From the ‘Internet of Trains’ case study, it’s evident that the performance of Siemens company has been greatly boosted by the use of sensor data. The sensor data helps the Siemens Company to keep the train operators on track, and this has helped to reduce the cases of train failures. Although the cases of train failures have been greatly reduced, there are still some needs which are yet to be addressed adequately. Siemens has concentrated so much on addressing the train failures which result from the engine problems and has neglected the other problems and failures which result from other components and systems of the trains. This has made it very difficult to have a 100% control and prevention of the problems affecting the train industry. Siemens has also neglected human error which is a major cause of some of the problems facing the train industry today. The human factor has not been addressed adequately, and that’s why we still have some safety problems among other human error-related problems facing the train industry (Walker and Stanton, 2017). It’s crucial for Siemens to consider the human factor and address it accordingly to reduce the cases of train failures and problems which result from human errors.
Delivering a BI solution will be very viable and very crucial in improving the overall performance of Siemens which will consequently help to reduce the cases of train failures reported in the train industry. A BI solution will help the company to consider and apply the best technologies, applications, and other digital practices which will the company in the processes of information collection, analysis, integration, and presentation of the information in the required formats (Tian, Chin, and Karg, 2016, pp.4-6). This will eventually help in improving the decision-making processes undertaken by the company, and this will help the company to come up with the best decisions and strategies which can help in reducing the cases of engine failures reported in the train industry (Thamir and Poulis, 2015, pp.34-37).
There are many risks associated with delivering a BI solution in Siemens, and so, it’s very important for the company to do a good risk assessment before delivering the BI solution. One of the main risks which face the process of delivering a BI solution is the high cost which will be incurred in the process of delivering the BI solution, and this high cost may not be recovered easily. As already said, the process of delivering a BI solution requires the company to embrace and introduce new technologies, applications, and other digital practices which will help in implementing the BI technology in the firm. These technologies, applications, and the other digital practices will cost the company huge amount of money, and the company may not recover this money after implementing the BI technology in the firm if it doesn’t plan the process well (Richards, Yeoh, Chong, and Popovi?, 2019). Therefore, it’s highly recommended that the company should plan well before delivering the BI solution and do a detailed cost-benefit analysis which can help it to address the expected risks and losses accordingly. A cost-benefit analysis will help the company to analyze the expected benefits and compare them with the expected costs of delivering the BI solution, and if the benefits surpass the costs, the BI solution will be delivered for the company to enjoy the many benefits (Boardman, Greenberg, Vining, and Weimer, 2017). Risk assessment is very crucial before undertaking any new project as it helps organizations to know the risks which may be incurred in implementing the project (Haimes, 2015). These risks can then be compared to the expected benefits, and then the right decision is made of whether to undertake the project or not.
Delivering a BI solution is expected to yield many benefits to Siemens and to the train industry. The major benefit which will be realized through delivering of the BI solution will be a significant drop in the number of train failures and problems experienced in the train industry. It’s also good to note that there will be other secondary benefits which will be realized through delivering a good BI solution in the company where some of the main secondary benefits which will be improved quality of services offered by the company and improved efficiency in the operations undertaken by the company (Olszak, 2016, pp.105-123). This will help the company and the train industry to have more customers who will help them to enjoy many benefits. A comprehensive assessment and analysis of the benefits associated with delivering a BI solution can be done using a cost-benefit analysis which helps organizations to compare all the expected costs of implementing a new project to the expected profits or benefits which will be realized from the project. After doing the benefits assessment, if the expected benefits are more than the costs and the risks expected to be incurred in delivering the BI solution, the BI solution can be undertaken and implemented to help the involved parties to enjoy the many benefits (Patassini, 2017).
There are many data management issues which affect Siemens and the train industry. These data management issues must be given the appropriate consideration especially during this critical time when Siemens wants to come up with a new BI solution which will help to improve its performance and reduce the cases of train failures and other problems reported in the train industry. In the process of delivering the new BI solution, Siemens will have to do extensive research to gather all the relevant data and information which will help it to deliver and implement the new project successfully. For the company to handle the expected new huge volumes of data and information effectively, it will have to design other new and modern databases which are capable of handling and managing huge and complex volumes of data effectively. All the new data and information which will be used in the entire process of implementing and using the new BI solution will be stored in these new and modern databases which are suitable in handling different types of data (Coronel and Morris, 2016). The data and the information required in implementing the new BI solution will come from the relevant online and offline resources which contain the relevant data and the information. These online and offline resources will help in the research process where they will provide all the required information and data required by the database designers and the other IT experts who will be involved in the design and implementation of the new BI solution.
Data mining can be described as the process applied by organizations to help them in arranging and sorting out their large and complex data (Witten, Frank, Hall, and Pal, 2016). Data mining helps the organizations to examine, analyze, and sort out the huge and bulky data in their databases, and this examination, analysis, and sorting consequently help the organization to make good and effective use of the data (Larose, 2015). It’s good to note that the data and information gathered and stored in the databases of organizations are of different and varying uses, and that’s why organizations undertake the process of data mining to help them in sorting and categorizing their data and information. This sorting and categorization help the organizations to make the best use of all the data and information in their databases (Montgomery and Purnell, 2018). Data mining can be used by Siemens to help in sorting out its bulky and complex data and information, and thus make the best use of all its data and information. Application of data mining in its operations will help to improve the overall management of its data which consequently helps to improve its overall performance. This has a positive impact on Siemens and the train industry as Siemens works closely with the train industry to help in reducing the cases of train failures and other train problems.
