Type of paper: Report

Topic: Information, Technology, Education, Database, Data Analysis, Innovation, Students, Project

Pages: 8

Words: 2200

Published: 2021/01/06

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Implementing Innovation In It Technologies And Customer Satisfaction

<Author Name>

Abstract

In this research paper, the attempt is made to determine as to how the innovation in information technology has succeeded in eliciting the consumer satisfaction. At times due to much hype about a certain innovation in information technology, the companies seem to race towards its adoption and implementation. There are lots of expectations from the new technology. However, the end results appear to have mixed response as the customers and stakeholders, at times get disappointment. This research aims at examining as to how well the new innovations in data-analytics and databases have been embraced by the world of information technology and how well the new technology has evoked the customer satisfaction. A research is conducted on this adoption and customer satisfaction, post implementation and the analyses and the discussion of results are shared in the relevant sections. Overall the results showed mixed response, with many of the stakeholders feel the return on the investment on the new innovations is not as expected.

Introduction

Off late one hears a lot on technological improvements and innovations in data analytics and database technologies. Everyone in the IT world seems to be so excited and talk about the BIG DATA Technologies. These innovations are presumed to be overtaking the conventional relational and other contemporary database technologies. Database Technologies itself have seen more than four decades of growth, advancement and evolution. These evolutions have basically been triggered by the scenarios and challenges posed by the software development industry and application developers to the database technologists. From the beginning as a basic file based database systems mainly handle by the languages such as COBOL, to the more advanced and stable relational technologies as provided by the database systems such as ORACLE, Information Technology has witnessed a sea of changes. With the growth of internet technologies and the need to make the database available from anywhere through internet, has off late posed new challenges. The database technologies have also geared up to meet the new challenges and offer new developments to support the internet based databases and column based databases. BIG DATA Technologies is the latest in this series of developments.

Objective of Research Project

The objective of research project is to determine whether the new technologies as embraced and implemented by the chose group are worth their time and investment. The research will be targeted to ascertain whether this new change has evoked a suitable satisfaction within the customers, or how well the customers are satisfied. An attempt will be made to determine the level of satisfaction by evaluating the given parameters which will come from the recommended benchmarks in evaluating success of information technology implementation projects.
The scope of this project includes the identification of a Banking Group which is known to use its databases extensively. Banks have experiencing data explosion as more and more businesses are embracing online banking transactions. This implies that every day the Banks have to deal with terabytes or even petabytes of data. Moreover, the banks are nowadays receiving all formats and varieties of data and the volume or velocity with which the data flows is sometimes tremendous. The Banks are therefore always in a lookout for improving their technology to support their business. Hence, in this research context Banks seem to be an ideal choice and the remaining part of the research will be conducted with Bank as the group in which the surveys and analysis will be directed.

This research is organized as a project with definite phases. Since this is a business research so there are several stages in this research which need to be planned along with the timelines. The research is more of exploratory in nature which involves lot of interviews and surveys to be conducted on the chosen research group members. As the group chosen is that of the bankers so it is assumed that it would be appropriate to direct the research process stages focusing on this group. The below figure represents an appropriate project plan, along with the Gantt Chart, illustrating the project stages and time lines.
Tasks and work to be performed

