question that many organisations have been wondering, ‘ Is big data considered mostly a problem or mostly an opportunity?’.

Based on Figure 7 conducted by TDWI Research, 70% of the respondents think that big data is considered mostly an opportunity because it yields detailed analytics for business advantage in their respective organisation while only 30% of respondents think big data is a problem because it is hard to manage from a technical viewpoint. Thus, many believe that big data and data analytics are able to discover new facts about customers, suppliers, costs, trends and etc which will increase the effectiveness of the business. Lastly, based on the survey paper from ‘Journal of Big Data’, to welcome the new age of big data, the possible high impact research trends are listed as below:
For the computation time, parallel computing will do all the data analytics work for big data for its great velocity. As a result, the technologies of cloud computing, Hadoop, and map-reduce will be parts of the big data and data analytics to handle the data analysis as soon as possible.
In the aspect of social network, big data and data analytic can be implemented to detect the behaviour of the users and provide the necessary things to the user. For example, data of the users will be analysed every time the user search for a certain things, hence, related information will be shown up to the user without user searching it by its keywords in the future.
Data privacy and security issues will be arisen if we enter the age of big data since large amount of data will continually be produced and stored every single day. There is no doubt that data protection will be one of the biggest research to welcome the age of big data.
Reference
Steve LaValle, Eric Lesser, Rebecca Shockley, Michael S.Hopkins and Nina Kruschwitz, A.(2011, Winter). Big data, analytics and the path from insights to value. MIT Sloan Management Review. Retrived from http://tarjomefa.com/wp-content/uploads/2017/08/7446-English-TarjomeFa.pdf
Philip Russom , A. (2011). TDWI best practices report big data analytics. Fourth quarter 2011, Retrived from https://vivomente.com/wp-content/uploads/2016/04/big-data-analytics-white-paper.pdf
Chun-Wei Tsai, Chin-Feng Lai, Han-Chieh Chao and Athanasios V. Vasilakos,A. (2015). Big data analytics : a survey. Journal of big data. Retrived from https://doi.org/10.1186/s40537-015-0030-3Amir Gandomi, Murtaza Haider . (April 2015) . Beyond the hype: Big data concepts, methods, and analytics”. International Journal of Information Management. Retrived from https://www.sciencedirect.com/science/article/pii/S0268401214001066
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