Posted: September 25th, 2022

Big Data Analytics Term Paper

Big Data Analytics Term Paper
Part I : Choose one of the following topics to address in an 6-8 page term paper. Using Ace homework tutors – APA formatting, 12pt Times New Roman font, double-spaced. Use at least three (3) academic sources, including at least 2 peer-reviewed journals. Get custom essay samples and course-specific study resources via course hero homework for you service – Include a bibliography (not included in page count).

Big Data Analytics

Big Data Analytics
Big data analytics is the process of examining big data with the aim of uncovering information. some of the uncovered information include correlations hidden patterns, customer preferences and market trends. They help organizations make informed business decisions. In other words, they provide a way in which organizations analyze data sets and gather information. big data analytics involve complex applications with elements of predictive models, statistical algorithms and the what if analysis since it is a form of advanced analytics. The massive amount of data set is hardly stored, analyzed or processes using the traditional tools.
An example of how big data analytics is utilized is the music streaming from Spotify. The company has more than 90 million users who streamline the music every day. The tremendous data generated in that process enables the cloud-based platform to automatically generate suggested songs with the id of the smart recommendation engine. It is based on the search history, shares and likes, among others. these data processing and automation, including tools and frameworks is the Big Data Analytics. Spotify users have come across the top recommendation section based on the likes and past history, among other things. Nowadays, applications like Instagram are doing the same. If you seem to be interested in a certain niche, they bring suggestions based on your search history or likes.
Benefits of Big Data Analytics
There are various benefits of Big Data Analytics. The first is risk management whereby, big data analytics is used to identify fraudulent discrepancies and activities (Hariri et al, 2019: 2024 – Online Assignment Homework Writing Help Service By Expert Research Writers). An example is the Philippine banking company, Banco de Oro, that uses it to narrow down the root causes of an issue and to obtain a list of suspects. The second benefit is development and innovations of a product. Big data analytics is used to analyze the efficiency of the engine designs in some of the automotive. The information acquired is then used to make improvements. An example is the Rolls-Royce, which is the largest manufacturer of the jet engine for the armed forces and airlines all over the world. rolls Royce uses big data analytics to examine the engine and make improvements where need be.
The third advantage is helping in making quicker and better decisions within organizations. Different factors in an organization are analyzed using big Data Analytics. Examples include demographics, population, and accessibility among others. For instance, Starbucks uses Big Data analytics in making strategic decisions. It leverages it to make decisions on locations suitable for a new outlet. The fourth benefit is improving customer experience. Big Data analysis improves the customer experience by identifying a negative feedback, making it easier for corrections to be done. For example, Delta Air Lines uses Big Data Analytics to monitor tweets that relate to the customer experience, delays and everything concerning the journey. The negative tweets help to find solutions to the issues observed by customers. By tweeting, the customers are publicly addressing the issues as well as offering solutions that improve customer relations.
Phases of Big Data Analytics
The lifecycle phases of Big Data Analytics explain further on how big data analytics work. In the first stage, there is evaluation of the business case which defines some of the reasons and the aim of the analysis. the second stage is data identification whereby various data sources are identified. The third stage is filtering of data whereby the identified data from the previous stage is filtered to eliminate corrupt data. The fourth stage is the extraction of data where any data found to be incompatible with the tool is removed and transformed into a compatible form.
The fifth stage is data aggregation where data within similar fields in different datasets is integrated. The sixth step is data analysis where analytical and statistical tools are used to evaluate data in discovering useful information. the seventh stage is data visualization where tools like Power BI, Tableau and QlikView are used in the production of graphic visualizations of the analysis. the final stage is final analysis of the results. here, the final analysis of the results is made ready to be used by the business stakeholders responsible for taking action.
Types of Big Data Analytics
There are various types of Big Data Analytics which include, descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics. Descriptive analytics assists in creation of reports by making summaries of past data into a n easily read form. Some of the reports made include an organization’s revenue, sales, profit, among others. it also takes place in tabulation of social media metrics. Diagnostic analytics is performed with the aim of understanding the cause of a problem. Some examples include data mining, drill-down and data recovery. Diagnostic analytics provides an in-depth insight of a problem.
Predictive analytics concentrates on the historical and present data in making future predictions. Some of the tools used include data mining, machine learning and AI in analyzing current data and making of the future predictions. It predicts the trends in the market, customer trends, among others. an example is PayPal which uses historical payment data and the user behavior data to build a predictive algorithm that identifies fraudulent activities. Prescriptive analytics provides the solution to a specific issue. it works with predictive and descriptive analytics. AI and machine learning are the most used tools. An example is how it can be used to maximize the profit of an airline by adjusting the flight fares depending on factors like the weather, holiday seasons, customer demand and oil prices.
