The big data analytics has been helping the companies to harness their data and utilize that for identifying the latest scopes. In turn this has been leading to smart business approaches, higher profits, happier customers and efficient operations (Provost and Fawcett 2013). Its presence has left impact on cost reduction, faster and better decision making and latest services and products.
There has been no definite technology encompassing the big data analytics. There has been various advanced analytics applied to the big data. However various kinds of technology have been working together for helping to retrieve the best value from the data. The biggest players lying here are the data mining, data management, text mining, predictive analytics and in-memory analytics.
The big data has been a big deal for the Commonwealth Bank that has been changing the way how the customers could pay and quickly the business get paid. Daily settle and the other merchant solutions and the mean funds have been quickly credited to the clients account regarding invaluable insights and real results.
The report aims to analyze the new decision making techniques under the light of big data. The fundamental objective is to undergo through a theoretical analysis providing a clear understanding.
The study has included the organizational analysis, big data strategy and multi-dimensional analysis. It has considered the case of NoSQL for big data analytics and lastly the role of social media in the process of decision making.
The Commonwealth Bank has been delivering various economic services. This includes business, retail and institutional banking, superannuation, insurance, funds management, broking and investment services (Provost and Fawcett 2014).
|
SWOT Analysis |
|
|
Strength |
Weakness |
|
The bank posses a strong revenue and rising profit |
The bank has witnessed the loan impairment. |
|
Opportunities |
Threats |
|
Implementation of the technology at the core banking activities could help to increase the quality of services. |
The bank could face risks because of changes in foreign exchange rates. |
Figure 1: “Stakeholder Analysis at Commonwealth Bank”
(Source: Www1.worldbank.org, 2017)
Figure 2: “The business pressure analysis at Commonwealth Bank”
(Source: Markets.theaustralian.com.au, 2017)
Since the customer relationships have been disrupted by the technological upheaval at the sector, the big data strategies have turned out to be one of the largest strategic focuses for the Commonwealth Bank at Australia.
Holistic:
For setting the basis for the long term success, the companies require the big-picture viewing that identifies the various distinct elements of the effective system.
Focusing on business:
The strategic planning regarding the big-data must be led by business.
Flexibility:
The strategies should account for the creation of the incremental value and the overall evolutionary process.
Scalability and structure:
The primary step has come forward by the adaptable and powerful ecosystem linking the discovery and the data platforms. This has been regarding the long-term scalability and connects to the crucial external sources (McLeod et al. 2017).
Commonwealth should think how to accelerate the innovation and predict the trends of the seasonal demand. They must find out the most effective supply chain partners and uncover the unnecessary costs of overhead.
Whether Commonwealth Bank performs the personalization of the promotional offers or the loyalty programs for various client segments, the big data could deliver the insights for helping to do that quickly and with more sustainably.
In order to keep the proper strategy in place, a unified and strong architecture to make that accessible could be used (Raghupathi and Raghupathi 2014). For creating the best value from the investments of big data investments, daunting and addressing the question regarding what to do with these data must be done.
The NoSQL or Columnar data sources include the InfoBright, Cassandra or MongoDB are the instances of the latest kinds of map reducing repository and the data aggregator.
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Big data technologies |
Characteristics |
Discussion |
Implementation |
|
Hadoop |
It stores data and run applications on the clusters of the commodity hardware |
It has huge data storage for any type of data with outstanding power of processing. |
As the security projects like Rhino, Sentri and others achieves stability, the implementation of Hadoop expands. |
|
Cloud solutions |
It provides perfect scalable resolution to manage large volume of information. |
As the Internet of Things has been spreading in the market place, the generation of data is on rise. There have been various advantages of Hadoop on the cloud in order to sustain various big data technologies. |
This could be implemented either by taking the server and put that on anyone else’s data centre or by having a service provider managing the devices. |
|
Self-Service Big Data applications |
This simplifies the data preparation, data cleaning and the tasks of data exploration. |
The tools such as the Hadoop and Tableau is been rising in popularity in the last few decades (Demirkan and Delen 2013). This highly reduces the efforts of end-users. |
Regarding implementation, it must be kept in mind that the bank has to evolve beyond the spreadsheets and IT. They should nor settle for what they already had and generate a core community of data. |
|
Revolutionizing of traditional database |
This has been consisting of the structured data |
For managing and processing the data, the NO-SQL databases are the best choice since the previous few years. |
The NO-SQL data bases like the Cassandra and MongoDB would be getting more implemented by the vendors (Nguyen and Cao 2015). |
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Definition |
Importance |
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The data analytics or DA has been the method to examine the datasets. |
This helps in drawing the conclusions regarding the data that remains rising with the aid to specialized software and system. |
|
The multidimensional analysis if the process of data analysis in the field of econometrics and statistics. |
It helps in grouping data into two sections, the data measurements and data dimensions (Janssen, van der Voort and Wahyudi 2017). |
The data analytics has been created on the strong grasp of the probability and statistics. These skills have been utilized to support the decision making. Moreover the big data scales that up (Sagiroglu and Sinanc 2013).
