need 3 pages

Big data analytics and machine learning are two related concepts, which have been getting great attention and value for businesses. The proposed research sets out to explore the benefits of big data and machine learning, as well as the potential demerits. A qualitative research models proposed, where data would be gathered form experienced individuals, who will give relevant response for the research questions. The findings will show that big data analytics drive data-driven decision making and business functions, while machine learning bolsters data mining and making sense, so as to deliver more value for businesses.

NO CPT

Table of Contents Abstract 2 Relationship with CPT 3 Introduction 5 Problems Statement and Justification 6 Research Scope, Aims and Objectives 7 Research Questions and Hypotheses 8 Literature Review 11 Proposed Solutions and Methodologies 15 Proposed Research Design 15 Proposed Data Collection and Analysis Process 15 Results and Analysis 17 Proposed Work Plan for Research 18 Conclusion 18 Bibliography 19

This topic is worth investigating since we are in the age of information, where large data amounts are being generated by consumers, organizations and suppliers. This data can be overwhelming, but when its value I realized, it is a priceless treasure. In order to realize the worth of the data, people should be educated about the value, including the utilization of machine learning and determining technologies to analyze and utilize the big data. The artificial intelligence that emanates from the machine learning process can be leverage to solve some of the challenges ta firms face, such as understanding the real market trends, as well as predicting demand so as to control supply and ensure optimal business planning. Machine learning is related with the author’s college major, which is Grad 699, and it relates to computing technologies that have continued to revolutionize all sphere of human life. The motivation to investigate the topic of machine learning as it relates to the contribution to business intelligence and success, in view of data mining and big data, was informed by the fact big data is increasingly becoming a powerful tool that enables firm to understand the expansive data at their disposal, and make informed decisions towards success. The proposed research will search for primary and secondary data about machine learning, in an effort to demonstrate its utility in business, as well as the pertinent shortcomings.

The problem that the proposed research will focus on is the extent to which machine learning contributes to the businesses’ capacity to mine data, and leverage the large amounts of data to make smart, data-driven decisions. On the same note, the study will include the consideration of the shortcomings that abound when utilizing machine learning and the big data to inform management decisions. The knowledge of the benefits and challenges will lay the foundation or understanding the way forward for the artificial intelligence algorithm, with a focus to providing evidential recommendations, identifying areas that may require further research attention. In that regard, the study that is being proposed will employ a multi-dimensional approach, in order to gather comprehensive data about machine learning, both from primary respondents such as IT experts, as well as from documented information, to raft an evidential description of machine learning in the context of effective business management. The research will to that end answer the research questions, and yield data to support the recommendations that will be given. The findings from the study are also expected to help in testing the hypotheses outline earlier in this report. Upon completion, the proposed research outcome are expected to show that machine learning is an essential technology that facilitates data mining and artificial intelligence, and that the resulting business intelligence has important implications for business management, especially in light of the growing significance of big data analytics. To that end, the significance of the proposed research to the business management promotion and enhancement cannot be overstated, in view of the reach goals of the proposed study.

As big data analytics continues to gain momentum, the adoption of the model has continued to become imperative. The organizations are collecting loads of information routinely, and often the overwhelming data is not turned into more valuable information, especially when there is a lack of a bid data management approach. Managing the big data volumes is therefore important, and there are emerging tools which can be leveraged to manage it. Some of those tools which have been used conventionally include the data warehouses, and database management systems. However, the large volumes of data requires that data analysts and managers have better data management tools in order to be able to understand and appreciate the data. To this end, big data analytics is essential in business. Machine learning, on its part, is a form of artificial intelligence, in which computers are programmed to behave like humans, albeit with a better programmed capacity to perform specific tasks more accurately and without the limitations of humans. To this end, the proposed research is desired to be used to explore machine learning with regard to how it may be leveraged to bolster big data analytics. In that regard, the research intends to design a triangulated data collection approach that helps in gathering information regarding the particular ways in which big data and machine learning can be leveraged for the enhancement of big business performance. The scope of the research is scoped within the context of business performance, especially in view of the modern highly competitive business environment, where the digital era has handed businesses many tools to an hence their competitiveness, and the more competitive business has to devise ways for deriving more value from the large amounts of data they have. The information from the study is intended to provide practical directions for business managers and data controllers, such that they apply the knowledge in designing the best approach to adopt in optimizing data management and analysis, by taking advantages of the machine learning paradigms.

The research questions which the evaluator plans on investigating are presented in this section. These questions were developed based on preliminary research of the existing literature, with the aim of identifying research gaps, and crafting the questions in such a manner that answering them would contribute to the information gap reduction, and more importantly provide direction for practical application in the context of business management. The research questions which the proposed research will attempt to gather data to answer include the following.

1. What is the role of big data in influencing business performance in the current digital age and data-driven business environments?

2. What is the role of machine learning in determining business performance in view of leveraging the large volume of data for the benefit and value of the business?

