Big Data and Analytics - Question 41

What is Parallel Processing? Why do we need it for Big Data Analytics?

Parallel Processing is a way to reduce the processing time by simultaneously breaking and operating different parts of a task in two or more processors. Parallel processing can be performed in a system with two or more CPU. Generally, parallel processing is used to carry out complex piece of work and computations.

To perform analysis of data, we need all of the data. Since, data is divided into splits and also it is kept in Hadoop Distributed File System (HDFS), parallel processing is mandatory to perform the operation for big data analytics. The most used solution which helps to perform parallel processing is MapReduce.

Big Data and Analytics - Question 42

What are the security issues related to IoT? Which one you believe is more important? and why?

There are many security issues and challenges related to loT. Some of the important ones are:

1. Insufficient testing and updating

Major problems with companies which build such devices is their careless approach in handling of the security risks related to device. Most of the IoT products and devices don’t get enough or any updates at all. This is an important security challenge as the loT connected devices worldwide is increasing day-by-day and this is not coming without a cost.

2. Brute- forcing and the issuing of default passwords

This is also a significant security issue related to loT. It has been noticed that weak characters and login information are leading to hacking of the passwords. This a major problem as manufacturers are also not penalised to do this.

3. Data Encryption

It has become common that in the transmission of the data, it get encoded. The hackers are always seeking for the methods to hack these data. This needs to be prevented as the world is becoming more connected.

4. Untrustworthy Communication

The companies should ensure that high encryption is used among the devices. It is seen that messages are send without any encryption.

Big Data and Analytics - Question 43

Compare and contrast Gartner and IBM data governance maturity models

The difference in the data governance maturity models is very hard to find. The basic difference between Gartner and IBM lies in the metamodel components. The metamodel components comprises of levels, domains, sub domains, artifacts and subject domain dimensions.

Gartner was introduced in 2008 with five basic goals.The total number of stages of maturity in the maturity model is six. The stages in the model has its own attributes. The six stages were: unaware, aware, reactive, proactive, managed and effective.

Whereas, IBM was introduced in 2007. It has a total number of eleven domains. Unlike Gartner, it has five levels: initial, managed, defined, quantitatively managed and optimising.

Big Data and Analytics - Question 44

List and discuss three prominent application areas for text mining. What is the common theme of the three application areas you chose?

As the availability of data in size and relevance is growing on a daily basis, the use of text mining is also gaining popularity.

1. Risk Management: It has been seen that lack of risk management is leading to failure in many industries. Therefore, softwares based on text mining is used to mitigate the risk.

2. Knowledge Management: It is always a problem to gather information quickly. Therefore, softwares based on test mining will help to gather information.

3. Cybercrime Prevention: With an increasing rate of cybercrime and frauds, there is an urgent need to prevent from these crimes. Text mining intelligence works as a rescue from these types of crimes.

Big Data and Analytics - Question 45

Discuss various types of analytical techniques and how they could be applied to different data such as call centre’s call records, Netflex Video streaming, Twitter hashtags for new products and credit card fraud prediction

There are different types of data analytical techniques:

Descriptive Analytics: The data always requires crunching of the data. This technique is used to analyse the market and to understand the drawbacks of the business. It does not deal with estimations. In call center, such analytics is used to record big data.

Diagnostic Analytics: This type of analytics focuses onto find out the deep causes of the events. It will help to find out the factors which led to some results. It can applied in the data of Netflix video streaming.

Predictive Analytics: Predictive analytics tells about the future. It forecasts what will be the possibility of the occurrence of a particular event in the future. It can predict the impact of Twitter hashtags for new products on sales of the products.

Prescriptive Analytics: This along with forecasting suggest course of actions which needs to be taken to achieve the goal. This can be used in the credit card fraud prediction.

Big Data and Analytics - Question 46

Data Mining for Telecom Companies Case Study

1. Why do you think that consulting companies are more likely to use data mining tools and techniques?

The consulting companies deals with new and specific set of scenarios, obstacles and analytical issues on a daily routine basis. Therefore, they need the data mining tools more likely than other companies. Also, to increase the effectiveness and deal with the cut throat competition, these companies need data mining tools.

2. What specific value proposition do they offer?

These data mining tools provide a broad range of modeling techniques and capabilities. Also, they provide fast return on investment. They provide strategies to be implemented for customer relationship management.

3. Why was it important for argonauten360° to employ a comprehensive tool that has all modelling capabilities?

It is necessary to gain competitive advantage in an environment which is highly competitive. The system will allow the argonauten360 degree to perform in a dynamic marketplace. Also, the comprehensive tools used helps for client scoring, clustering and life time value calculations.

4. Can you think of other problems for telecommunication companies that are likely to be solved with data mining?

Due to stiff competition among telecommunication companies, they are facing wide range of problems. The companies can use data mining and predictive analytics to increase the revenue of the company and hence the profitability. With the help of the data, the companies can find the details of the customers who are leaving and the potential customers.

Remember, at the center of any academic work, lies clarity and evidence. Should you need further assistance, do look up to our Big Data Analytics Assignment Help

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