WK 8 Peer Review Response

            As humanity’s use of technology continues to increase and serve as a critical component in many organizations and businesses, the collection of large amounts of potentially sensitive information continues to grow. What are some benefits to collecting such a large amount of information, and how do different enterprises find value in different data sets? Organizations must establish a secure database management system (DBMS) and value proposition to retain and utilize this data.

There are several database management techniques, as will be discussed in this review, to manage and store large amounts of data. As stated in an article addressing big data adoption, “data storage and the management of large volumes of data also challenge traditional statistical and algorithmic methods, thus pushing companies to create innovative techniques to harness the data” (Gong, 2024). This article goes further to describe the largest push for big data in the construction industry being technology advancement, competitiveness, and government plan and policy initiatives. Challenges include insufficient systems to support big data adoption, data collection methods, and lack of experienced staff in this career path, with approach strategies to address these challenges being clear organizational structure, government incentives, and training. In addition to the construction sector, social media organizations also find Big Data valuable as it assists to build revenue through the use of targeted advertising. Understanding consumer preferences and market research is key to developing these targeted ads in social media.

            Organizations such as social media companies collect large amounts of information through daily operations, and have recently prioritized the efforts to collect, consolidate, and analyze this information to enhance market research, improve profits, and understand consumer behaviors. Big Data presents the opportunity for these companies to make more informed decisions to positively impact their businesses. The information they collect not only benefits product and sales improvements, but also draws concern over the amount of sensitive, private information collected and maintained outside of the individual’s control or complete awareness, aside from what is often offered in “the small print”. As stated in an article by Gibbs, “the biggest barrier for taking advantage of Big Data is privacy concerns, according to 47 percent of federal IT officials. Officials believe the challenge will be explaining that Big Data analytics is not equivalent to ‘Big Brother.’” (Gibbs, 2013). In addition to privacy concerns, organizations that research Big Data struggle with data quality, scalability, data integration, governance and compliance, energy consumption, data validation, staff experience, and algorithm optimization. Additionally, a problem may exist where researchers “confuse (or mixing) the theory (of Big Data) with the practice or use of it” (Tosi, 2024).

            The purpose of this research is to bring awareness to the processes associated with Big Data and the associated security and privacy concerns through a qualitative review of case studies, government publications, industry standards, and peer reviewed references.

Protecting and properly securing data may not always be an organization’s primary priority in the pursuit of increased profits. Activities associated with Big Data involve a massive volume of data and it is important to understand the implications and regulations that govern the handling of personal information.

            This research discusses Big Data processes, the strategies involved to analyze and realize the value of this data, and concerns with data security and privacy. To bring awareness to several facets of this concept, the following questions drive the discussion. What are the technical options for Big Data collection and handling? How does an organization identify the most impactful data points and build a story around those metrics? What are the requirements to ensure protections of personal information? What do the concepts of Confidentiality, Integrity, and Availability and National Institute of Standards and Technology (NIST) standards mean for Big Data? These questions highlight several topic areas that should be reviewed to better understand Big Data.

            Big Data researchers consist of professionals throughout various industries such as healthcare, finance, engineering, marketing, Internet of Things (IoT), telecommunications, and social media organizations. Effective businesses today rely heavily on internet usage and associated data analysis. Business and social media companies collect large amounts of information about their users to determine patterns, analyze consumer trends, and improve targeted ads. Users of these tools and websites should be made aware that their information is being collected and studied. They should be concerned with how this data is being protected and potentially shared or sold with other organizations with different intentions for that data. This paper reviews current literature on the topic with the intent to drive further thought and awareness around Big Data challenges, solutions, and security considerations.

            Availability – Ensuring timely and reliable access to and use of information (CSRC NIST, 2024).

            Big Data - an unprecedented collection of data that is enormous in volume, continually growing at an exponential rate, and extremely diverse in its nature (APU, 2024).

Confidentiality – Preserving authorized restrictions on information access and disclosure, including means for protecting personal privacy and proprietary information (CSRC NIST, 2024).

Graphical User Interface - a software interface designed to standardize and simplify the use of computer programs, as by using a mouse to manipulate text and images on a display screen featuring icons, windows, and menus (Dictionary.com, 2024).

            Integrity – Guarding against improper information modification or destruction, and includes ensuring information non-repudiation and authenticity (CSRC NIST, 2024).

Internet of Things (IoT) - The interconnection of electronic devices embedded in everyday or specialized objects, enabling them to sense, collect, process, and transmit data. IoT devices include wearable fitness trackers, “smart” appliances, home automation devices, wireless health devices, and cars—among many others (CSRC NIST, 2024).

Multiple academic sources were studied in order to facilitate this discussion on Big Data. The literature highlights Big Data research in construction, real estate, retail, and primarily social media. Questions were stated as a means to inspire thought to several topic areas, with the first few questions asking about the processes associated with Big Data handling. It also asks how value can be identified in the collection of Big Data.

