Big data use in Transport NSW multiple projects
Discuss about the Strategic Project Management in NSW.
A consulting firm has appointed a project manager to manage multiple projects which Transport NSW is currently handling. It is the responsibility if the project management team to diagnose the risks while carrying out the multiple NSW projects simultaneously. The project stakeholders involved with the project is responsible for carrying out the following projects- they will have to identify the train stations in Sydney as well as the infrastructure which includes the timeline estimation, design of the train stations and lastly the resources identification (Culnane, Rubinstein and Teague 2017). The stakeholders have been given the responsibility to maintain the existing stations of Sydney. They will have to operate in accordance with the key performance indicators. NSW has also given the responsibility to the stakeholders to build new rail stations in Sydney. They will have to restore all the train stations which are in dire needs. The stakeholders are concerned if they conduct the project activities in parallel then it may happen the project quality will suffer, and they will not be able to finish the project within given timeline. NSW can solve this by applying big data analytics. The big data analytics helps in making decisions relatively easy. Both the reporting as well as the analytics can assist the project management team to understand the NSW construction project activities (Mulley et al. 2017). The project manager with the aid of big data can be able to take the critical decisions which are required to complete the project with ease. They also get the facility to use the timely insights as well as the customer insights to engage them in the collective feedback or more information about the ongoing project activities or the ongoing project progress. The big data will assist to schedule the construction project and will let them know the potential outcome of the project. Thus big data will help them to conduct the multiple projects in parallel.
The stakeholders are concerned whether the project will meet the quality or not. The big data analysis will help them to implement the policies within the company premises. They can be able to know the strengths of the employees. The big data analytics help to prepare schedule resources for the project and in this way the managers can be able to allocate resources wisely. The project team can integrate the entire team and can meet the project objectives.
Summary of case study: ‘Analytics: The real-world use of big data in financial services’
Big data technology is not limited to technology; it provides solutions to long-standing business challenges for the financial market companies as well as the banking companies all over the world. The financial service companies are dependent on big data to transform their business.
The recent survey states that the enterprises are currently on the stages of the big data planning as well as development efforts. The employees and the management staffs are not used to the big data technology (Weisbrod, Mulley and Hensher 2016). The banking and the financial markets companies have started to adopt big data in their premises. Nearly about 26 percent banking and financial market companies are focussing on learning the fundamental concepts.
The enterprises were asked for the three objectives for the big data, out of which 55 percent of the banking, as well as the financial markets, consider customer-centric objectives as the top priority (Debnath et al. 2014). The banks and the financial institutions are under tremendous pressure to get transformed from product centric to customer-centric organisations. The financial institutions are trying to provide the customer-centric products in this way they will be able to provide customer-centric service and loyalty.
The banks and the financial institutions will have to set up information foundation. The information foundation must be capable of holding the large chunks of data. The respondents were asked regarding the big data projects (Yin and Kaynak 2015). This approach can make it easy to assess the current state of the big data infrastructure. Fifty percent of the bank and the commercial companies report that they have the integrated information structure.
Initial big data efforts are focussed on gaining insights from the existing and recent sources of internal data (Kejariwal, Kulkarni and Ramasamy 2015). According to the recent survey, more than half of the banking and the financial organisations consider the internal data as the primary source of big data in their premises.
Big data does not create value. However, the big data is used to address important business challenges. More and more data is required to conduct the analysis. Various kinds of samples are used to complete the analysis. The financial organisations by analysing the data can be able to adopt the pragmatic approach. The banking and the financial institutions decide to take up the financial services hence it can be predicted that they have the desired analytical skills, predictive modelling as well as optimisation and simulations. However, they are lagging in advanced data visualisation and text analytics (Hashem et al. 2015). The banks and the financial institutions by adopting big data can improvise the data analytics performance and in turn, can improvise the business. The respondents were asked to describe the level of big data activities. The results suggest four main stages of big data adoption. The stages are the Educate, Explore, Engage and Execute.
Challenges of big data implementation in the financial organisation
There are several challenges for implementing the big data in the financial organisation. Out of several other challenges, two challenges will be discussed. First one is the talent deficit and the second one is the security. There is a tremendous demand of the data scientists in the industry whereas supply is acute in the industries (George, Haas and Pentland 2014). The shortage can be prominently observed in the sectors like the auto and industrial equipment. The enterprises require practical advantages of building, buying, borrowing analytic talent to develop the capabilities other require.
