Discussion Data Collection, Analysis, And Application

Discussiondata Collection Analysis And Application Is Big Business

Discussion: Data collection, analysis, and application is big business. This is the foundation of business intelligence. This week I’d like for you to share here what your experiences are with big data and the businesses that use it. You may feel that the data Facebook collects on you is invasive or beneficial. You may look at what the government has on you threatening or reassuring (since they have even more on the bad guys).

Now don’t everyone go picking on Facebook and Google. You don’t even need to make this personal. Business intelligence could be data about competitors. It can be business or government. The list is long, and I’d like to see some creative thinking out there.

Even in my industry, Cybersecurity, there are many cyber threat intelligence sources that I access daily to know what’s going on in the world that could be threatening my bank. It’s my job to protect your money. What would you think of a CISO that didn’t keep a finger on the pulse of malicious activity in the financial sector? What types of threats affect your business and where do you go for information about that? Get creative out there! 300 words APA format with intext citations with references.

Paper For Above instruction

In the contemporary digital landscape, data has become an invaluable asset for businesses and governments alike. The proliferation of big data analytics has transformed how organizations make decisions, strategize, and safeguard their interests. Personally, my experience with big data revolves around its application in cybersecurity, where monitoring threat intelligence is crucial to prevent malicious activities and protect sensitive assets (Kumar et al., 2020). Organizations utilize diverse sources such as dark web monitoring, social media analysis, and government alerts to stay ahead of cyber threats, much like financial institutions that rely on real-time data to detect fraudulent transactions (Chen et al., 2019). These sources enable a proactive security posture, exemplifying the importance of timely and relevant data collection. Notably, the value of such intelligence extends beyond cybersecurity, encompassing competitor analysis, market trends, and regulatory changes, thereby informing strategic decisions (Davenport et al., 2018). For instance, companies monitor competitors' social media campaigns and financial disclosures to anticipate market movements (Marr, 2020). Conversely, individuals may perceive data collection by entities like Facebook or Google as invasive, raising privacy concerns. While some argue these practices enhance personalized experiences and targeted advertising, others worry about data misuse and surveillance (Tufekci, 2018). The balance between leveraging data for economic advantage and safeguarding individual privacy remains delicate. In the realm of government, intelligence agencies gather vast amounts of data to protect national security, reassuring citizens of their safety while potentially alarming those concerned about civil liberties (Nissenbaum, 2010). In cybersecurity, organizations adopt a layered approach involving threat intelligence platforms, intrusion detection systems, and industry sharing groups to defend against threats such as malware, phishing, and insider threats (Gartner, 2022). These initiatives underscore the critical role of continuous data collection and analysis in mitigating risks and ensuring operational resilience. Overall, big data's strategic application stretches across sectors, underscoring its significance in contemporary business and security paradigms.

References

• Chen, W., Zhang, Y., & Ma, J. (2019). Cyber threat intelligence sharing and analysis: A systematic review. Journal of Cybersecurity, 5(1), 1-15.
• Davenport, T. H., Guha, A., Grewal, D., & Bressgott, T. (2018). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48, 24-42.
• Gartner. (2022). Market guide for cyber threat intelligence platforms. Gartner Research.
• Kumar, R., Singh, M., & Jain, V. (2020). Big data analytics in cybersecurity: A comprehensive review. International Journal of Information Security, 19, 457-470.
• Marr, B. (2020). Data-driven marketing: The main pillars and principles. Forbes.
• Nissenbaum, H. (2010). Privacy in context: Technology, policy, and the integrity of social life. Stanford University Press.
• Tufekci, Z. (2018). Twitter and tear gas: The power and fragility of networked protest. Yale University Press.