   Social media has become a standard communication, sales, and information gathering tool in today’s environment. On the backend of these social media applications is a complex system of interconnected database structures consisting of specific language that facilitates user queries and results accessible through a graphical user interface (GUI).

Social media Big Data analytics relies on different types of DBMS in order to manage high volume, velocity, and variety of data types. The systems used may fall into one a category to include Relational Database Management Systems (RDBMS) which are often used to maintain structured data such as user profiles, transactional data, or metadata. Introduced in the early 1980s, RDBMS continued to enhance their performance and according to Elmasri, “relational databases became the dominant type of database system for traditional database applications. Relational databases now exist on almost all types of computers, from small personal computers to large servers” (Elmasri, 2016). Social media may also use NoSQL Databases for unstructured or semi-structured data to leverage scalability and flexibility, Time-Series Databases to track trends such as post engagement, likes, and shares, Distributed File Systems to maintain high volumes of raw unstructured data, Columnar Databases to complete fast querying of datasets, or a combination; a Hybrid DBMS solution to combine features of relational and NoSQL databases to potential manage both structured and unstructured data sets within on system such as metadata and more user accessible content. This Big Data captured through social media content is typically stored in Data Warehouses and Data Lakes in either raw or processed formats. These companies marry the use of these DMBS solutions with Search Engines and Text Analysis Tools for text indexing and social media post analysis, Streaming Data Platforms for real-time processing of posts and live streams, and Graph Processing Systems to provide insights into networks connections and behavioral influences.

In an article in the Journal of Global Information Management, a theory is posed that social media analytics directly relate with supply chain demand forecasting and product demand predictions. The research studies how Big Data analysis associated with clothing and apparel in the retail industry has the potential to improve marketing and sales modeling which would address short product life cycles. The study concludes that integrating social media analytics and Big Data analysis into consumer demand predictions improves accuracy, reduces costs, and facilitates supply chain optimization, increasing rotation of retail items on store shelves.

As discussed above, there are several benefits and DBMS options for an organization to collect large amounts of data for analysis. While this activity has been described as beneficial, there are risks associated with the retention of Big Data which prompt government involvement through policy requirements. Government organizations have established specific policy requirements to require specific handling procedures to secure sensitive data types. There are several international privacy laws to govern information security to include those metrics collected in Big Data, such as the General Data Protection Regulation (GDPR) within the European Union. The GDPR requires the collecting organization to obtain explicit consent to all them to collect personal data, to minimize and anonymize the data as much as feasibly possible, and imposes penalties for data breaches and non-compliance with these requirements. The Data Protection Act aligns with the GDPR, but has been tailored for use in the United Kingdom. Similarly in the United States, the California Consumer Privacy Act (CCPA) allows California residents the right to know, access, and delete personal data and requires the collecting agency to offer opt-outs from marketing campaigns. In Canada, the Personal Information Protection and Electronic Documents Act (PIPEDA) informs organizations how they must handle personal information in business related activities.

While these regulations govern the use and handling of private information, there are also standards, best practices, and government-imposed security regulations such as the Health Insurance Portability and Accountability Act (HIPAA) to restrict the use of protected health information, Federal Information Security Management Act (FISMA) to identify appropriate security control parameters to protect information, and ISO/IEC 27001 to standardize information security systems. Other regulations include the Genetic Information Nondiscrimination Act (GINA) to protect the privacy of genetic information, the Family Education Rights and Privacy Act (FERPA) to govern the use of educational information, the Children’s Online Privacy Protection Act (COPPA) to direct children’s privacy, the Graham-Leach-Bliley Act (GLBA) and the Fair Credit Reporting Act (FCRA) for financial privacy, and the Privacy Act of 1974 to require protections of information used in the US Government. These policies were influenced by the Fair Information Practice Principles (FIPPs); “a framework of privacy governance principles that became the global touchstone of privacy regulation” (Barrett, 2018).

            The last research question asks about industry standards and the CIA triad in order to prompt additional security considerations for protecting sensitive information collected in Big Data.

The NIST Framework is an internationally recognized set of standards and best practices for information security. The framework lists five functions that, when implemented together, facilitate a comprehensive analysis of an organizations cyber risk management program. The five functions are IDENTIFY which refers to using tools to bring awareness to all IT assets in the enterprise, PROTECT which refers to the tools to protect systems and components, DETECT which refers to the methods to monitor cyber events and adverse IT activities, RESPOND which refers to tools to flag incidents direct activities to address issues, and RECOVER which refers to the process to return to normal functions and operations after an attack. These functions must be applied to an organization’s information security program especially if they are handling sensitive information such as personal data that is often collected during Big Data analysis.  

