Data Security Concepts for Business Organizations

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Table of Contents

Executive Summary
Introduction
Analytics Tools and Methods Used in Data Analytics
Spotlight: A Large Enterprise
Spotlight: Smaller Company
Applicability of Management Theories
Conclusion
References

Executive Summary

The future of a company greatly relies on data security, being one of the fast-rising issues; it can potentially make a company grow or leads to its downfall. Every company is mandated to become aggressive and develop strong and reliable data security since big data can be both an opportunity and a threat to the firm. Both small and big businesses are susceptible to cyber-attacks and just a single data breach can lead to a lot of losses. Mangers have the responsibility to take corrective measures to avoid a crisis before stakeholders get the information and change the investment strategy. Proper and promising data security helps in enhancing the companys profits. It also allows firms to make proper use of their resources for profit gains.

Introduction

The most controversial subject in any company is the issue of data analysis. Data analysis creates a lot of attention and fear among managers and administrators concerned. Several companies assume that the data they store and deploy cannot be hacked, however to hackers any type of information seems valuable and can be utilized for malicious deals (Ghosh & Nath, 2016). Many companies have so far assumed that cloud computing is the best solution to data analysis problems, although they still face challenges associated with data storage and deployment (Sharma, 2017). Big companies are also affected by data security just as it is observed in Small and Medium Enterprises (SMEs). Adopting, big and reliable data analytics does not depend on the size of the company therefore both large and small businesses need to incorporate the idea.

Analytics Tools and Methods Used in Data Analytics

Currently, there are more than ten thousand tools used for data analytics and analysis. The requirements of big data analysis are among the methods that can be used to determine the meanings of the invisible relationships in big data. Among the requirements, is reducing the movement of data by mainly conversing the computing resources. The process of mining and analyzing a large amount of data is known as big data analytics and it involves the use of several tools and sites. Some of the most used tools include Apache Spark, Apache Hadoop, HBase, Grid Gain, Storm, HPCC, and Casandra (Imran et al., 2018). The tools are used based on the parameter identified for the big data analysis. Data analytics is growing at a faster rate than it was previously observed. It is currently being advanced to contain both traditional and semi-structures data.

Data analytics is a very standard approach to discovering previously obtained data which is generated using several applications for pattern searching that cannot be investigated, processed and managed by other present tools or methods. It is a strategy used to obtain crucial correlated information from the huge dataset (Imran et al., 2018) big data analyzing tools has five major approaches used to analyze data and create reports or analysis data. The Discovery tool is very important in the information lifecycle for frequently exploring and analyzing information of any combination of the unstructured, structured, and semi-structured source. These tools enhance analysis in conjunction with traditional systems (Chawda & Thakur, 2016). Users can always come up with new reports and make meaningful reports and decisions using Business Intelligence (BI) tools. The BI tools are very crucial in analyzing, reporting and management of performance mainly with information systems and data warehouses.

High-availability distributed objected-oriented platform (Hadoop) tool, is the most popular open-source tool for reliable computing. This tool is very significant in data pre-processing. It helps in the identification of macro-trends and discovering chunks of information for instance, out of range values. All companies mainly use Hadoop as the precursor for advancing modes of analysis (Chawda & Thakur, 2016). One of the major problems faced by big data and data analytics is the possibility for them to be accessed from external sources by the use of certain methods, for example, Hadoop. Another method is in-database analytics which are analytical methods that enhance data processing applied directly within the database. It consists of several techniques such as detection of fraud, credit scoring, trends, or discovering patterns and relationships in the data.

Decision management is another important tool that contains predictive modeling, personal learning, and business rules to take proper action depending on the current context. This method allows diverse recommendations across several channels thus maximizing the significance of all clients to communicate. All the methods play a significant role in discovering the hidden relationship. Several businesses, provide analyzing tools and framework which are present for exploring big data and can be used to perform analytics (Chawda & Thakur, 2016). The advantages of big data analytics include cost reduction, sound decision-making by corporates, and observable improvements in the operation of firms. On the other hand, big data is really overwhelming because of its speed, volume, and diversity which must be properly managed.

Spotlight: A Large Enterprise

Target used big and predictive data analytics to identify pregnant women before the women disclosed the information to their relatives and close friends. In 2002, the company hired Andrew Pole, a statistician who tasked the marketing department to find out if there was a method to apply predictive analytics and statistics to discover a pregnant customer. The research began by going through the present baby shower registry and recognizing the women who have informed target about their pregnancy through registering (Sheldon, 2016) the purchasing behavior of the clients was observed thus helped the statisticians to identify important patterns. The data and predictive analytics created an opportunity for revenue growth since Target was able to reach women with ads and coupons for the products they yearned to buy. This helped them convert a client into their loyal guests.

Target experienced a serious data security threat towards the end of 2013 and this seriously affected the companys reputation besides its profitability. The company thus decided to take precautions that were significant to avoid outsiders from hacking their systems. They installed tools for detecting the new malware and the tools were developed by FireEye (Sheldon, 2016). FireEye is a computer security company used mainly by several government agencies including the Central Intelligence Agency. Target was still unsuccessful in its attempts even after the installation of the new and reliable data security system. Hackers still managed to compromise several credit card and debit accounts. The clients remained vulnerable since their card data was intercepted. More than 40 million clients card information was seized and the massive breach affected more than 70 million clients.