3. How can businesses make the most optimal use of the big data analytics and machine learning models to enhance performance and overcome pertinent challenges associated with the overwhelming data volumes?

4. Are there some notable businesses that have already embraced big data and machine learning in their operations? If yes, what are the outcomes in view of the desired aims of improving business performance by taking advantage of the data that s generated every time?

5. What are the factors or situations that hinder the optimal value addition to business by using machine learning and big data analytics?

6. How can the challenges that abound and limit the optimization of machine learning and big data in view of enhancing business performance be overcome?

In view of the research questions, the proposed research will be done based on the assumption that big data and machine learning can be useful and beneficial to business that have the will and capacity to leverage the technologies. Specifically, the proposed research will be premised on the assumptions of hypotheses stated below.

1. Machine learning has a key role to play in enhancing business performance in modern businesses, by enhancing data analysis and visualization for interpretation.

2. Big data analysis has an important role to play in enhancing business performance in modern businesses, by enhancing data analysis and visualization for interpretation.

3. Business organizations can explore ways for optimizing the large volume they gather every day, so as to convert them into more valuable tacit assets to drive business performance.

4. The businesses that have embrace machine learning and big data analytics have realized positive results on their performance due to the power of big data when turned into more valuable information that can influence smart decision making.

Machine learning is based on the theory that a computer can learn new information, based on unique programs, after they are exposed to new data. A such, without anyone entering any more information or commands to the computer, the computer is capable of learning new insights, and utilizing the to make smart decisions, such as identifying a face form a crowd of photos. Therefore, with machine learning, all a person needs is to have a computer that has pattern recognition capabilities, programmed to function independently. It is then able to employ artificial intelligence to learn new data and execute commands, even without any additional input. Moreover, the idea behind machine learning is that complex mathematical calculations can be executed, which help in the programming process in order to arrive at the machines. The concept of machine learning is closely related to big data, sic machine learning entails the use of big data to program computers to make sense of huge data volumes, and aiding the simplification of information to a form that is easy to understand and use in making decisions (Morton, Runciman, & Gordon, 2014).

The value of machine learning in business and commerce originates from the fact that the big data, which has been gaining popularity in the recent past, can help people in business to make predictions, understand trends, and make data-driven decisions, reducing the odds of failure. Aside from the business concept, daily life activities can be contextualize t machine learning, where computers artificial intelligence is used to perform everyday life activities, including driving ad cleaning the compounds, or taking children to school and coming back to wash utensils (Morton, Runciman, & Gordon, 2014). This underlines the fact that machine learning has a high potential for use in a varied range of activities, including in management.

Machine learning and big data combine to deliver high levels of accuracy and efficiency in business management. On this note, big data analytics has become one of the most important uses of application virtualization and distributed systems (Morton, Runciman, & Gordon, 2014). These are intended to enable the firm to deliver more value at competitive prices. Previously, big data was left alone, as it was too much for people to identify and understand it. However, today, there are better tools for doing that, so the high data algorithms are finding their way to the debate room. In big data, useful information patterns are hidden therein, with the main issues being that if a business can leverage the data and derive the information patter, it can engage in successful machine learning, and use the information to enhance its artificial intelligence utilization (Al-Jarrah, O., Yoo, P., Muhaidat, S., Karagiannidis, G., & Taha, K., 2015). The relevance of big data in machine learning is that big data analytics have paved the way for machine leaning, which translates to business intelligence in the business management (Morton, Runciman, & Gordon, 2014).

Business intelligence and artificial intelligence, all of which are central to machine learning, have been sown to be effective in aiding decision making. Machine learning in the context of big data is not all about learning new insights and gaining new data; rather, more emphasis is on the finding and analysis of that data, so as to continue aiding the intelligence decision making process. Machine learning utilizes a variety of other distributed and transformative technologies, including cloud computing, data mining and statistics. Once the machine learning data is obtained, a wide range of processes kick off, in an effort to make sense of the data. Moreover, since big data and machine learning are not all about processing he data volumes, it is essential to consider how firms can create value from the large amounts of data that they have at their disposal. The type of the data does not matter as far as value is concerned. For example, structured, unstructured and semi-structured data can still be made valuable, if it is stored and analyzed properly and using the right channels (Al-Jarrah, O., Yoo, P., Muhaidat, S., Karagiannidis, G., & Taha, K., 2015).

Machine learning in the context of a business has a lot of value to business managers. For example, machine learning and big data together have been shown to positively influence risk management decisions and operations, change management, adoption of bet parties in line with strategic business plans, as well as engagement in benchmarking activities. Anderson et al. (1983) made interesting claims about machine learning, which have come to be true today, as far as the value and process of machine learning is concerned. The author notes that machine learning was a way of firms to gain new knowledge, that is learning, and these new ideas, which could be converted to data-driven decisions, would have far reaching positive implications for the firm. Andrieu, De Freitas Doucet & Jordan state that Makov chain Monte Carlo (MCMC) machine learning is one of the most effective algorithms that has found wide applications in physics, computer science, econometrics, statistics and bioinformatics for decades (Andrieu, De Freitas, Doucet, & Jordan, 2003). The rationale behind MCMC machine learning is that it has a high potential of processing large volumes of data in terms of optimization and integration, hence find wide application in the management of big data.

