CSIA459 Wk 6

Technology Review #1: Emerging Application of Technology (Consumer or Business)

Kenneth Hansberry

CSIA 459 Evaluating Emerging Technologies

University of Maryland University College

Professor Dr. Johnny Bledsoe

April 9, 2017

Introduction

In this technology review paper, the discussion will be based on the emerging application of big data analytics technology and how it is suitable for future use in consumer-oriented products and services as well as the security of individual personal and organizational information. Big data analytics is an emerging application of technology that include processes for examining bulky and varied data sets in order to discover hidden patterns, unidentified correlations, market trends, consumer preferences, and other essential information that can support in making informed business decisions (Pyne Prakasa & Rao, 2016).

Big data analytics are driven by resourceful analytics systems and software to identify various business opportunities and threats. The most important issue in this paper is in regard to cybersecurity and how big data analytics can support security analytics. For instance, fraud detection is one of the most common application of emerging technologies such as big data analytics. In addition, security intelligence tools can be synced in real-time to extract any relevant information one need to convey relevant information. Discovering security threats and attacks patterns can help organizations to establish the sequence and impact as well as supporting the sharing of that information with other stakeholders in order to contain the attacks.

However, there are security implications that arise due to big data analytics and which include confidentiality, integrity, availability, and authentication. Confidentiality arise due to violation of privacy and sharing of personal details with other industry sectors as well as law enforcement. Privacy regulations prohibit access, storage and sharing of personal information without the consent of the owner. The developments and advancements in big data analytics has provided users with tools and techniques to extract and correlate data thus, making privacy violation easier (Jones, 2015).

Surveyed professional literature

The first surveyed professional literature on big data analytics is the Enterprise big data engineering, analytics, and management scholarly research conducted by Atzmueller, Oussena, and Roth-Berghofer, in 2016. Their research is basically more interested on engineering approaches emerging big data analytics technologies such Hadoop ecosystem, complex-event processing, NoSQL databases which enable the analysis of bulky, heterogeneous datasets at unanticipated scale and velocity. According to the three researchers, the big data analytics engineering concepts are transforming these technologies by facilitating the storage, maintenance and analysis of information. The other two researches that support the above mentioned research are the Big data and business analytics conducted by Liebowitz and the Handbook of research on Innovative database query processing techniques conducted by Yan in the year 2013 and 2016 respectively.

The other notable research on big data analytics is the Data revolution: Big data, open data, data infrastructures & their consequences, a professional literature conducted by Kitchin in year 2014 and it pertains to technical attributes of big data analytics technology. In addition there four renowned professional literature that define and explain the uses and benefits of emerging big data analytics technologies and how they can be aligned with other emerging technologies such cloud computing in order to improve efficiency and performance. They include Applied business analytics: Integrating business process, big data, and advanced analytics by Lin, 2015; Right-time experiences: Driving revenue with mobile and big data by Lopez, 2014; Biomedical informatics: Discovering knowledge in big data by Holzinger, 2014; and The visual organization: Data visualization, big data, and the quest for better decisions by Simon, 2014.

The Big data analytics: Methods and applications professional literature in regard to emerging big data analytics technologies conducted by Pyne, Prakasa, and Rao in the year 2016 and the technical research titled the All the data we can get: A contextual study of learning analytics and student privacy conducted by Jones in the year 2015 illustrate the security concerns hampering the emerging big data analytics. The developments and advancements in big data analytics has provided users with tools and techniques to extract and correlate data thus, making privacy violation easier. Typically, users can exploit vulnerabilities existing in the big data analytics technologies to advance threats and attacks.

Evaluation

Benefits- big data analytics is beneficial to an organization as it helps a company to discover hidden patterns using various tools such as business intelligence, complex competitive analysis, call center optimization, consumer sentiment analysis, and intelligent traffic management applications as well as other significant big data analytics applications. However, the most important issue in this paper is in regard to cybersecurity and how big data analytics can support security analytics. For instance, fraud detection is one of the most common application of emerging technologies such as big data analytics. In addition, security intelligence tools can be synced in real-time to extract any relevant information one need to convey relevant information.

One way one can use big data to enhance security is through emerging big data analytics for security intelligence. One of the core impacts from big data technologies is that they enable organizations to build affordable and scalable infrastructure for security monitoring through the combination of cloud computing. The big data collected, stored and maintained is analyzed using big data security analytics applications.