Data visualization is a broad term which encompasses different techniques which are used to improve the presentation and the readability of data (Ward, Grinstein, and Keim, 2015). Data visualization helps data analysts in converting data and information from one form to another, and this conversion helps in the presentation of data in the best and the most suitable forms required in different areas. Data visualization would play a very important role in Siemens as it would help the designers and the data analysts to present the data they collect and store in different forms of their choice. The data can then be used to prepare visual graphs or some other appropriate images which may be required by the organization. Graphical representation of data is very important in organizations as it helps the organizations the organizations to come up with the appropriate graphs and other visual images which can help to show the trends of the performance of the organizations. Therefore, every organization requires to use the appropriate data visualization techniques which can help in tabulation and presentation of data in graphical formats or other appropriate formats which can enhance the readability and the usability of the data. Applying the appropriate data visualization techniques in Siemens will greatly enhance the process of tabulation and presentation of data, and thus allow the company to understand its performance trends and thus make the necessary improvements where needed (Saket, Srinivasan, Ragan, and Endert, 2018, pp.1316-1330).
The process of delivering BI solution can be very effective and successful if done appropriately. If we assume that Siemens currently uses Oracle as its database and uses SAP to manage its corporate systems, and all the staff members have reasonable access to various computing resources; the introduction of the BI solution will not overload the servers of the company, and thus will work appropriately to improve the performance of the company.
There are different BI options which exist in the market today. Some of the most important BI options which can work well with Siemens to reduce the cases of train failures and problems include reporting and querying software which consist of applications which can be used in the extraction and presentation of data, online analytical processing (OLAP) software which can be used in the processing of the data and information used by the organization, among other options.
The way data is owned, used, and shared in Siemens can be changed effectively to enhance data security and data management process in the organization. The role of data management and protection is mainly assigned to the database administrator and the one who can facilitate the process of changing the data ownership, usage, and sharing in the organization. This process of changing should only be done when necessary, and should not violate the security and the privacy of the organization’s data.
Changing the organizational culture in relation to IT services should also be done when necessary. This change should be implemented by the IT experts of the organization who are always assigned the role of managing and controlling all the IT services of the organization. The change, should, however, not violate the security and the privacy of the data.
BI management should be left to the qualified IT experts of the organization. These experts have a good understanding of the BI framework which is a framework which helps in the identification and maintenance of the BI strategy. These IT experts are responsible for managing the business intelligence techniques applied in the organization to make sure these techniques are helpful to the organization and work to realize the desired targets. To achieve the best results from BI management, Siemens should make sure it gives out the role of BI management to the most qualified IT experts who will work to achieve all the set targets.
Boardman, A.E., Greenberg, D.H., Vining, A.R. and Weimer, D.L., 2017. Cost-benefit analysis: concepts and practice. Cambridge: Cambridge University Press.
Coronel, C. and Morris, S., 2016. Database systems: design, implementation, & management. Boston: Cengage Learning.
Haimes, Y.Y., 2015. Risk modelling, assessment, and management. New Jersey: John Wiley & Sons.
Larose, D.T., 2015. Data mining and predictive analytics. New Jersey: John Wiley & Sons.
Montgomery, C.S. and Purnell, S.K., 2018. Customizable data aggregating, data sorting, and data transformation system. U.S. Patent 9,985,609.
Olszak, C.M., 2016. Toward better understanding and use of Business Intelligence in organizations. Information Systems Management, 33(2), pp.105-123.
Patassini, D., 2017. Beyond benefit cost analysis: accounting for non-market values in planning evaluation. London: Routledge.
Richards, G., Yeoh, W., Chong, A.Y.L. and Popovi?, A., 2019. Business intelligence effectiveness and corporate performance management: an empirical analysis. Journal of Computer Information Systems, 59(2), pp.188-196.
Saket, B., Srinivasan, A., Ragan, E.D. and Endert, A., 2018. Evaluating interactive graphical encodings for data visualization. IEEE transactions on visualization and computer graphics, 24(3), pp.1316-1330.
Thamir, A. and Poulis, E., 2015. Business intelligence capabilities and implementation strategies. International Journal of Global Business, 8(1), pp.34-37.
Tian, J., Chin, A. and Karg, M., 2016. Digital services in the automotive industry. IT Professional, 18(5), pp.4-6.
Walker, G.H. and Stanton, N.A., 2017. Human factors in automotive engineering and technology. Florida: CRC Press.
Ward, M.O., Grinstein, G. and Keim, D., 2015. Interactive data visualization: foundations, techniques, and applications. AK Peters/CRC Press.
Witten, I.H., Frank, E., Hall, M.A. and Pal, C.J., 2016. Data Mining: Practical machine learning tools and techniques. Massachusetts: Morgan Kaufmann.
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