Privacy and Ethical Considerations in Information Management
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Information management is a critical facet in organizations. As firms continue to adopt various forms of data and information technologies, the size and complexity continue to grow. As such, organizations have to establish mechanisms that ensure the security, reliability, and sustainability of their systems. Further, firms are faced with various ethical and privacy issues that they have to address to safeguard the interest of their stakeholders, those of the organization, and meet various compliance regulations (Pearlson, Saunders and Galletta, 2020). However, for an organization to meet these requirements, they must have a basic understanding of their IT systems architecture, data systems, and use as well as the various sustainability options available. This paper aims to shed light on this matter by looking at data warehouse architecture, big data, and green IT.
Data warehouse Architecture
Data warehouse architecture is an organization’s central information repository system where every data stream within the organization is directed to, cleaned, and transformed into storage formats that provide information for various queries launched. As Pearlson, Saunders, Galletta (2020) indicate, these systems are instrumental in organizational operations since they serve as their repositories for analysis, reporting, decision making, and forecasting.
Arif and Mujtaba (2015) assert that, as a management technique, data warehouses enable organizations to collect information on the business from single or multiple sources. However, for these systems to be operational, specific requirements and components are necessary for successful architectural development and governance. These include the identification of potential users, the definition of various security requirements, the required skills, and so forth.
Data warehouse architecture has various formats, namely single-tier, two-tier together with three-tier. However, the three-tier form is the one that business organizations usually adopt for their operations (Arif, and Mujtaba, 2015). The top tier represents the portion that is used by the clients who seek to interact with the organization. This layer is known as the client and has the query tools, reporting tools, data mining tools, analysis, and so forth. The middle layer, on the other hand, is the mediator between the end-users and the database. It is known as the OLAP server and provides an abstracted view of the database. Finally, the bottom layer is where the back tools operate and are used to host cleaned data that has been transformed and loaded. This tier serves as the database as it stores the data (Ashok, 2006). It works like a relational database system bringing together data that has been mined from various sources.
The architecture of the data warehouse requires that a business brings together these components to yield the desired results. The architecture is, however, subdivided with two portions representing the back room and front room. The backroom represents the stage where data is gathered (operational Source System) and taken to the staging area. While the sources could be multiple, there is a single staging area where the data is worked on. At the staging area, the data is extracted from diverse sources; it is transformed yielding useful information, cleaned and loaded to the data marts or warehouse. In this section, data is converted to ensure that it meets the standards for a data warehouse. These include summarizing, making it anonymous, eliminating duplications, filling in the missing data, and so forth. ETL tools are used to carry out these activities.
The third section of the backroom is data representation, where big historical data that can be used in the process of decision making is stored. This data is then moved to the middle tier which is the OLAP server,
Data warehouse architecture front room is generally the top tier or client tier where various tools of accessing data are available such as query, data mining, reporting, and analysis. Figure 1 represents these components of data warehouse architecture
Figure1. Data warehouse architecture
An assessment of trends in data warehouse architecture shows that several emerging trends are making their way in the industry. These include the adoption of cloud services and having data managed by a third party. These vendors are transforming the operations of an organization based on their ability to offer efficiency, reliability, scalability, as well as security in data warehouse architecture (Fernando, 2018).
The industry is also marked with rising trends in the adoption of various technologies in data warehouse architecture such as AI, machine learning, and cloud-based architecture, which is offering organization grander scale, flexibility, as well as speed. The use of a client system that is full of management is also transforming operations. Examples include Azure Data Factory, which is bringing a whole new way of using data warehouses and utilizing all the benefits it has to offer.
Big Data
Recent decades have been marked with the rapid growth of information beyond the capacity that available computer technologies could handle (Khan, Yaqoob, Hashem, Inayat, Ali, Alam, Shiraz .and Gani, 2014). It is this explosion that gave rise to the concept of big data since the available information is so vast that it cannot be processed using the methods and technologies that were available.
As such, big data is a concept that seeks to describe these limitless volumes of data that have been continuously growing from different fields, namely society, science, business, and so forth. As such, to deal with these complex bodies of data, various tools have been developed to help manage and analyze it to yield insightful information. Through big data, organizations can bring together that data is unstructured, semi-structured as well as fully structured. This data is used by organizations to mine insightful information through the applications of advanced analytics, machine learning, and engage in predictive modeling (Cody, 2014).
Big data has forever transformed the operation of various industries from commercial business operations, healthcare, and even public service delivery. In commercial transactions, big data has been exploited to yield informative insight regarding the customers of an organization, their preferences as well as purchase behavior. Through advanced data analytics, an organization can obtain historical trends of their customers as well as their real-time data to better understand their customers. This information has been used to inform the business strategies of the organization as well as their marketing campaigns, yielding more returns on their investments. For example, the Amazon Company can make use of its big data to understand its customer preferences and ensure that they achieve more conversions in their operation. In essence, the data has enabled companies to focus more closely on their customers and their needs (Alam, Sajid, Talib, and Niaz. 2014).
While big data offers numerous benefits to organizations, it also comes with various demands that have to be carefully navigated. Khan et al. (2014) assert that big data has generated new challenges and issues in the area of analysis and management that companies have to deal with. For example, companies have to contend with the massive volume of data and adopt technologies that can support it. They also have to deal with the velocity, variety, and complexity as well as information value. Further, organizations face the demand for security and management of this vast information, all of which come with severe technical issues. Organizations, therefore, have to develop policies to deal with these demands as well as have the right personnel.
