Refers Voluminous Amount Both Unstructured Essay

Question:

Discuss About The Refers Voluminous Amount Both Unstructured?

Answer:

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

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

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 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.

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

· Profit margin will be impacted due to rising cost of both food as well as non-food

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.

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.

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 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.

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

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).

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).

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.

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 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.

Identify and discuss the required Technology Stack

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:

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.

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 (Assun?ao 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.

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.

Discussion on Data Analytics and MDM to support DS&BI

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:

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.

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.

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 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.

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.

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 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.

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 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.

References

Assun??o, 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.

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.

Wamba, S.F., Akter, S., Edwards, A., Chopin, G. and Gnanzou, D., 2015. How ‘big data’can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, pp.234-246.

Wang, L., Zhan, J., Luo, C., Zhu, Y., Yang, Q., He, Y., Gao, W., Jia, Z., Shi, Y., Zhang, S. and Zheng, C., 2014, February. Bigdatabench: A big data benchmark suite from internet services. In High Performance Computer Architecture (HPCA), 2014 IEEE 20th International Symposium on (pp. 488-499). IEEE.

How to cite this essay: