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Discussion-1 150 words with one reference
The concepts of data mining and data analysis are often used interchangeably and while they are remarkably similar, they do have differences. According to Marcu & Danubianu (2019), “data analytics was first used in sales, also called Business Intelligence. This branch of research uses computer techniques to synthesize huge amounts of data and turn them into powerful tools for making the best marketing decisions.” Data analysis focuses on relationships between large sets of data and examines the big picture of the data. Data analysis is concerned with the “why” of the data and the results are typically displayed visually using dashboards and visualizations. Data mining is a component of data analysis and involves examining large amounts of data to discover new information, patterns, or rules. Data mining tools analyze data and covert raw unstructured information into structured and useable information. Data mining focuses on the details of the data and can be used for both small or big data, as well as, structured, relational, and dimensional data. Data mining focuses on prediction, discovery, and statistical analysis and it is used for strategic decision making. When mining data, you are able to get a closer view of reality and gather information for building models that can be analyzed through data analysis to make decisions. Data mining methods that can be used to exploit resources are prediction, classification, clustering, outlier detection, model discovery, matrices, partitioning, among others. Credit card companies use data mining to detect credit card fraud by analyzing the usage patterns to see if there is any deviation from the norm (Koralage, 2019). Data analytics is used by Netflix for targeted ad marketing, Coca-Cola uses data analytics for customer retention, Amazon uses data analytics for innovation and product development, and PepsiCo uses data analytics for enhancing supply chain management (J., 2020). I currently use data analytics and mining techniques to a small extent to gather information from our various education systems.
Resource
J. K. (2020, February 10). 5 Real-World Examples of How Brands are Using Big Data Analytics: Mentionlytics Blog. Retrieved May 27, 2020, from https://www.mentionlytics.com/blog/5-real-world-examples-of-how-brands-are-using-big-data-analytics/
Koralage, R. (2019). Data Mining Techniques for Credit Card Fraud Detection.
Marcu, D., & Danubianu, M. (2019). Learning Analytics or Educational Data Mining? This is the Question.. BRAIN: Broad Research in Artificial Intelligence & Neuroscience, 10, 1–14. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&AuthType=shib&db=a9h&AN=139367236&site=eds-live
Discussion-2 150 words with one reference
Data Analytics Is the process of breaking important information that has been gathered from multiple sources into several pieces of important information by which organizations will be able to analyze the relationship between certain factors in the data. That means for understanding the relationship between certain factors existing any data the concept of data analytics easiest. The concept of data analytics is useful for understanding the market patterns and trends. Organizations make use of data analytics for identifying and building robust data management through which they will be analyzing the answers to the question of how a business is happening, where is the business successful, what are the steps taken by the organization to have success in the market. The concept of data analytics is used by data analysis and data scientists in the reset process of understanding your business and how they will be able to take effective decisions for future development. Organizations like Amazon, Netflix, capital one and many other organizations make use of the idea of big data analytics for better decision making through which they will be able to analyze the purchase patterns and the requirement of customers and how they're spending their income based on which offers are developed to attract customers.
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The concept of data mining includes extracting information from data, cleaning, and learning from an unstructured form of data after which the concept of data analytics can be applied. Data mining experts use of algorithms which may be mission-based for intelligent analysis of data and to sort out the data. The concept of analytics will make use of statistics and also machine learning.Data mining can be referred with the process of discovering the data end-user method which is used for the analysis of the information for gaining a different viewpoint after which we will be able to summarize the observed data in a helpful manner. That means some unstructured form of data will be better understood after the application of data mining. Imarticus (2018) states that “Data mining techniques are utilized in entirely different analysis fields like selling, information science, arithmetic, and biological sciences. Mining is another variety of data processing, that is usually used in client relationship selling.”
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Reference:
Imarticus (Oct 2018). WHAT ARE THE DIFFERENCES BETWEEN DATA ANALYTICS AND DATA MINING?
Discussion-3 100 words
With advancements in information communication and technology and increased access to the internet, firms are more exposed to vulnerable threats that may result from employees, staff, and the techniques used in the organization. Internal threats result in authorization of accessing the network by someone in the organization (Boothe & Caspary, 2017). The external threats for an organization result from individuals or firms outside the company. Internal threats result from employee actions or in situation when the organization's process fails hence bypasses the exterior defense.