Green IT
As technological advances continue to increase, organizations find themselves in the various dilemma of deciding the path to take. Pearlson, Saunders Galletta (2020), however, indicates that every organization must ensure that they engage in responsible computing. Organizational managers should, therefore, ensure that they assess their IT initiatives to ensure that they adhere to various ethical issues. One of the ways that an organization can practice responsible computing is through the adoption of green IT
Green IT is a practice that is adopted by organizations to ensure that they support environmental sustainability in their computing operations. As such, the goal of green IT is to reduce the adverse effects that the use of computing systems has on the environment. Loeser (2013) asserts that green IT prompts firms to minimize the emissions and waste products, to practice responsible stewardship with their products, and to ensure their optimal use as well as ensuring that their developments are sustainable through a reduction of their footprint.
Some of the strategies that have been adopted to ensure that data centers are green include the implementation of power management strategies, eliminating redundant servers and adopting virtualization, alternative energy sources as well as recycling (Nanath and Pillai, 2014).
Various organizations have taken the step to implement green IT in their computing and achieved some great results. An example of such an organization is the Microsoft Company. In an attempt to achieve greater environmental sustainability and the company has sought to move its data center undersea. This strategy is aimed at boosting resource utilization, ecological sustainability, and lowering cost.
One of the significant challenges that affect data centers is the massive amount of heat generated. As such, these areas can spend extensive amounts of resources in cooling the plant. To deal with this challenge, the company has come up with a creative strategy to regulate the temperature through undersea location ( https://natick.azurewebsites.net/)
Conclusion
A good comprehension of the architecture of a data warehouse is critical in helping the organization implement all the necessary security procedures. It ensures that organizations can adhere to the various regulatory compliance as well as ethical issues.
On the other hand, a good understanding of big data and the possibilities it offers an organization is critical in ensuring that full exploitation and ethical compliance. Finally, green IT enables companies to ensure that their operations make effective use of the available resources and are sustainable. A mastery of data warehouse architecture components, big data, and green IT is critical in informing ethical and privacy considerations for purposes of information management.
References
Alam, J. R., Sajid, A., Talib, R., and Niaz. M. (2014). A Review on the Role of Big Data in Business, International Journal of Computer Science and Mobile computing Monthly Journal of Computer Science and Information TechnologyISSN 2320–088XIJCSMC, Vol. 3, Issue. 4, April 2014, pg.446–453 Retrieved from https://www.ijcsmc.com/docs/papers/April2014/V3I4201480.pdf
Arif, M., and Mujtaba, G. (2015).A Survey: Data Warehouse Architecture International Journal of Hybrid Information TechnologyVol.8, No. 5 (2015), pp. 349-356http://dx.doi.org/10.14257/ijhit.2015.8.5.37ISSN: 1738-9968. Retrieved from https://pdfs.semanticscholar.org/bc2f/2a5cc55fb1c4555e31b42aaabf540c4e8451.pdf
Ashok, N. (2006) Pick Your Data Warehouse Architecture. DM Review. Oct2006, Vol. 16, Issue 10, p20-22. 3p. Retrieved from http://eds.b.ebscohost.com/eds/detail/detail?vid=0&sid=de9e7f83-c617-4fe4-831d-f9d19fe18352%40sessionmgr101&bdata=JkF1dGhUeXBlPXNoaWImc2l0ZT1lZHMtbGl2ZQ%3d%3d#AN=23507981&db=buh
Cody, A. (2014). Big Data: An Exploration of Opportunities, Values, and Privacy Issues, New York: Nova Science Publishers, Inc. 2014. Retrieved from http://eds.a.ebscohost.com/eds/detail/detail?vid=2&sid=f5559a09-30f5-4ae5-bf1a-af75f46f0f09%40sessionmgr4008&bdata=JkF1dGhUeXBlPXNoaWImc2l0ZT1lZHMtbGl2ZQ%3d%3d#AN=811106&db=nlebk
Fernando, A. (2018). Emerging Trends in Data Warehousing and Analytics in Cloud. Retrieved from https://dzone.com/articles/emerging-trends-in-data-warehousing-and-analytics
Khan, N., Yaqoob, I., Hashem, I, A, T., Inayat, Z. Ali, W. K., M., Alam, M., Shiraz, M.and Gani, A.(2014). Big Data: Survey, Technologies, Opportunities, and Challenges. Volume 2014 |Article ID 712826 | 18 pages | https://doi.org/10.1155/2014/712826 Retrieved from https://www.hindawi.com/journals/tswj/2014/712826/
Loeser, F. (2013). Green IT and Green IS: Definition of Constructs and Overview of Current Practices 19th Americas Conference on Information Systems (AMCIS), At Chicago, IL Retrieved from https://www.researchgate.net/publication/267737651_Green_IT_and_Green_IS_Definition_of_Constructs_and_Overview_of_Current_Practices_Completed_Research_Paper
Nanath, K., and Pillai, P. (2014). Green Information Technology: Literature Review and Research Domains. Retrieved from https://www.researchgate.net/publication/262066244_Green_Information_Technology_Literature_Review_and_Research_Domains
Pearlson, K., Saunders, C., Galletta, D. (2020). Managing and Using Information Systems: A Strategic Approach, 7th Edition. Hoboken, NJ: John Wiley & Sons, Inc. ISBN: 978-1119560562