For instance, big data analytics technologies such Hadoop ecosystem, complex-event processing, NoSQL databases which enable the analysis of bulky, heterogeneous datasets at unanticipated scale and velocity enhance analysis of security information. In addition, WINE platform1 and BotCloud2 let the utilization of MapReduce to appropriately process data for security analysis. Thus, big data analytics application help organizations to extract valuable information that either concerns tracking particular behaviors or detect threat attacks (Holzinger, 2014).

Thus, the ability to use big data analytics for security intelligence is an effective measure for controlling security concerns and attacks. Discovering security threats and attacks patterns can help organizations to establish the sequence and impact as well as supporting the sharing of that information with other stakeholders in order to contain the attacks. Discovering the attack patterns result into identifying the vulnerabilities that are being exploited and hence, reducing risk. Reducing risk means that the big data analytics are efficient and thus, the big data analysis will lead to informed business and security decisions.

Vulnerabilities-a lot vulnerabilities in the big data and analytics technologies, and which include physical, architectural, administrative and logical based loopholes. Enterprise-wide big data have complex structural challenges which pose enormous vulnerabilities for most organizations. For instance, emerging big data analytics are accompanied by new software components but with few utilities to protect features and application interfaces (APIs). And considering that the big data application are created on web-based services models, criminals can utilize well-known threats to attack big data assets and information (Jones, 2015).

In addition, criminals can utilize physical security mechanisms to infiltrate the information and communication technology infrastructure to steal big data assets. In addition, an insider threat such as malicious employees can defy the code of conduct and conspire with external agent to steal the big data assets that are on transport. This can be referred to as a big data breach. On the other hand, big data can also be accessed illegally if the design was inappropriately developed, if the software implementation was poorly deployed or configure and when business process fails. The vulnerabilities that exist due to inappropriate configuration or deployment can be used by attackers to launch logical-based attacks.

Security implications-despite application of emerging big data analytics technology promising to be a big achievement to solving security issues through real-time threat monitoring, there are other security implications that arise from it is implementation and use. The security implications include confidentiality, integrity, availability, and authentication. Confidentiality arise due to violation of privacy and sharing of personal details with other industry sectors as well as law enforcement. Privacy regulations prohibit access, storage and sharing of personal information without the consent of the owner. The developments and advancements in big data analytics has provided users with tools and techniques to extract and correlate data thus, making privacy violation easier (Jones, 2015).

On the other hand, the bulky data can lead to poor analysis hence, integrity issues while availability of data can also be affected by vulnerabilities that exist in the new components of emerging big data analytics. For instance, remote controlled attacks can alter or leak data meaning authentication is an issue that need to be addresses by future developers while modifying data will lead to inaccurate analysis and hence, misinformed decisions.

Conclusion

In conclusion, big data analytics are transforming technologies by facilitating the storage, maintenance and analysis of information as well as align them with intelligence tools and applications to unravel hidden patterns hence, improving business operations.

References

Ambur, M. Y., & Langley Research Center,. (2016). Big data analytics and machine intelligence capability development at NASA Langley Research Center: strategy, roadmap, and progress.

Atzmueller, M., Oussena, S., & Roth-Berghofer, T. R. (2016). Enterprise big data engineering, analytics, and management.

Jones, K. M. L. (2015). All the data we can get: A contextual study of learning analytics and student privacy.

Liebowitz, J. (2013). Big data and business analytics. Boca Raton, FL: CRC Press.

Kitchin, R. (2014). The data revolution: Big data, open data, data infrastructures & their consequences.

Lin, N. (2015). Applied business analytics: Integrating business process, big data, and advanced analytics.

Lopez, M. (2014). Right-time experiences: Driving revenue with mobile and big data.

Holzinger, A. (2014). Biomedical informatics: Discovering knowledge in big data.

Marr, B. (2015). Big data: Using SMART big data, analytics and metrics to make better decisions and improve performance.

Pyne, S., Prakasa, R. B. L. S., & Rao, S. B. (2016). Big data analytics: Methods and applications. New Delhi, India: Springer

Simon, P. (2014). The visual organization: Data visualization, big data, and the quest for better decisions.

Yan, L. (2016). Handbook of research on Innovative database query processing techniques.

In West, L. L., & In Worthington, A. C. (2017). Handbook of research on emerging business models and managerial strategies in the nonprofit