Big Data Analytics Applications

This assignment delves into the multifaceted realm of big data analytics, examining its diverse applications across industries such as healthcare, government, urban planning, and economics. It critically analyzes the technical challenges associated with big data processing and highlights the potential benefits of leveraging large datasets for informed decision-making. Real-world examples and case studies illustrate the impact of big data analytics on contemporary society.

Running head: BUSINESS INTELLIGENCE USING BIG DATA
Business Intelligence using Big data
Name of the Student
Name of the University
Author’s Note

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BUSINESS INTELLIGENCE USING BIG DATA
Table of Contents
Introduction......................................................................................................................................4
1. Identify, create and discuss Business Strategy for a Big Data use case......................................4
1.1 Organizational Analysis.........................................................................................................4
1.2 Nature of the business............................................................................................................4
1.3 SWOT analysis......................................................................................................................5
1.4 Stakeholder analysis..............................................................................................................6
1.5 Business pressure analysis.....................................................................................................6
1.6 The need for developing big data strategies..........................................................................6
1.7 State the big data strategy that the organization wants to adopt............................................7
2.1 Describe the initiatives that the organization needs to take under such a strategy................7
2.2 Discuss the outcomes of each initiative and the critical success factors for each initiative. .8
2.3 Discuss the sources of big data that have to be used for supporting such tasks under each
initiative.......................................................................................................................................8
3. Identify and discuss the required Technology Stack...................................................................9
3.1 Availability of various big data technologies and their characteristics.................................9
3.2 Detailed discussion of the adoption of specific big data technologies for supporting this big
data strategy...............................................................................................................................10
3.3 The implementation process................................................................................................10
4. Discussion on Data Analytics and MDM to support DS&BI....................................................11
4.1 Define data analytics and multi-dimensional data analysis and discuss their importance. .11
4.2 Process of using data analytics and multi-dimensional data analysis for supporting
decision-making in organizations..............................................................................................11
4.3 Kinds of data analytics that is used by the organization under the proposed big data
strategy for support decision making.........................................................................................12
5. Discussion support of NoSQL for big data analytics................................................................12
6. Different NoSQL Databases and Their uses..............................................................................13
7. Role of Social media in organization’sdecision-making process..............................................13
8. Big Data Value creation in organizations..................................................................................14
Conclusion.....................................................................................................................................14
References......................................................................................................................................16

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Introduction
Big data refers as voluminous amount of both unstructured as well as structured data that
the organizations generally utilize for analyzing the profit of the business (John 2014). It is
identified that Big data can be utilized in business intelligence as well as decision-making
procedure. It is found that the report mainly focuses on the organization “Woolworths” which is
one of the retail supermarkets in Australia. The organization faces number of challenges as well
as issues from its competitors and in order to resolve the challenges the organization utilizes the
strategy of social networking within the organization that generally creates knowledge as well as
business intelligence. In addition to this, the big data strategy would be helpful in enhancing cost
effective scalability and assists in improving business intelligence
1. Identify, create and discuss Business Strategy for a Big Data use case
1.1 Organizational Analysis
The organization Woolworths Limited is one of the supermarket chains in Australia. It is
identified that the organization is most trusted as well as reliable brand in Australia. The strategy
of the organization for branding is considered as the main factor behind the success of the
organization (Provost and Fawcett 2013). It is analyzed that the main priority of Woolworths is
the quality of products and thus quality provided by the organization helps in creating faith and
trust among people
1.2 Nature of the business
Woolworths limited is one of the retail chains of stores that generally helps in offering
different ranges of food, clothing, merchandise. The organization has mainly 293 corporate
stores and 145 franchise stores around the world. Woolworth’s financial services are operated

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jointly with ABSA and assists in providing customers with number of credit for assisting them to
purchase number of merchandise in the stores of Woolworths (Lazer et al. 2016). The brand of
Woolworth generally incorporate number of food store which are mainly attached with the
department store in various prosperous areas.
1.3 SWOT analysis
The SWOT analysis is one of the procedures that help in identifying the strengths,
weaknesses, opportunities as well as threats of the organization.
Strengths Weaknesses
 Strong brand name as well as efficient
operations
 Successful as well as popular own store
brand
 Pioneer as well as among of the oldest
company that introduce model of retail
trade
 Well known in the market in the market of
Australia
 Improper global presence as compared to
competitors
 Brand fails in sustaining proper
competitive advantage
 Entered within the online market late
Opportunities Threats
 Promotes the brand with the help of
advertising, promotions
 Generally seek growth with the help of
franchise model and strategic acquisitions
 More competition from various internal
organizations
 Economic recession that hinders the growth
strategy