On the other side, the agents to external threats do not have access to the organization's facilities such as networks or computer systems. Thus, the threat agent must act outside and overcome the exterior organization defense for it to access the database. Both internal and external threats have negative impact on the organization and may lead to loss or damage of data. Internal and external threats contrast in that, external threats have limited access to the company's network. However, the internal threat has various levels of accessibility depending on the privilege of every network resource.
Threats, whether internal or external, have negative impacts on the organization since it may result in destruction of data or information disclosure, among other effects. Thus, each category threat is entitled to various countermeasures that help to prevent threat occurrence. In both types of threats, countermeasures are designed to mitigate the risk and prevent the loss associated with them. The internal threats may have countermeasures like security awareness,re-verification, compliance activity, or security policy (Gromyko,2018).On the other side, countermeasures for external threats may have
security firewalls and perimeter to prevent unauthorized persons from entering the organization premises. Besides that, external threats also incorporate used of security lighting as a psychological deterrent to an intruder intending to access the network systems. Thus, all the countermeasures for each case focus on strengthening the security of data.
Reference
Boothe, D., & Caspary, M. (2017). Critical Issues and Practices for Hybrid and Online Teaching and Learning: Building Improved Models for 21st Century English Language Learning. Cognitive Science–New Media–Education, 2(1), 99-108.
Gromyko, A. A. (2018). Greater Europe: Internal and external threats to security. In Multipolarity: The promise of disharmony (pp. 161-174).
McKeen, J. D., & Smith, H. A. (2015). IT strategy: Issues and practices (3rd ed.). Pearson
Discussion-4 100 words
Threats are common phenomenon in today’s organizational function. Inclusion of information technology has increased the chances of threats in both internal and external environments making serious considerations necessary for developing counter measures.
External and Internal threats and Counter measures
External threats refer to the categories of attacks and risks that damage the physical existence of an organization – its building, establishment, tangible technological and no-technological assets and human resources. Natural calamities, robbery, attacks, sabotage, fire and other accidental causes are responsible for physical threats in an organization. To narrow the perspective, in data centres physical threats are also those that damages the information system components like computers, network connection tools etc. Such threats can be prevented by different precautionary measures. Crime Prevention Through Environmental Design (CPTED) is a discipline that emphasises on mitigating these risks by building planning, space setting, designing buildings and surroundings, lighting, security locking, installing modern technological equipments to maintain surveillance, improving physical security monitoring etc. Security for technological assets needs some additional measures like authenticating users to permit access to the data centre and physical digital asset, restricting external devices, monitoring activities and the like (Clancey, 2015).
Internal threats can cause due to lack of maintenance of machines, equipments, electricity and unhealthy working condition in general. In specific, where IT operation is concerned, internal threats relate to cyber crimes and attacks to the organization’s information system – to the network and database to be precise. Organization’s database retain valuable information about different areas required for business development. External threat agents or internal vulnerabilities cause severe data breaches violating the three pillars of data security – Confidentiality, Integrity and Availability. Malicious agents like virus, worms, rootkit, SQL injection, bot, spyware, ransomware and other similar malware get access to a system’s administration by exploiting vulnerabilities like absence of anti-malware tools, firewall and other intrusion detection and prevention system including penetration testing for security check. Social engineering and phishing attacks are the most common causes behind these breaches resulted from human error. User education, installation of security tools, regular monitoring, access control, authentication and most importantly security regulatory compliances can save internal systems of IT from attacks and ensure business continuity and disaster recovery in unusual circumstances (Rao & Selvamani, 2015).
Conclusion
However, in both cases two things are common – regular security checking and dedicated teams for maintaining external and internal security and managing disasters. Physical security teams are engaged for combating external threats and incident response teams are built and maintained for ensuring quick disaster recovery and uninterrupted business.
Reference
Rao, R., & Selvamani, K. (2015). Data Security Challenges and Its Solutions in Cloud Computing. Procedia Computer Science, 48, 204-209. doi: 10.1016/j.procs.2015.04.171
Clancey, G. (2015). Think crime! Using evidence, theory and crime prevention through environmental design (CPTED) for planning safer cities. Crime Prevention And Community Safety, 17(1), 67-69. doi: 10.1057/cpcs.2014.18