Resources

As it was highlighted that this project is a research project and the research is a sort of exploratory research which requires a lot of interviews to be conducted with the group participants. Hence, once the research questions are ready, the resources in the form of the group members would be required. These members would be required to answer a few questions as precisely as possible. The interviews will be organized through the proper communication plan and affirmation from the members. There will also be some survey conducted through the online participation of the members. Hence a website would be required where the interviews /survey questions could be hosted.
Expected Outcomes
The new technology is supposed to be implemented. The existing conventional databases and applications need to be migrated to Big Data Technologies. Hence, briefly, following outcomes will be expected from this implementation:
1. Successful migration to new innovative technology – BIG DATA.
2. Successful implementation of analytics which can help to smoothen the Banking operations, which include a lot of data analysis and reporting
3. Performance Improvement, which means that there is no more slowness experienced currently due to phenomenal increase of the data.
4. Ubiquity, which means that the analysis, dashboards and data accessibility is from anywhere, and not just limited to the office premises. This helps the Bank’s Managers to control the operations any time and not just limited to only the office hours
5. Ease of Use, which means that the client must be comfortable working on the new technology and the new implementation, is ergonomically designed.
There are several ways through which one can examine and rate the outcomes as observed from the new implementation. Best way as recommended by the researchers is by undertaking the through exploratory research and analyzes the response made by the participants from the group. (Saunders et. al., 2009).
Knowledge Management
It is expected that some prior knowledge of research process, database technologies, data analytics and project management is necessary. Although, as the author is still at the learning level, but, some knowledge was inculcated through the course studies as provided. This helped a lot in devising this project and also drawing the project plans, including the Gantt chart. Some help was also taken from the professors, online resources and the knowledge experts. Some online resources really helped a lot in gaining the appropriate knowledge and managing the knowledge to successfully completing the project. (Chandani & Neerja, 2007).
Work to be Performed
The work required to be performed was explicitly mentioned in the tasks. The tasks has be performed by the author along with the questionnaire designed as provided in the appendix section.
Literature Review on Database Evolution and New Trends
For any research, literature review is most critical part to ensure its fruitfulness and help achieve the research objects. In this paper also, a thorough research was conducted by following the research process. A recommended research process is as suggested by Saunders et. al. (Saunders et. al., 2008). The researchers suggest an upward spiraling research process as shown in the below diagram:
So the research process is pretty much iterative and goes on till the major length of the research stages. The literature review is started from the early stages of the development of the technology.
With the advent of internet mode of operations and the advancement of the business operations as supported by modern companies like Google, Facebook, Amazon and eBay, there is a sea of changes in the way business works today. Now every company has a social profile, internet transactions, social and business connects, online advertisements and transactions. Lot of unstructured database is being generated nowadays as one can observe that in a site like Facebook, various formats of data is created and shared (Kemper et. al., 2012). There are images, video clips, spread sheets, graphs, audio clips, messages and various other objects. Sometimes the size of these objects are huge and different type of objects are required to be linked with an Id such as the profile which creates such unstructured data.
The new trend is such that everyday huge data is being generated and the companies like Google and Facebook need to manage as far as 20 Petabytes of data per day!. This is actually a huge data size. The old relational technologies as provided by IBM/Microsoft may no longer be able to support this huge generation of very large data (Barber et.al., 2012). The requirements have posed new challenges to the database technologists. The new trends in the databases are related to the Big Data Technologies. Big Data – these are the new trends which come from the applications which require very huge data to be managed, as stated earlier. This huge data may go up to several petabytes of data per day. Many University Groups in America, Australia and other leading universities are undertaking strong research and development to support the Bigdata initiatives. The Universities are involving multi-disciplinary groups like statistical leads, computational experts, and scientific domain experts to enhance the new development. One such group involves the experts like David Dunson, Duke University; Rafael Irizarry, Harvard University; Hongkai Ji, Johns Hopkins University; Eric Laber, North Carolina State University; Jeffrey Leek, Johns Hopkins University; Tyler McCormick, University of Washington; Sherri Rose, Harvard University; Chad Schafer, Carnegie Mellon University; Mark van der Laan, University of California, Berkeley; Larry Wasserman, Carnegie Mellon University & Lingzhou Xue, Pennsylvania State University. (http://www.amstat.org/policy/pdfs/BigDataStatisticsJune2014.pdf) (Dunson et. al., 2014). Their main contribution is towards the development of visualization of data concepts and techniques. With huge data being generated in the applications today, even in the fields of bioinformatics, visualization of the data is necessary due to the sheer volume and unstructured nature.