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 Profit margin will be impacted due to rising
cost of both food as well as non-food
1.4 Stakeholder analysis
Stakeholder analysis is one of the significant techniques of stakeholder identification as
well as analysis. Woolworth’s stakeholder helps in influencing the various strategic direction of
the entire organization (Raghupathi and Raghupathi 2014). It is identified that proper analysis,
the stakeholders of Woolworths are generally categorized into various levels depending on the
beliefs as well as policies of the organization. The groups include investors, customers,
employees as well as suppliers. It is identified that various types of managerial decisions are
generally based on the stakeholder of the organization.
1.5 Business pressure analysis
It is identified that the dominant retail giant Wesfarmers generally hold 50% of the
market share of Australian fresh food grocery. Meanwhile it is found that the major retailers like
Woolworths are feeling disruptive due to the competition in the market of Australia (Murdoch
and Detsky 2013). It was mainly reported by Wesfarmers that $1.4 billion profit is earned by
them whereas it Woolworth reported that that confronted with loss of $1.3 billion. Thus it is
identified that the competition in the market creates for Woolworths in the market of Australia.
1.6 The need for developing big data strategies
The big data strategy will be helpful in changing the way in which the business operate
as well as compete. It is identified that organization like Woolworths will successfully derive
values of data that have distinct advantage over various competitors (Kitchin 2014). The big data

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strategy would be helpful in enhancing cost effective scalability and improving business
intelligence. In addition to this, utilization of big data in Woolworths helps in reducing
proprietary hardware as well as software costs.
1.7 State the big data strategy that the organization wants to adopt
The organization wants to utilize social networks in order to either exploit knowledge or
for creating business intelligence. It is identified that by utilizing social aspect of data analysis as
well as data popularity, the IT staff as well as executive offers large number of befits to the
organization (Chen and Zhang 2014). It is found that social networks associated with BI assist in
learning s well as sharing within the entire organization. The creation of BI tools mainly assist in
predictive analysis alas smart data visualization that helps the business users with proper tools as
well as algorithms that provide proper access to the users that is quite easy to share as well as
personalize.
2. Identify and align business initiatives, objectives and tasks with the
developed Business Strategy
2.1 Describe the initiatives that the organization needs to take under such a strategy
The initiatives that the organizations take under the strategy are as follows:
Understand various types of opportunities as well as key threats: The organization
must go through the strategy of the organization so that social media can be creatively helpful for
the entire organization.
Plan the content: It is very much important to map the content of social media before
launching the entire campaign (Jagadish et al. 2014).

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Know the audience: The most important is to aim the audience in detail. This is
possible because the organization cannot produce targeted and content unless they have proper
knowledge about the audience.
Measure: The organizations must make sure that they achieve their goals. A quick round
of key stats helps in achieving success (Hashem et al. 2015).
2.2 Discuss the outcomes of each initiative and the critical success factors for each initiative
The outcome of each initiative and the critical success factor for each initiative is
provided below:
Understand opportunities: The organization had gone through various strategies so
that social media can be helpful for the entire organization.
Content planning: The content of social media is mapped properly before launching the
entire campaign (George, Haas and Pentland 2014).
Understanding audience: The organization aims the audience properly in detail as this
is only possible, as the organization does not produce targeted and content unless they have
proper knowledge about the audience.
Measurement: The organization ensures that they have achieved their goals by
measuring the various success factors.
2.3 Discuss the sources of big data that have to be used for supporting such tasks under
each initiative
The sources of big data that is mainly utilized for supporting the task in each initiative
include social network profiles, social influencers and activity generated data. It is identified that

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a straightforward integration of API for importing various pre-defined fields as well as values is
quite important (Groves et al. 2016). It is identified that activity generated data are also
necessary for understanding opportunities, content planning, understanding audience as well as
measurement.
3. Identify and discuss the required Technology Stack
3.1 Availability of various big data technologies and their characteristics
The available big data technologies and their characteristics are provided below:
Hadoop: The characteristics of Hadoop are provided below:
 Not complex: It is identified that Hadoop is not complex and thus it helps in
providing simple as well as smooth handling technique (Barrett et al. 2013).
 Error recovery: It automatically assists in replicating the data is the server crash
 Decrease overload: It helps in distributing data on different servers for
minimizing network overloading
NoSQL: The characteristics of NoSQL are as follows:
 Scalability: It is analyzed that it does not requires expansion for becoming itself
in the size of server (Sagiroglu and Sinanc 2013).
 Multiple storage system: Data can be properly stored as document as well as key
value.
 Simple as well as easy layout: It is very much simple to create the entire
architectural layout:

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3.2 Detailed discussion of the adoption of specific big data technologies for supporting this
big data strategy
The Big data technology generally helps the organization in handling large volume of
complex as well as unstructured data from various social sources. By taking the instance of big
data analytics platform need to deal with information from various sources such as sites of social
media (Varian 2014). It is identified that the connector generally helps in enabling the
conversion of data from different kinds of sources into Hadoop based data warehouse. After
proper collection of the data, Apache’s Mahout, which is one of the scalable machine learning as
well as data mining solution, can be generally utilized for categorizing the data for storing it
properly as per the categories.
3.3 The implementation process
The implementation of Hadoop in context to social network strategy that helps in creating
knowledge as well as business intelligence includes some steps, which are as follows:
Analyze technical and business requirements: It is identified that utilization of big data
technology for the business as well as technical requirement must be analyzed (Assunçao et al.
2015).
Prepare to be agile: Starting with the technology of Hadoop is quite confusing therefore,
it is quite important to have proper specific data set.
Analyze benefits of social network: The utilization of social network must be analyzed
properly.

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Utilizing existing framework: The existing framework of the organization must be
utilized during the implementation of new strategy (Kitchin 2014).
Training new people: The workers of the organization must be trained properly after the
adoption of the technology.
4. Discussion on Data Analytics and MDM to support DS&BI
4.1 Define data analytics and multi-dimensional data analysis and discuss their importance
Data analytics: Data analytics is referred as qualitative as well as quantitative technique
or procedure that is utilized for enhancing the productivity as well as business gain (Kim et al.
2014). It is identified that data analytics help the organization like Woolworths by providing
proper job opportunity, assists the business growing need for coordination as well as
mainstreaming the utilization of big data in marketing.
Multi-dimensional data analysis: It is defined as one of the informational analysis
technique that generally helps in grouping two types of data that include data measurements and
data dimensions. It is considered as one of the intuitive way for people for analyzing data that are
easy, neutral as well as attractive.
4.2 Process of using data analytics and multi-dimensional data analysis for supporting
decision-making in organizations
The procedure of using analytics for decision making in organization includes six critical
steps that are as follows:

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Recognize problems: It is identified that effective definition of problem is quite
necessary
Review findings: Proper effort must be made in order to leverage various types of
experience (Wang et al. 2014).
Model solution: Depending on the detailed problem, proper hypothesis must be formed.
Collect data: This step helps in assuming credit union data which must be properly
integrated across the data quality as well as the organization for access
Analyze data: The analysis team must be selected wisely the skills, knowledge as well as
experience so that it can be applied to big data analytics tool (Hu et al. 2014).
Presenting result: The decision maker takes the center stage and assists the stakeholders
for taking action so that problem can be solved.
4.3 Kinds of data analytics that is used by the organization under the proposed big data
strategy for support decision making
It is identified that descriptive analytics are generally utilized by the organization for the
proposed big data strategy in order to support decision-making process (Wamba et al. 2014).
Descriptive analytics helps in uncovering various types of patterns that offer appropriate insight.
It is mainly useful in sale cycle, categorizing customers in making various types of preferences.
5. Discussion support of NoSQL for big data analytics
It is identified that NoSQL database mainly helps in supporting dynamic scheme design
by offering the appropriate potential to enhance flexibility, scalability, as well as customization

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in comparison to various relational software (Barrett et al. 2013). This assists them appropriate
for content management system, web applications as well as various types of on-uniform data for
updating different types of varying field format. It is found that these types of database are
mainly designed with big data requirements in mind.
6. Different NoSQL Databases and Their uses
According to (), NoSQL database helps in providing number of advantages over various
types of traditional relational database. The various types of NoSQL include:
Key value store NoSQL database: This is considered as the simplest database and it is
identified as ideal choice while relationship between two data values is needed (Kitchin 2014).
Document store NoSQL database: This type of database is quite similar to
various key value databases and these types of database are considered as the right choice for
running various types of complex search queries (Jagadish et al. 2014).
Column store NoSQL database: In this type of database, data is generally
stored within the cell that is grouped within column rather on row. The main advantage of
storing data in column is fast data aggregation.
7. Role of Social media in organization’sdecision-making process
Social media is responsible in driving the communication, collaboration as well as
decision making procedure of organizations. It is identified that the three important social
networks like Facebook, LinkedIn, Twitter have emerged recently as the professional network
for the people and this type of network are considered as one of the essential decision making

