Responses | Management homework help

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ANSWER ALL 4 RESPONSES.
Question 1(Mandeep)
Big data refers to data whose complexity makes it impossible to employ traditional methods of data analysis. The complexity of big data is gauged on the four Vs, namely volume, velocity, variety, and veracity. In the modern-day, big data is a contributor to an organization’s competitive edge over its competitors. One company that employs big data to attain this competitive advantage is Amazon.
Velocity :

The velocity aspect of big data refers to how fast information flows into an organization. Companies like Amazon use this fast-moving data to gain a competitive advantage over their competitors. Amazon provides its services through its online platforms hence the products that its customers purchase or interact with create a data flow back to the company. Using big data, the company can compile histories of a customer’s interactions on their platform and use that information to predict and recommend relevant additional purchases (Garcia, 2013). A faster flow of consumer purchase history hastens the feedback loop by creating prompt responses that could affect consumers’ buying. If a recommended suggestion is viable, the consumer buys two products or more when they intended to buy one, hence adding to Amazon’s sales and giving it a competitive edge.

Variety

Big data grants organizations access to different data types that often do not fit into the traditional relational databases. For instance, big data is inclusive of video and photographs. Big data enables an organization to deduce data meaning and relevant metadata hence making meaning of it. Therefore, Amazon is steadily aware of unmet consumer needs, feedback, and current trends through information sourced from social media and online sources to meet them effectively.

 Data veracity :

Big data can be messy and inaccurate, hence misleading. Due to the volume of big data, the accuracy and quality of data presented may often be incorrect. Big data analytics make it possible for an organization to effectively distinguish facts from fiction hence getting the upper hand over its competitors. That way, Amazon is aware of market trends and relevant information, which advises decision-making on behalf of the organization (Hewage et al., 2018).

Data Mining and its Benefits :

Data mining refers to analyzing large data volumes to find relevant business intelligence for specific objectives within a business. The process helps organizations solve various business problems while gaining advantages like; fraud detection, credit risk identification, revenue increase, understanding customers, and monitoring operational performance. Moreover, data mining helps to improve customer loyalty and retention and increasing ROI from marketing campaigns.
Question 2(Monique)
Big data is comprised of large datasets that are too big to be captured, visualized analyzed, the conventional way (Sharpe, et al., 2019). Instead, big data, according to scientists at IBM, have defined the 4 V’s that make big data as volume, variety, velocity and veracity (Sharpe, et al., 2019).  Verizon Communications, which is a global conglomerate, specializing in telecommunications, has depended on the collection of big data in order to foster more analytic culture. Verizon has groups that are focused the application of analytics and cognitive technology to Verizon’s interactions with its customers (Bean & Davenport, 2017). With these groups, Verizon is able to gain competitive advantage through the variety of data it is capturing, including landline, wireless and broadband data.  Additionally, the speed and timeliness of the data, or the velocity, is captured through Verizon’s teams with dedicated resources, as well as AI technology (Bean & Davenport, 2017).  Last, the veracity of the data is the measured quality (or lack thereof) of the data (Sharpe, et al., 2019).  Verizons’ in house teams manage the audit of the big data for quality. 
These teams also engage in the process of data mining to identify patterns and relationships in efforts first making the correlation between the data understandings and business understandings through developing analytics (Sharpe, et al., 2019).  The outcome of data mining among other things is to make valuable predictions for the company.  In order to meet that expectation, it is important to understand some of the challenges a company like Verizon can face, such as having too many variables – it can add a degree of unnecessary difficulty to the model selection.  Also, challenges can exist in the data preparation part of modeling, like spending too much time on investigating missing values or reconciling data definitions.  These challenges are time consuming if Verizon lets it, so it is especially helpful to have automated tools to help speed up the process and mitigate the issues.
Question 3(Andrew)
  Text mining allows a company to examine text data from websites with the aid of machine learning to uncover new topics or connections (SAS, 2021).  An example of a company that uses text mining is Fitbit.  Fitbit has used text mining to gather and analyze text posted to Twitter.  During six months, 33,000 posts referenced Fitbit.  The company was able to use text mining tactics to realize an issue with the strap on their Fitbit Charge HR model.  Once Fitbit understood the challenges their customers were having, they could fix the problem (St. Jeor, 2020).  This is an example of text mining because Fitbit compiled a large volume of data from a website to uncover an issue with its product.
                There are three types of web mining. They are web content mining, web structure mining, and web usage mining (Eduonix, 2018).  Amazon uses each of these web mining processes to gather data on their customers and improve the customer experience on their site.  David Selinger was the lead for the Customer Behavior Research unit at Amazon, and he recounted how they would walk through a single customer’s experience on their website.  Then, based on an understanding of that individual’s experience, they could personalize their experience through their website (Kelion, 2021).  This is an example of web structure mining where analysts review how web pages and hyperlinks are used (Eduonix, 2018). 
Another tactic that Amazon has used is to partner with larger retailers such as Target and Borders to have Amazon operate their e-commerce website.  Amazon then used these sites to gather data about these retail sectors and eventually added these other sectors to their website (Kelion, 2021).  Amazon was able to learn about the other businesses through web usage mining.  Web usage mining collects related files from web servers to discover customer transaction patterns (Eduonix, 2018).  The final web mining approach that Amazon has used in web content mining.  This is the process of gathering and analyzing content such as text, images, audio files, and video files (Eduonix, 2018).  Kelion (2021) requested all of the data that Amazon had collected from his usage of Amazon.  He found that Amazon had collected audio clips from his interactions with Alexa, a file that revealed 2,670 product searches, and details of 83,657 Kindle interactions.  Amazon uses all of this data to personalize recommendations and customize landing pages for each customer (Kelion, 2021).  Amazon has become an expert at web mining. They have even launched a separate company, Amazon Web Services, to sell these practices to other companies.
Question 4(Catherine)
Data mining allows a business to gather information from many sources and then use that data to determine patterns, answer questions, and solve problems. Two ways to practice data mining are text mining and web mining. Text mining allows a business to pull information from text. For example, this can be an email or a review. They can search for key phrases within this text that allows them to answer their question posed by the data mining project. Web mining allows a business to identify and discover patterns. These patterns allow the business to learn more about the customer and their habits.
Microsoft, as an organization, uses these methods of data mining to their own advantage One example of how Microsoft uses text mining is how they take the feedback left by the users of their software, such as Teams and Office, and uses the data pulled from these reports to make changes in their updates. They use web mining to visualize how their business operates. By using this form of mining they are able to easily uncover challenges and bottlenecks that users might encounter while using Microsoft hardware and software.
In addition to using these forms of data mining in their own business practices, Microsoft also provides customers with the software to perform these projects within their own businesses. Microsoft sells a service called Azure, which provides customers with Natural Language Processing (NLP) features. With the features Azure provides companies can use it to do a variety of text mining practices. Key phrase extraction allows companies to quickly identify the main concepts in bodies of text by looking for talking points. Language detection can detect the language a text was written in. And Named Entity Recognition (NER) can identify and categorize specific words in large bodies of text as people, places, organizations, and quantities. All of these tools offered by Azure can be applied to many data mining projects.

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