Utilizing Big Data Analysis Technology for Digital Data Management: A Case Study of Total Parking So

DIGITAL TECHNOLOGY DIGITAL TECHNOLOGY Name of the Student Name of the University Author Note 1 DIGITAL TECHNOLOGY Executive Summary: Total Parking Solutions Inc. is alocally operating automated and semi-automated system for the building that facilitates parking solutions. The company faces various issues, from storing various types of data in various locations that cannot be accessed remotely. Thus, itrequires a solution for managing digital data using digital technology. The digital technology used for the system is the Big Data analysis technology used for categorizing and saving the data in a centralized location. The paper analyses the opportunities and threats impacting the implementation and integration of the technology in the company. The best tool is selected for the AWS big data analysis services, with its features described that makes the tool unique and benefit the company. 2 DIGITAL TECHNOLOGY Table of Contents Introduction: .............................................................................................................................. 3 Digital technology: .................................................................................................................... 3 Business threats: ........................................................................................................................ 4 The best tool and its features: .................................................................................................... 5 Conclusion: ................................................................................................................................ 6 References: ................................................................................................................................ 7 3 DIGITAL TECHNOLOGY Introduction: Total Parking solution Inc. is alocal automated and semi-automated building system facilitating parking solutions. The company is currently facing challenges from the traditional data storage functionality and unavailability of remote access to data. Thus, the company has thought about implementing adigital technology best fit for its data management capabilities. The client's opportunities from the new technology are discussed, along with the business barriers that exist in the company for applying the technology. The best tool for solving the situation for the company is discussed, along with the features that make itunique. Digital technology: The organization has been facing awide range of problems from data management. The company has been traditional in their approach to data management, keeping data saved over the hard drives, google drives and even soft copies and hard copies, making it impossible to find the required data on time. The company aims to utilize their big data and manage their scattered data locally and remotely in acentralized location to be accessed whenever needed by authorized personnel. The best technology that the company can utilize is Big Data Analysis technology. The Big Data is the combination of the semistructured, unstructured and structured data by the organization collected through active mining of information used for machine learning, artificial intelligence, predictive modelling and other advanced ways of analysis (Salkuti, 2020). The systems that are used for storing the big data proposing acommon architecture for data management combining tools for supporting data analysis. The big data can be described as large volume, large variety, and generation, collection and processing data with velocity. The big data analysis technology would use the advanced techniques for analysis, managing large datasets of different sizes and structures. The benefits of the big data analysis on the organization can be better and faster decision making accessing large data volume analysing the sources gaining new insights on handling 4 DIGITAL TECHNOLOGY historic records in real-time (Ullah, Awan & Sikander Hayat Khiyal, 2018) .The big data to be analysed through the new technology would reduce the cost of flexibly processing and storing data analysing large data amounts. Discover the insights and patterns on the data identification and management to operate more efficiently. The data can be collected from the traditional data from the legacy systems and latest technologies like the sensors, videos, devices empowering social media and web services empowering organization to be more data-driven (Islam & Reza, 2019). The big data analyses, prioritizes and mitigates customer risks and aims for creating new products and the services. Business threats: The company has diverse types of data that is needed to be managed. The file includes video, images, chats and emails, client information, product data sheet, project information, notes and schedules and marketing data. The data is distributed among various platforms and locations like the hard copies on paper, hard drives, WhatsApp and telegram. The data storage in the Google cloud and the computer hard drive has been exhausted, containing outdated, duplicated and redundant data. The data sheets can be misleading relating to the different subjects, like relating to the project and the product at asimilar time. These were duplicated in different sub folders and within other folders; thus, there is adire need for data cleansing to ease data access in the time of need. The data to be managed would require to be perfectly accessed by the different levels of users in the organization with different levels of access and access time. The data has been poorly tagged or labelled due to the various locations where itcan be stored. The present function for searching the data doesn ’tshow any relevant content searched. The data must be classified into raw or user data, information and knowledge. The employees must understand that they can gather the required data without any help from the senior professionals anytime, anywhere. 