Big Data Analytics Tools
Some of the important big data analytics tools include the following, Hadoop, used to store and analyze data. MongoDB is used on frequently changing datasets. Talend is another tool that is used to integrate and manage data. Cassandra is a distributed database used for handling data chunks. Spark to analyze large data amounts and real-time processing. STORM happens to be an open-source real-time system of computation. Kafka is the final example and it is a streamlining platform that is distributed, used to store fault-tolerant.
Big data analytics in the world today
Big data analytics influences how machines learn human language. Big data is always increasing and it includes emails, social media, texts, sensor data, among others. machines can sift through big data volumes with the help of natural language processing to analyze sentiment, uncover trends and identify correlations. Big data analytics has built a better world today. SAS has been passionate about the use of advanced analytics in improving the future. It could be in addressing poverty related issues, hunger, diseases, education, climate change, or illiteracy. Alternative data is essential in changing the business world today (Popovič et al., 2018: 2024 – Write My Essay For Me | Essay Writing Service For Your Papers Online) It can be said to be unstructured big data of limited use in raw form.
How Big Data Analytics Work and Key Technologies
There is no specific technology that enables the functioning of big data analytics. Several technology types work together in attaining the most value from the fed information. some of the key players include cloud computing, data management, data mining, data storage, Hadoop, in-memory analytics, machine learning, and text mining. Cloud computing is a subscription-based model of delivery. it offers fast delivery, scalability an IT efficiencies needed for big data analytics (Ciampi et al., 2021). It gets rid of many physical and financial barriers to allow the aligning needs of IT with evolving business goals. that is why it is effective in organizations of all sizes.
In data management, the required data should be of high quality and should also be well governed before it is analyzed. It is essential to have processes that are repeatable now that data constantly flows in and out of an organization. that helps in building and maintaining data quality standards. A master data management program should then be established once data is reliable to get the enterprise on the same page. Data mining technology assists in examining large data amounts to discover data patterns. The information can further be used to analyze data in finding answers for complex business questions. Data mining software helps to sift through all noises in data and to identify that which is relevant. It is also used to assess likely outcomes when that information is used and accelerate the pace of making decisions that are informed.
Data storage which includes data warehouse and data lake, is able to store vast amounts of both unstructured and structured data. That enables business users and data scientists to access and use the required data. Some of the unstructured data is like images, streaming data, social media content and voices. Hadoop is an open-source software framework that facilitates data storage in large amounts. It also facilitates running parallel applications on commodity hardware clusters. The constant increase in data volumes and varieties has made Hadoop a key technology for doing business. Hadoop’s open source framework is not charged and uses commodity hardware in storing and processing large data amounts.
Machine learning is a subset of Artificial Intelligence whose work is training machine how to learn, enabling it to quickly and automatically produce models responsible for analyzing bigger, more complex data and delivering more accurate results in a fast speed. Text mining technology enables analyzing of text data from comment fields, books, the web and other sources that are text-based in uncovering insights that had not been seen. Text mining utilizes both natural language processing and machine language technologies in combing through emails, competitive intelligence, blogs, surveys, twitter feeds and other documents in analyzing large data amounts and discovering new term relationships and topics.
In conclusion, big data analytics and Hadoop were firmly rooted on the ground from 2011 where the organizations and the public. big data analytics helps in quickly analyzing large data amounts obtained from different sources, in various formats and types. It also helps in making decisions that are better informed for an effective strategy. That improves the supply chain and other areas of strategic decision-making. Some of the challenges that come with the use of big data analytics are like data insecurity due to complexity of big data systems. Accessibility of data is another challenge where storage and processing gets complicated with large data amounts. There is continued use of big data analytics as the key technology driving digital transformation.

References
Ciampi, F., Demi, S., Magrini, A., Marzi, G., & Papa, A. (2021). Exploring the impact of big data analytics capabilities on business model innovation: The mediating role of entrepreneurial orientation. Journal of Business Research, 123, 1-13.
Popovič, A., Hackney, R., Tassabehji, R., & Castelli, M. (2018: 2024 – Write My Essay For Me | Essay Writing Service For Your Papers Online). The impact of big data analytics on firms’ high value business performance. Information Systems Frontiers, 20(2), 209- 222.
Hariri, R. H., Fredericks, E. M., & Bowers, K. M. (2019: 2024 – Online Assignment Homework Writing Help Service By Expert Research Writers). Uncertainty in big data analytics: survey, opportunities, and challenges. Journal of Big Data, 6(1), 1-16.

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