Confidentiality, Integrity, and Availability of information is critical in data security. The CIA Triad was intended to articulate the pillars of data protection. Confidentiality refers to “preserving authorized restrictions on information access and disclosure, including means for protecting personal privacy and proprietary information” (CSRC NIST, 2024). Integrity refers to “guarding against improper information modification or destruction, and includes ensuring information non-repudiation and authenticity” (CSRC NIST, 2024) and Availability refers to “ensuring timely and reliable access to and use of information” (CSRC NIST, 2024). These concepts are important to Big Data research as they encourage data security and accessibility considerations. “The CIA triad has not only shaped and informed the theoretical understanding of information security, but also the very practices through which security is developed and implemented in organizations” (Hiza, 2022).

            In summary, this discussion draws attention to the value Big Data research, managed through the use of DBMS, can bring to industries to improve operations, understand consumer behaviors, enhance market research, and drive profitability. There are several challenges to Big Data research which can be addressed through the use of DBMS tools and practices. Although “good for business”, there are concerns with data privacy, governance, and protections of personal information which have been addressed through government policy, international standards, and information security best practices. Government regulations such as GDPR, HIPAA, and FISMA, alongside the CIA Triad and frameworks developed by NIST, aim to ensure data protection and accountability.

Big Data research remains dynamic, flexible, and leverages evolving technologies to address various challenges. Future efforts in Big Data application should focus on enhancing efficiencies, ethical and security considerations, utilizing artificial intelligence (AI) to improve data analysis, and integration into advanced DBMS and user applications. The research highlights the importance of balancing technological advancement through Big Data research with the ethical handling of sensitive information, advocating for strategies to address privacy concerns and compliance with policy and industry best practices. This study provides insights into Big Data challenges, security measures, policy requirements, and evolving technologies driving this complex field

Alashoor, T., Han, S., & Joseph, R. C. (2017). Familiarity with Big Data, Privacy Concerns, and Self-disclosure Accuracy in Social Networking Websites: An APCO Model.  Communications of the Association for Information Systems, 41, 4. https://doi.org/10.17705/1CAIS.04104

Barrett, L. (2018). Model(ing) Privacy: Empirical Approaches to Privacy Law & Governance. Santa Clara High Technology Law Journal, 35(1), 1-64. http://ezproxy.apus.edu/login?qurl=https%3A%2F%2Fwww.proquest.com%2Fscholarly-journals%2Fmodel-ing-privacy-empirical-approaches-law-amp%2Fdocview%2F2135080066%2Fse-2%3Faccountid%3D8289

Buresh, D. L. (2021). SHOULD PERSONAL INFORMATION AND BIOMETRIC DATA BE PROTECTED UNDER A COMPREHENSIVE FEDERAL PRIVACY STATUTE THAT USES THE CALIFORNIA CONSUMER PRIVACY ACT AND THE ILLINOIS BIOMETRIC INFORMATION PRIVACY ACT AS MODEL LAWS? Santa Clara High Technology Law Journal, 38(1), 39-93. http://ezproxy.apus.edu/login?qurl=https%3A%2F%2Fwww.proquest.com%2Fscholarly-journals%2Fshould-personal-information-biometric-data-be%2Fdocview%2F2587190170%2Fse-2%3Faccountid%3D8289

Dictionary.com. (n.d.). Graphical user interface definition & meaning. Dictionary.com. https://www.dictionary.com/browse/graphical-user-interface

Elmasri, R., & Navathe, S. (2016). Fundamentals of Database Systems. Pearson.

Friedman, S. (2024). CISA publishes update to continuous diagnostics and mitigation model incorporating additional FISMA metrics. Inside Cybersecurity, http://ezproxy.apus.edu/login?qurl=https%3A%2F%2Fwww.proquest.com%2Ftrade-journals%2Fcisa-publishes-update-continuous-diagnostics%2Fdocview%2F3142914620%2Fse-2%3Faccountid%3D8289

Gao, Y., Wang, J., Li, Z., & Peng, Z. (2023). The Social Media Big Data Analysis for Demand Forecasting in the Context of Globalization: Development and Case Implementation of Innovative Frameworks. Journal of Organizational and End User Computing, 35(3), 1-15. https://doi.org/10.4018/JOEUC.325217

Gibbs, M. (2013). Big Data, Big Business, Big Government, Bigger Brother: Big Data will change business and government and if CISPA is made law, Bigger Brother's will be right around the corner. Network World (Online), http://ezproxy.apus.edu/login?qurl=https%3A%2F%2Fwww.proquest.com%2Ftrade-journals%2Fbig-data-business-government-bigger-brother%2Fdocview%2F1312292248%2Fse-2%3Faccountid%3D8289

Gong, D., Zhao, X., & Bohan, Y. (2024). Big Data Adoption in the Chinese Construction Industry: Status Quo, Drivers, Challenges, and Strategies. Buildings, 14(7), 1891. https://doi.org/10.3390/buildings14071891

HIZA, D. (2022). Assessing the Significance of Cia Triad Security Model in Establishing ICT Security Controls in The Public Sector (Doctoral dissertation, Institute of Accountancy Arusha (IAA)).