5 DIGITAL TECHNOLOGY The best tool and its features: The best tool that the organization can utilize for appropriate data management would be Amazon Web Services which is a broadly adopted cloud platform offering over two hundred fully featured data services helping millions of customers (Navale & Bourne, 2019) . The AWS allows lower organisational costs to improve data infrastructure, be more flexible and agile, and support faster innovation (Gon çalves et al., 2022) .The AWS is the largest cloud platform with the most diverse range of services for computing, database and storage, providing infrastructure, allowing emerging technologies like artificial intelligence and machine learning, big data analysis and data lakes. The tool facilitates afaster, cost-effective, and easier approach for moving the organisation's existing data to the cloud, keeping the imagination open. The cloud service is secure and robust satisfying security requirements. The AWS allows computational power to research, develop and innovate new technologies faster with services like serverless computing. AWS has services to cover the big data barriers seen in the company. The Amazon Web Services provides facilities like the Data Lakes and Analytics along with services like Amazon Glue, Amazon Machine Learning, Amazon Redshift, Amazon Kinetics and Amazon QuickSight. The Amazon Data Lake and Analysis allows analytical services that fit big data analysis in the organization reinventing businesses with the available data. The big data can operate in scalable data lakes, built on the purpose of increasing performance and reducing costs. The serverless option can be seen in AWS Glue being easy to use, unifying data security, access and governance with integration of Machine learning. The other big data in various locations, can be utilized by the Amazon Data Exchange to make extensive data catalogues and streamline the data procurement, governance and management with ease of using data files and tables ("AWS Data Exchange", 2022) .Services like Amazon S3 allow data availability, security, and scalability to store any data amount, organise them and fine-tune access controls to meet the specific compliance and 6 DIGITAL TECHNOLOGY organizational requirements ("Cloud Object Storage –Amazon S3 –Amazon Web Services", 2022) .The AWS Glue Databrew facilitates data cleansing to cleanse and deduplicate data with built-in technologies for machine learning ("AWS Glue Features |Serverless Data Integration Service |Amazon Web Services", 2022) .The AWS Glue allows for cleaning and preparing the big data for further data analysis. Conclusion: Total Parking Solutions Company want to utilize the raw data present in the organization to be managed successfully. The company is facing difficulties with its traditional data storage and management approach. The data is needed to be availed remotely to the business to be utilized for further analysis. The company thus should use Big Data Analysis to mitigate the current problems that the company is facing with multiple locations and types of data to be stored digitally. The paper discusses the business opportunities gained from the technology and the current organizational barrier to using the technology throughout the organization. The amazon web services are detected to be the best cloud service provider allowing the business to utilize its big data analysis services to mitigate the various issues in the organization. The organization must consider the scalability, availability and flexibility offered by the big data analysis and computing technology to the traditional approach. 7 DIGITAL TECHNOLOGY References: AWS Data Exchange .Amazon Web Services, Inc. (2022). Retrieved 13 May 2022, from https://aws.amazon.com/data-exchange/ . AWS Glue Features |Serverless Data Integration Service |Amazon Web Services .Amazon Web Services, Inc. (2022). Retrieved 13 May 2022, from https://aws.amazon.com/glue/features/ . Cloud Object Storage –Amazon S3 –Amazon Web Services .Amazon Web Services, Inc. (2022). Retrieved 13 May 2022, from https://aws.amazon.com/s3/ . Gon çalves, D., Bergquist, M., Al änge, S., & Bunk, R. (2022). How Digital Tools Align with Organizational Agility and Strengthen Digital Innovation in Automotive Startups. Procedia Computer Science ,196 ,107-116. https://doi.org/10.1016/j.procs.2021.11.079 Islam, M., & Reza, S. (2019). The rise of big data and cloud computing. Internet Things Cloud Comput ,7(2), 45. http://dx.doi.org/10.11648/j.iotcc.20190702.12 Navale, V., & Bourne, P. E. (2018). Cloud computing applications for biomedical science: A perspective. PLoS computational biology ,14 (6), e1006144. https://doi.org/10.1371/journal.pcbi.1006144 Salkuti, S. R. (2020). A survey of big data and machine learning. International Journal of Electrical & Computer Engineering (2088-8708) ,10 (1). https://doi.org/ 10.11591/ijece.v10i1.pp575-580 Ullah, S., Awan, M. D., & Sikander Hayat Khiyal, M. (2018). Big Data in cloud computing: a resource management perspective. Scientific Programming ,2018 . https://doi.org/10.1155/2018/5418679 8 DIGITAL TECHNOLOGY