Moreover, Freitag underscores the fact machine learning was closely related to big data, since it had been found to be effective in isolating or extracting data and meanings from informal domains (Freitag, 2000).The relates to being presented with huge volumes of data, which otherwise are overburdening and senseless. However, with machine learning, it is possible to extract the data that makes sense, based on an established algorithm or criterion. To this end, machine learning is an essential tool for business intelligence and artificial intelligence, which empowers modern business to leverage the data they collect every day, and become successful. As such, machine learning eliminates the need for the people to worry about gathering huge volumes of data and information, since the information could be handled. A good example to this end is the use of machine learning to solve the problem of oil spills (Kubat, Holte, & Matwin, 1998).

In that case, machine learning enabled the process of managing the varied data and understanding the information that was relevant in managing the crisis, and I was successful in the end, underscoring the fact that machine learning and business intelligence could be leveraged to solve many challenges that firms meet. On the same note, Sebastian demonstrated that machine learning was useful in analyzing and categorizing automated text, which existed in the form of large amounts of data (Sebastiani, 2002). With machine learning algorithms, the automated texts could be analyzed and simplified, and they could be used to make decisions while they are more sensible following the algorithmic manipulation. Machine learning is therefore an important technology, which has already been shown to be effective in managing large data volumes to make sense of them and aid the decision making process (Gupta, 2016).

The proposed research will be implemented based on a qualitative approach, so as to gather detailed and in-depth qualitative data. Qualitative data involves the perceptions, views, experiences and expert input of research participants, whether such is primary as in the form of direct responses such as is done in an interview, or in documented form such as in the form of existing research. The choice of this research approach is justified for the proposed study, since it suits the need for the researcher to obtain a wide range of data to explore the issue of big data and machine learning as they apply in business performance enhancement efforts. The business management exploration using qualitative approaches is expected to yield a deeper understanding of the concepts. To this end, two data collation approaches will be deployed to obtain data. These include interviews and documented data in the form of existing literature. The proposed data collection approaches have been justified in previous research to be effective in aiding in the collection of quality data. The design of the research questions forth interviews will be tailored to attract quality responses. Interviews will be done through the phone, while secondary data will be read and analyzed from the existing sources, such as journal articles and conference papers. The data obtained from the two sources is anticipated to be relevant and in appropriate amounts, so as to ensure that the research questions are answered conclusively.

The researcher proposes to use the two data collection approaches to gather quality data. To this end, the interviews will be conducted via phone. That is, the interviews questions will be developed prior to the interview day, and the research will use the interview tool to ask questions. The responses obtained from the research participants will be recorded using suitable technologies, such as voice recorders. Phone interviews will be conducted due to the limitations of time and travel logistics, and the fact the researcher expects to obtain a team of expert respondents within the Philadelphia region where the university is located. In addition to the primary questions set out in the interview sheet, additional questions will be asked to the interviewee in responses to the answers given and with a view to seeking clarification and gaining more insights.

Moreover, data will be collected from documented sources. The researcher will use specific keywords, such as machine learning” big data,” big data analytics,” machine learning intelligence,” business intelligence,” artificial intelligence,” benefits of machine learning to business,” and such others to search the web, generally, as well as focus on specific computing journals, such as the IEEE, ACM Computations, and an array of others.

The responses obtained from the proposed study will be relevant and answer the research questions. In that regard, it is expected to be shown that big data and machine learning are related, and they can help a firm to develop better operational models and decision-making practices (Luo, G., 2015). All of these would translate to an enhanced competitive advantage in the market. In the modern business settings where there is stiff competition and trend and changes are occurring rapidly, it is essential that decisions exercise prudence when making decisions such as marketing and production. With big data analytics, it is possible to overcome the limitation of the human mind by utilizing the large data sets to make interpretations and conclusions, which may then be applied in making conclusions and decisions about the data (Langford, J., 2012). In addition, since machine learning occurs through the use of the environment and the data and information within it, it may be said that the practice can help in learning the business patterns, ad making inferences and predictions, which can position the business in a strategic point within the market. The underpinning issue is to ensure that machine learning and big data are combined to deliver more data value for firms (Hu, 2017).

The table below gives the proposed work plan, which will be used to execute the suggested study.

The proposed study seeks to explore the role that machine learning and big data can play in business organization’s development. The two technologies entail the utilization of large data sets to act or guide action in the most appropriate approach. The study proposed in this proposal document is relevant for business managers and business executives, as well as employees of firms providing relevant computing services.

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