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technique (Lazer et al. 2014). It is found that social media generally act as a tool of
communication driven support system that helps various managers as well as executives in
taking proper decision. Moreover, the managers can utilize social media for influencing the
behavior of the consumer.
8. Big Data Value creation in organizations
The technology of big data helps in providing unprecedented access to large amount of
data and information that is otherwise would costly to pursue for the organizations. It is
identified that the potential of big data in value creation is huge and there are number of factors
that helps in influencing successful as well as effective value creation.
IT intensity: This is considered as one of the big factor when it is found that right tools,
It engagement as well as support is necessary.
Data availability: Many organizations have incomplete data tat helps in making it
available on time for the consumers (Raghupathi and Raghupathi 2014).
Data driven: This is considered as one of the significant cultural factor. It indicates the
utilization of data in decision-making procedure by the organization.
Analytical talent: It is identified that some of the organizations heavily investing on
analytical talent in context to industry requirements and it is considered as premium talent.
Conclusion
It can be concluded from the entire assignment that utilization big data strategy would be
helpful in enhancing cost effective scalability and improving business intelligence. In addition to

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BUSINESS INTELLIGENCE USING BIG DATA
this, utilization of big data in Woolworths helps in reducing proprietary hardware as well as
software costs. It is found that utilization of social networks helps in either exploiting knowledge
or in creating business intelligence. It is identified that by utilizing social aspect of data analysis
as well as data popularity, the IT staff as well as executive offers large number of befits to the
organization. It is found that social networks associated with BI assist in learning s well as
sharing within the entire organization. The creation of BI tools mainly assist in predictive
analysis alas smart data visualization that helps the business users with proper tools as well as
algorithms that provide proper access to the users that is quite easy to share as well as
personalize.

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BUSINESS INTELLIGENCE USING BIG DATA
References
Assunção, M.D., Calheiros, R.N., Bianchi, S., Netto, M.A. and Buyya, R., 2015. Big Data
computing and clouds: Trends and future directions. Journal of Parallel and Distributed
Computing, 79, pp.3-15.
Barrett, M.A., Humblet, O., Hiatt, R.A. and Adler, N.E., 2013. Big data and disease prevention:
From quantified self to quantified communities. Big data, 1(3), pp.168-175.
Chen, C.P. and Zhang, C.Y., 2014. Data-intensive applications, challenges, techniques and
technologies: A survey on Big Data. Information Sciences, 275, pp.314-347.
George, G., Haas, M.R. and Pentland, A., 2014. Big data and management. Academy of
Management Journal, 57(2), pp.321-326.
Groves, P., Kayyali, B., Knott, D. and Kuiken, S.V., 2016. The'big data'revolution in healthcare:
Accelerating value and innovation.
Hashem, I.A.T., Yaqoob, I., Anuar, N.B., Mokhtar, S., Gani, A. and Khan, S.U., 2015. The rise
of “big data” on cloud computing: Review and open research issues. Information Systems, 47,
pp.98-115.
Hu, H., Wen, Y., Chua, T.S. and Li, X., 2014. Toward scalable systems for big data analytics: A
technology tutorial. IEEE access, 2, pp.652-687.
Jagadish, H.V., Gehrke, J., Labrinidis, A., Papakonstantinou, Y., Patel, J.M., Ramakrishnan, R.
and Shahabi, C., 2014. Big data and its technical challenges. Communications of the ACM, 57(7),
pp.86-94.

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John Walker, S., 2014. Big data: A revolution that will transform how we live, work, and think.
Kim, G.H., Trimi, S. and Chung, J.H., 2014. Big-data applications in the government
sector. Communications of the ACM, 57(3), pp.78-85.
Kitchin, R., 2014. The real-time city? Big data and smart urbanism. GeoJournal, 79(1), pp.1-14.
Kitchin, R., 2014. The data revolution: Big data, open data, data infrastructures and their
consequences. Sage.
Lazer, D., Kennedy, R., King, G. and Vespignani, A., 2014. The parable of Google Flu: traps in
big data analysis. Science, 343(6176), pp.1203-1205.
Murdoch, T.B. and Detsky, A.S., 2013. The inevitable application of big data to health
care. Jama, 309(13), pp.1351-1352.
Provost, F. and Fawcett, T., 2013. Data science and its relationship to big data and data-driven
decision making. Big Data, 1(1), pp.51-59.
Raghupathi, W. and Raghupathi, V., 2014. Big data analytics in healthcare: promise and
potential. Health information science and systems, 2(1), p.3.
Sagiroglu, S. and Sinanc, D., 2013, May. Big data: A review. In Collaboration Technologies and
Systems (CTS), 2013 International Conference on (pp. 42-47). IEEE.
Varian, H.R., 2014. Big data: New tricks for econometrics. The Journal of Economic
Perspectives, 28(2), pp.3-27.