The banks and the financial organisations generally use the traditional servers, and they are not suitable for the big processing data. HPC servers are having high performance, and the analytics server are required for the big data analytics. Thus the banks and the financial organisations will have to pay heavy for the adopting the IT big data in their premises. The banks will have to risk as the big data analytics will facilitate them in the long run. The CIO will have to create the business report in simple English so that the other stakeholders of the company can understand the purpose of installing the server and the big data analytics in the premises (Lee and Lee 2015). The CIO should analyse the objectives of big data analytics, and he must analyse what the potential benefits he and his organisation is going to it, after that he should proceed on to the investment are.
From the figure 2, it can be observed that the near about three-quarters of the financial services companies have initiated implementing big data in their enterprises. If the baking and the financial market is taken into consideration, then it can be stated that 26% of the banking and the financial market have not started the big data activities. Other 47% is focussing on planning big data analytics (Laudon and Laudon 2016). Another 27% is concentrating on implementing the business activities.
Project management generally involves five groups namely, the initiating, planning, execution, monitoring, control and finally the closing. The project closure phase is generally carried out by the project manager. The project manager can hand over the deliverables to the clients. However, it does not signify that the project is complete. The project is still incomplete. It may happen that the customer is not happy with the project deliverables. The client or the customer may opt for some modifications, he may ask for a redo. That is why agreement form the client’s end is an absolute necessity (Rahschulte, Martinelli and Milosevic 2016). If the client agrees to the deliverables, then the project can be stated as complete. The project manager will have to keep a record of all the lessons learned from the project. Thus the project closure is very important and must not be ignored. The project manager will have to ensure the success of the project and the project deliverables. The project manager must compile all the project files and must convey to the stakeholders involved in the project. All these archived files can be used in mere future.
Activities carried out at the time of project closure
The project closure consists of the following four key activities- formal customer sign-off, detailed scope analysis of the final product, lessons learned documentation and lastly the release of the resource. The project manager is responsible for handing over the project deliverables to the customers. If the customer agrees with the deliverables, he or she will have to sign-off. The customer sign-off signifies that the project is completed.
The project manager will have to analyse the scope analysis of the product. The project manager will have to assess that the project scope and will have to check whether the project is being undertaken as per planning or not (Lenaghan et al. 2015). He will have to analyse whether the project is up to the mark or not. The project scope should meet the demand of 100%, only then the project can be termed complete.
After that comes the release of the resource. The project manager allocates resources for the project. He analyses the best person for the service and allocates resources for the project. After delivering the project and receiving the formal sign-off from the customer, the project manager must handover resources to the respective departments, so that those resources can be further used in the future. The project manager will have to follow the policies appropriately before the resource release.
References
Culnane, C., Rubinstein, B.I. and Teague, V., 2017. Privacy Assessment of De-identified Opal Data: A report for Transport for NSW. arXiv preprint arXiv:1704.08547.
Debnath, A.K., Chin, H.C., Haque, M.M. and Yuen, B., 2014. A methodological framework for benchmarking smart transport cities. Cities, 37, pp.47-56.
George, G., Haas, M.R. and Pentland, A., 2014. Big data and management. Academy of management Journal, 57(2), pp.321-326.
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.
Kejariwal, A., Kulkarni, S. and Ramasamy, K., 2015. Real time analytics: algorithms and systems. Proceedings of the VLDB Endowment, 8(12), pp.2040-2041.
Laudon, K.C. and Laudon, J.P., 2016. Management information system. Pearson Education India.
Lee, I. and Lee, K., 2015. The Internet of Things (IoT): Applications, investments, and challenges for enterprises. Business Horizons, 58(4), pp.431-440.
Lenaghan, D., Dolan, B., Franklin, P., Beevor, J., Danner, G., Barrett, M. and Spataru, C., 2015. Investigating the potential impact of new energy technologies on the shape and scale of electricity demand in the UK Project Closure Document.
Mulley, C., Clifton, G.T., Balbontin, C. and Ma, L., 2017. Information for travelling: Awareness and usage of the various sources of information available to public transport users in NSW. Transportation Research Part A: Policy and Practice, 101, pp.111-132.
Rahschulte, T., Martinelli, R.J. and Milosevic, D.Z., 2016. Project Closure. Project Management ToolBox, Second Edition, pp.351-374.
Weisbrod, G., Mulley, C. and Hensher, D., 2016. Recognising the complementary contributions of cost benefit analysis and economic impact analysis to an understanding of the worth of public transport investment: A case study of bus rapid transit in Sydney, Australia. Research in Transportation Economics, 59, pp.450-461.
Yin, S. and Kaynak, O., 2015. Big data for modern industry: challenges and trends [point of view]. Proceedings of the IEEE, 103(2), pp.143-146.
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