Through the adoption of the multidimensional technology of data analysis to the statistics helps in analyzing the basic data of the banking industry. Thus the analyses of the results are yield for the decision making.
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Types of data analytics |
Discussion |
|
Prescriptive |
This reveals the type of actions to be taken. These results in recommendations and rule for the following steps. |
|
Predictive |
This analyzes the suitable situations that could take place. Here the deliverables are generally of the predictive forecast. |
|
Diagnostic |
This look at the previous performance for determining what have occurred and why. The outcome of the analysis has been the analytic dashboard (Bailey 2016) |
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Descriptive |
There the occurrence has been based on the incoming data. For mining the analytics the real time dashboard is used. |
With the emergence of the different NoSQL software platforms, the IT business executives and managers are involved in the technology possess more choices on the database deployments. The NoSQL databases have been supporting the dynamic design of schema. This has been delivering the potential for the rise in customization, scalability and flexibility compared to the relational software. The NoSQL databases have been disrupting the software monopoly (Pedrycz and Chen 2015). They have dented the dominance of the relational databases. However this has not been likely to be fully broken holding that the SQL technologies have on the users. The NoSQL databases have been scaling upward for the cloud computing.
|
Types |
Uses |
Examples |
|
Key-Value Store |
It posses the Big Hash Table of keys and values |
Amazon S3, Riak |
|
Graph-based |
It uses the edges and the nodes for representing and storing data |
Neo4J |
|
Column-based Store |
Every storage block has the data from only one single column (Gandomi and Haider 2015). |
HBase, Cassandra |
|
Document-based Store |
This stores the documents that are made up of the tagged elements. |
CouchDB |
As it comes to the preparation of the decision making process, there have been various factors how the buyers could use the social platforms that are to be considered. This includes, what sites are visited, why are they be used and when the process of decision making are have been viewed by them. The source of social influence and the activities of social platform have notably influenced them (Liebowitz 2014). The consumers could use the online sources for obtaining the product information vital for the purchase decisions. This has been love of the hyping social media, but inability in realizing what requires to be done to adopt the social media at Commonwealth Bank. They could perform anything that is under their power for creating the mentions of their brand and they could make sure that there have been positive mentions.
Before the Commonwealth Bank joins that rush for spending the more over big data, it has been salutary in considering what has been really worked and which capabilities of management is needed by the bank. This is done to retrieve the value from the big data. The central finding has been that this has neither been the data itself nor the distinct data scientists (Power 2014). Moreover, the value creation takes place by the process of data management. Here the managers have been capable for contextualize, experiment, democratize and execute the data insights in a periodic manner.
Conclusion:
Though the commonwealth Bank has undertaken every effort for ensuring that the data has been error free, nevertheless no surety has been given to the completeness and accuracy. Any estimates or opinions expressed herein have been those of the bank on the data of preparation. Moreover, this has been on the subject to change without any notice. Despite this no such estimates or opinions constitute investment, legal or any other advice. Commonwealth Bank must find the independent investment, legal or the proper advice from the suitably qualified or the regulated and authorized advisor. This is before making any investment, legal or the other decisions. This has been meant for the purposes of information only. Further this has not been intended as any recommendation or offer to sell, buy or otherwise deal in the privacy or securities. The big data tools have currently existed for allowing the multiple channels for understanding. The procedures and polices manuals could remain. Now, the bank could use the tools of data measurement for easily tracking how frequently they have been accessed. They could generate and store the user-generated content like short instructions videos, blog posts, social media or job aids and then track the access. Through learning of the big data to study personalization the Commonwealth Bank have been not only expanding the amount of learning scopes but also could deepen the impact of the learning. They no longer require to be constrained by the lack of data that forces learning on their audience in the way they could not prefer. Various modalities of the similar data have been no longer meaning the training department has been inconsistent or unfocused. Through the leveraging the data according to the preference of the learner, the Commonwealth Bank have been consistently changing the learning landscape. This is optimized for every person for allowing them to discover, explore and be rewarded for the discoveries. This has been the latest method to imagine how the big data could help designing the best robust solutions of learning.