Annually, almost 70% of companies are faced with the issue of data security where hackers access their systems. Information management needs to come up with policies to stop hackers from breaching their security. Target lost several stakeholders, clients, and donors, because of data insecurity experienced in 2013. The company would have had a ticket to solve the malware issue that was untrustworthily uploaded by the hackers, but many thought that the new security tool would provide a solution. The security team thus removed the application to discover the foreign programs and the warnings were not identified in time thus allowing hacking to take place (Hess & Cottrell, 2016). It placed a broad effort to minimize hackers through the installation of anti-virus software and malware detection systems, although their overall detection and defense system was not helpful.

Spotlight: Smaller Company

Cybercrimes always affect enterprises and the practices always affect profitability in several firms. Small companies are always ill-prepared to end the emerging data security threats. The administrators with good leadership skills can help prioritize the activities needed to end data insecurity and ease information security fears. Within small businesses, the managers, play a very crucial role in protecting the companys information through the activities they can initiate and their influence on the employees (Barlette et al., 2017). Currently, the information systems of small enterprises in the USA are susceptible to data insecurity and system hacking.

Cybercriminals can easily find information on small businesses by discovering the existing weak points. Small enterprises can effectively fight and defend themselves against data insecurity just as large firms such as Walmart that are recognized globally. Just as, Target, Yale University faced a serious security breach. It took some time to notify everyone from students to investors that they had been affected by the data breach. Furthermore, they state that the information was taken without their knowledge. The Hackers also retrieved their dates of births, physical addresses, and email addresses. This contained private information for instance names and other personal details that could be used to harm them (Osborne, 2018). Many of the affected individuals had to scramble to find techniques of protecting the already leaked information. The university cleared the data that was interfered with by the hackers. Since the occurrence of the incident, Yale has advanced in its electronic security.

To avoid data breaches, small businesses should conduct regular audits for their information security. Furthermore, they should initiate cheap and practical strategies for security issues. For instance, deployment of an internal computer response team or installation of an intrusion detection system (Barlette et al., 2017). All companies regardless of their sizes should be able to protect the clients personal information and the managers of small and medium enterprises (SMEs) have to comprehend and value the different concepts concerning online business and internet-based services.

Applicability of Management Theories

Although a lot of progress has been made for many years in the development of management theories, numerous confusing theories have been established. There are classical management theories that are still of great importance currently (Tuczek et al., 2018). Management theories are crucial basic theories that help several companies to develop and thrive even in times of doubt. Good leadership is earned and every leader should always be willing to learn new skills. Every leader must not end the training process but must practice all the acquired skills and knowledge. There is no shortcut to discovering new management and leadership skills. Leadership is the skill to take charge of any activity and guide other people to succeed besides achieving the targets. Firms have to find options to discover opportunities that can help them withstand the problems they face by increasing their data and ensuring proper security of their information.

Theories of change define the effectiveness with which companies can adjust their strategies and structures. Transformation is crucial for companies in growing, highly competitive businesses (Hussain et al., 2018). Management theories are very crucial although the traditional aspects need modification to ensure they are adaptable. Based on data analytics and information security, administrators must find ways to protect their companies against cybercrime. Good leadership and management always ensure that there are proper strategies that ensure the companys information is not leaked out by frauds.

Organizations should develop systems with security modes that can withstand a possible breach in data security. Firms that dont come up with a technology-dependent operation, experience serious threats that affect big data (Hussain et al., 2018). Both big and small companies should practice good leadership and management practices to ensure that the companys data is secure and the business can thrive. Proper leadership and management skills will help one come up with great ideas that will encourage them to build strong data security to prevent hackers from accessing the companys private information.

Conclusion

Since data security has the potential to make or destroy a company, organizations should have an enhanced security system that is purposely to protect the documentations of the company in case of a breach in data, size of the data notwithstanding. Business firms are expected to emphasize putting in place a technology-oriented operation to counter the effects and threats that are likely to result from the Big Data revolution. On the other hand, emphasis is made on the role of leadership as taking the mantle and guiding and making the whole organization actively engage in activities that can protect the organization. In case of breach should be communicated to consumers, customers, investors, and shareholders without any delay

References

Barlette, Y., Gundolf, K., & Jaouen, A. (2017). CEOs information security behavior in SMEs: Does ownership matter? Systems information management, 22(3), 7-45.

Chawda, R. K., & Thakur, G. (2016). Big data and advanced analytics tools. In 2016 symposium on colossal data analysis and networking (CDAN) (pp. 1-8). IEEE.

Ghosh, K., & Nath, A. (2016). Big Data: Security Issues and Challenges. International Journal of Research Studies in Computer Science and Engineering, 1-9.

Hussain, S. T., Lei, S., Akram, T., Haider, M. J., Hussain, S. H., & Ali, M. (2018). Kurt Lewins change model: A critical review of the role of leadership and employee involvement in organizational change. Journal of Innovation & Knowledge, 3(3), 123-127.

Imran, M., Ahamad, M. V., Haque, M., & Shoaib, M. (2018). Big Data Analytics Tools and Platform in Big Data Landscape. In Handbook of Research on Pattern Engineering System Development for Big Data Analytics (pp. 80-89). IGI Global.

Osborne, C. (2018). Yale University discloses an old-school data breach. ZDNet.

Sharma, P. K., Kaushik, P. S., Agarwal, P., Jain, P., Agarwal, S., & Dixit, K. (2017). Issues and challenges of data security in a cloud computing environment. In 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON) (pp. 560-566). IEEE.

Sheldon, L. (2016). Implementing Information Security Architecture and Governance: A Big Framework for Small Business.

Tuczek, F., Castka, P., & Wakolbinger, T. (2018). A review of management theories in the context of quality, environmental and social responsibility voluntary standards. Journal of cleaner production, 176, 399-416.

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