Outcome and Results
The participants were identified from the group and the questionnaires were administered. There were totally 50 participants to the survey which were identified. The participants were communicated in advance and the questionnaires were given to them on time. Their responses were collected and analyzed. Following is the results of the responses:
Conclusion
It has been four decades and database technology is continuously evolving. Even at this stage, one cannot say that the database technologies have matured enough as when one studies the evolution, there has always been a sea of change at every new phase of development. New challenges trigger the new approach of handling databases. With the advent of internet based working and the new trends of cloud computing, there has been an immense shift in the way one looks at the databases. Today the companies talk about maintain petabytes of data per day, when just a decade ago, petabyte size of total database was just unheard of. Along with this, the database technologies also need to support the unstructured nature of new data being generated which can hardly fit into the relational technology. But, the new emerging Big Data technologies have not evoked a good satisfaction amongst the most important users. As one can still observe that the companies are still in the transition stage or continue to use the conventional database technologies, till the time Big Data matures enough to evoke a good response and customer satisfaction.
References
Abadi, P. Boncz, S. Harizopoulos, S. & Idreos S. Madden, S.(2012). The Design and Implementation of Modern Column-Oriented Database Systems. Foundations and Trends•R in Databases, vol. 5, no. 3, pp. 197–280.
Amazon. (n.d.). Getting Started with Amazon EC2 Windows Instances. Amazon.com.
Bigdataq.com. (2014). Big Data analytics. Bigdataq.com.
Barber, R. Bendel, P. Czech, M. Draese, O. Frederick, H Namik, H. Idreos, S. Min-Soo, K. Koeth, O. Jae-Gil, L. Tianchao, T. Guy, M. Konstantinos, M René M. Murthy, K. Pandis, I., Qiao, L. & Szabo, S. (2012.). Business Analytics in (a) Blink. IEEE Data Eng. Bull., 35(1):9–14,
Carter, B., & Levy, J. (2012). Facebook Marketing: Leveraging Facebook’s Features for your marketing campaigns. . Pearson Education.
Chandani, A., & Neerja, B. (2007). Knowledge management: an overview & its impact on software . IEEEXPlore.
Data&Society Research Institue. (2014). The Social, Cultural & Ethical Dimensions of “Big Data”. Data&Society.
DatabaseSkill. (2011). CGI ASP PHP JSP ASP.net Comparison.
Dunson, D. Irizarry, R. Hongkai, J. Laber, E. Leek, J. McCormick, Chad T. van der Laan, M. Wasserman, L. y & Lingzhou, X. (2014). Discovery with Data: Leveraging Statistics with Computer Science to Transform Science and Society 2014.
Franz F, May, N. Wolfgang L. Große, P. Müller, I. Rauhe, H. & Dees, J. (2012).The SAP HANA Database – An Architecture Overview. IEEE Data Eng. Bull., 35(1):28–33.
Hughes, T. (1987). The evolution of large technological systems. MIT Press, Cambridge MA, London, pp. 51–82.
Kemper, F. Neumann, T. Funke, F. Leis, V. & Mühe, H. HyPer. (2012). Adapting Columnar Main-Memory Data Management for Transactional AND Query Processing. IEEE Data Eng. Bull., 35(1):46–51.
Martina-Cezara A, Kemper, A. & Neumann, T. (2012). Massively Parallel Sort-Merge Joins in Main Memory Multi-Core Database Systems. Proceedings of the Very Large Data Bases Endowment (PVLDB), 5(10):1064–1075.
Oracle. (2009). The Benefits of Risk Assessment for Projects, Portfolios, and Businesses . Oracle White Paper.
PMP. (2014). Introducing Project Risk Management.
Per-Åke, L. Hanson, E. and Susan L. (2012). Price. Columnar Storage in SQL Server 2012. IEEE Data Eng. Bull., 35(1):15–20.
SAP. (2013). SAP Cloud Applications Studio – A powerful business tool for freedom, flexibility, and speed in the Cloud. SAP.com.
SAP Cloud Applications Studio – A powerful business tool for freedom, flexibility, and speed in the Cloud. SAP.
Saunders T., Lewis P. and Thornhill A. (2008) Research Methods for Business Students. 5th Ed, Pearson Education.
Appendix
CUSTOMER-SATISFACTION-SURVEY
Following are the questions on which the group participants response were elicited.
Section A
1. What is your age category:
16-25
26-40
41-55
56-65
66-75
2. Tick on the gender
Male
Female
3. What is the Sector in which the Bank Belongs?
Public
Private
Hybrid
4. Are you a foreigner?
Yes
no
Section B
5. Which BIG Data Technology you use:
Java/apache/Map reduce/Hadoop
IBM solution
Oracle’s Big Data suite
Microsoft Big Data Solutions
6. Please rate your satisfaction of this service on the following:
Very satisfied Satisfied Dissatisfied Very dissatisfied No opinion
Network availability
Application Availability
Performance
7. When you call to complain or query anything, how satisfied are you on the following:
Very satisfied Satisfied Dissatisfied Very dissatisfied No opinion
Overall customer
care service
Ability to get
support quickly
Attitude of the
Support Team
Ability to provide
a solution
8. , how satisfied are you with the use of this service
Very satisfied
Satisfied
Dissatisfied
Very dissatisfied
No opinion

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WePapers. (2021, January, 06) Project Plan Report Samples. Retrieved July 31, 2021, from https://www.wepapers.com/samples/project-plan-report-samples/
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Project Plan Report Samples. Free Essay Examples - WePapers.com. https://www.wepapers.com/samples/project-plan-report-samples/. Published Jan 06, 2021. Accessed July 31, 2021.
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