Week 4 Response to Discussion 1 & 2 BUS 624/625
kingstonman41
BUS625Week4ResponsetoDiscussion1.docxBUS 625 Week 4 Response to Discussion 1
Guided Response: Your initial response should be a minimum of 300 words in length. Respond to at least two of your classmates by commenting on their posts. Though two replies are the basic expectation for class discussions, for deeper engagement and learning you are encouraged to provide responses to any comments or questions others have given to you.
Below there are two of my classmate’s discussion that needs I need to response to their names are Lisa Schreiner and Robert Mcalexander
SaturdayMar 14 at 9:18am
1. Volume: It refers to the incredible amount of data that is generated each second from multiple sources such as cell phones, social media, online transactions, etc.
2. Velocity: It refers to the speed at which the data is generated, collected, and analyzed.
3. Variety: If refers to the different types of data such as structured, semi-structured and unstructured data. Structured data has a fixed format and size, semi-structured data has a structure but cannot be stored in a database, unstructured data does not have any format and or hard to analyze.
4. Veracity: It is the trustworthiness of data in terms of quality and accuracy. Extracting loads of data is not useful if the data is messy and poor in quality.
Netflix collects big data for competitive advantage by tracking the types of shows or movies people watch. According to Dans (2020), “the company began to verify when it used to send DVDs by mail, then it began to replace this with streaming. An approach that provides superior data and instantaneous feedback, as well as setting it apart from the competition” (para. 3). The velocity or speed at which the data collects through the internet is in real time with a click of the mouse or remote control button. The variety of data collects in a structured manner by genre, frequency, and time of day. The structured data allows quick algorithms to run in the background and suggest specific viewing options on the account in an instant. The veracity or quality and accuracy of the data collection provides a level of detail with no external or human influence to interpretation leading to clean data in the warehouse. “Netflix’s success proves this: if we consistently resort to data analysis, a greater percentage of our decisions will be better made, the risks we take will be more balanced, and the results will be better” (Dans, 2020, para. 5). Netflix has provided me with many successful viewing suggestions during personal use proving the data mining and analysis techniques work.
The values of data mining in a business is to provide correlations, patterns or trends in the market products or services. The data use can increase the marketing strategy by defining the target market for cost effectiveness and maximizing profits. Fraud detection is a valuable purpose for many businesses such as banks, credit card companies, insurance, retail, and more. Minimizing losses due to fraud increases value in a product or service providing protection. Data analysis adds value to decision-making in organizations and personal practices through process improvement, identifying successful offerings for further expansion, or unsuccessful offerings to cut the losses.
Challenges in managing a data mining project include unstructured data, unclean data, data protection and security, ensuring questions to answer are specific, and willing to work with others – this is a team effort. Unstructured and dirty data leads to unreliability. The errors can manifest from measurements, quantification, or simple human keying mistakes. Protection and security of data is the forefront of every person and business due to the ease and speed of obtaining information through technology. Acquiring permission to pull data for analysis is challenging in this environment. Questions must be specific for the process otherwise the database is too large providing unreliable and possible duplication in output. Data mining is a team effort. One must be willing to work and share knowledge and findings across levels to provide reliable conclusions.
References
Dans, E. (2020, January, 15). Netflix: Big Data And Playing A Long Game Is Proving A Winning Strategy. https://www.forbes.com/sites/enriquedans/2020/01/15/netflix-big-data-and-playing-a-long-game-is-proving-a-winningstrategy/#2ec78c7a766e (Links to an external site.)
Sharpe, N. D., De Veaux, R. D., & Velleman, P. F. (2019). Business statistics (4th ed.) . https://www.redshelf.com
Robert Mcalexander
SundayMar 15 at 8:05am
Good morning everyone!
Provide an example of a company that is collecting big data for competitive advantage. Explain how each of the three Vs, outside the volume, is helping the company achieve competitive advantage.
Big Data: The collection and analysis of data sets so large and complex that traditional methods typically brought to bear on the problem would be overwhelmed.
A standard example of a company that uses big data would be our friends over at Amazon. All the data that comes in on a daily basis surely helps them to sustain their competitive advantage here in the United States, and it also helps them to seek one on a global level. When looking at the four “V’s” of big data, the first one seems rather obvious. With the number of users on a daily basis, there has to be overwhelming quantities of data continuously streaming in. Amazon will also receive quite a variety of data even when just looking at one user. Think of all the different types of services Amazon offers. They can track down what you are listening to, what you are watching, what you are eating, what you like to wear, your favorite games or hobbies, and much more simply from your search/buy history. This variety can help them to better tailor their user interface to each individual by making suggestions based on data they have collected. The next aspect of big data to look at would be the velocity of the data. Again even just breaking this down on a single user basis, there is so much data coming in all at once just through a few clicks. So when you take that and multiply I by the hundreds of millions of users that frequent these services on the daily, that is a lot of data at a high rate. The final aspect of big data would be the veracity, or the quality of the data. This is where I feel Amazon would have trouble sorting through the quantity of data they receive. But with all of their services they surely have the opportunity to gather extremely high quality data.
Explain the values of data mining in a business and at least three challenges in managing a data mining project.
One of the best values that comes from data mining is the ability to make an accurate forecasts on demand. By tracking trends in data, Amazon can be much better prepared and provide the end user with the highest quality services. But by doing this, there will inevitably be massive challenges associated with it. Three significant challenges would be the infrastructure, responsiveness, and finally using the data gathered. Infrastructure would be a major piece of this equation. You would need to have a sufficient hardware, software, and manpower in order to accurately gather and use all the data. The next challenge would be the time taken to respond. This will by no means be a quick turnaround. “This can be a time-consuming part of the process and is also likely to be a team effort. Investigating missing values, correcting wrong and inconsistent entries, reconciling data definitions, and merging data sources are all challenging issues.” (Sharpe 753) The final challenge that Amazon would face would be on actually using the valuable data. Being able to make decisions based on the data found will again take time and by then the information could be deemed irrelevant.
Govindarajan. V.G. (2018 February 2) Can anyone stop amazon from winning the industrial internet? The Challenges for industrial giants. Retrieved from: https://hbr.org/2018/02/can-anyone-stop-amazon-from-winning-the-industrial-internet (Links to an external site.)
Sharpe, N. D., De Veaux, R. D., & Velleman, P. F. (2019). Business statistics (4th ed.). Retrieved from: https://platform.virdocs.com/r/s/0/doc/509177/sp/68046783/mi/291160736?cfi=%2F4%2F2%5BP700101598900000000000000000C2CE%5D%2F28%5BP700101598900000000000000000C3F3%5D%2F6%5BP700101598900000000000000000C3F6%5D%2F8%5BP700101598900000000000000000C3FD%5D%2F2%5BP700101598900000000000000000C3FE%5D (Links to an external site.)