The recommendations are described below:
The Commonwealth Bank has been focusing their strategies of big data on the efforts that could deliver the most effective value of business. This indicates that beginning the strategy of analytics with the customer analytics for providing the clients with better services. This in turn must leas in better retention of customer. The outcome is that the Commonwealth Bank would require understanding their clients as the individuals. They require investing the advanced analytics and new technologies for doing this.
The blueprint of their big data strategy must cover the entire vision. The strategy and the necessities should not be on the departmental basis. This must be on the enterprise basis. It might lead to the creating of the enterprise-wide common understanding regarding how the bank expects to utilize the usage of big data, hardware and tools required for making the blueprint in actuality. With this the bank must be able to recognize the primary challenges of the business to get overcome with the business process necessities defining how the big data could be used.
For achieving the short-term outcomes as the implementation of big data begins and collects steam. The enterprises require becoming realistic regarding what they could achieve at first. For the people who have imposed the successful strategy that is delivering the business value already, the simplest space to collect insights has been from the information that has been in the bank already.
As the marketplace gets matured, the businesses have been forced to opt between the rise number of the analytics tools and having to deal with complicated shortage of the analytics tools at Australia. The success of the big data has hinging to find the way across that.
For developing the viable strategy of big data and assuring that there would be the ongoing investment and interest from the decision makers. The enterprises require assuring that case regarding the current investment has been on the basis of the quantifiable outcomes of business. This means that the business leaders of Commonwealth Bank must be capable for seeing the benefits. Commonwealth could do this through assuring that there has been the active sponsorship and involvement from the business leaders. This must take place as the original strategy gets developed and the initial implementation occurs. Moreover the vital importance here has been the current cooperation between the IT departments and business. This must assure that the business value of the investments in analytics of big data has been understood properly.
References:
Bailey, M., 2016. 12 Will Big Data Diminish the Role of Humans in Decision Making?. Big Data Is Not a Monolith, p.163.
Demirkan, H. and Delen, D., 2013. Leveraging the capabilities of service-oriented decision support systems: Putting analytics and big data in cloud. Decision Support Systems, 55(1), pp.412-421.
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.
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.
Janssen, M., van der Voort, H. and Wahyudi, A., 2017. Factors influencing big data decision-making quality. Journal of Business Research, 70, pp.338-345.
Kaisler, S., Armour, F., Espinosa, J.A. and Money, W., 2013, January. Big data: Issues and challenges moving forward. In System Sciences (HICSS), 2013 46th Hawaii International Conference on (pp. 995-1004). IEEE.
Liebowitz, J. ed., 2014. Bursting the big data bubble: The case for intuition-based decision making. CRC Press.
Markets.theaustralian.com.au. (2017). The Australian – CBA Profile. [online] Available at: https://markets.theaustralian.com.au/shares/CBA/commonwealth-bank-of-australia [Accessed 3 Sep. 2017].
McLeod, S., Mulder, C., McGregor, M., Katz, A., Singer, A., Liddy, C., Barry, A., Eibl, J., Klein, D., Holmes, S. and Viner, G., 2017. Family Medicine Forum Research Proceedings 2016Do urine cultures in the emergency department change management of young women with symptoms of uncomplicated urinary tract infection? Ontario data support Starfield’s theory on practice quality and costHome-based primary care for frail eldersMeasuring the social determinants of health with linked administrative dataUsing big data to understand medication adherence in ManitobaUnderstanding patient referral wait times in OntarioDevelopment of a pharmacist …. Canadian Family Physician, 63(2), pp.S1-S108.
Miller, H.G. and Mork, P., 2013. From data to decisions: a value chain for big data. IT Professional, 15(1), pp.57-59.
Nguyen, H.T.H. and Cao, J., 2015. Trustworthy answers for top-k queries on uncertain big data in decision making. Information Sciences, 318, pp.73-90.
Pedrycz, W. and Chen, S.M. eds., 2015. Granular computing and decision-making: interactive and iterative approaches (Vol. 10). Springer.
Power, D.J., 2014. Using ‘Big Data’for analytics and decision support. Journal of Decision Systems, 23(2), pp.222-228.
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.
Provost, F. and Fawcett, T., 2014. Authors’ Response to Gong’s,“Comment on Data Science and its Relationship to Big Data and Data-Driven Decision Making”. Big data, 2(1), pp.1-1.
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.
Waller, M.A. and Fawcett, S.E., 2013. Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. Journal of Business Logistics, 34(2), pp.77-84.
Waller, M.A. and Fawcett, S.E., 2013. Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. Journal of Business Logistics, 34(2), pp.77-84.
Www1.worldbank.org. (2017). Stakeholder Analysis. [online] Available at: https://www1.worldbank.org/publicsector/anticorrupt/PoliticalEconomy/stakeholderanalysis.htm [Accessed 3 Sep. 2017]
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