Running head: USING BIG DATA AND DATA ANALYTICS IN BUSINESS DECISIONS Strategies to Implement and Integrate Big Data and Data Analytics to Improve Business Decisions Uday Gundluru IST 8101 Wilmington University 1 USING BIG DATA AND DATA ANALYTICS IN BUSINESS DECISIONS 2 Table of Contents Methodology………………………………………………………………………...…………….3 Research Design…………………………………………………………………………………...4 Participants………………………………………………………………………………………...5 Instrumentation………………………………………………………………………………...….6 Procedure…………………………………………………………………………….……………7 Data Processing and Analysis…………………………………………………………………….8 Summary………………………………………………………………………………………….8 References……………………………………………………………………………………….11 USING BIG DATA AND DATA ANALYTICS IN BUSINESS DECISIONS 3 Methodology Due to the development of technology as well as the increasingly large amounts of data which flow in and out of businesses on the everyday basis; there are a requisite for fast as well more efficient methods to analyze this type of data. Piling large amounts of data does not guarantee competent decisions at the right period. These information sets no longer allow analyzation of through traditional data management and analytic methods thus allowing the use of new practices as well as tools to analyze the big data as well as the required designs to be used for storing and handling data. Due to the development of big data, effects on the lot ranging from data itself to its processing to the last final removed decisions should be considered (Power, 2014). Organizations should be able to manage the place and how big data should be stored once assimilated. Outdated methods of the organized data storage as well as data retrieval methods include data marts, data warehouse, and relational databases. Data is uploaded to stowing devices from the working data storages by the use of Transform, Extract, load or Transform ETL. The tools enable removal of data from outside sources altering the data to fit in its working needs where the data is finally loaded into the database of the data store of the organization. The information is thus gutted, cataloged and transformed before it is made obtainable for data mining as well data analytical purposes. Contrary to the new methods, big data environments allow for MAD (Magnetic, Agile and Deep) study skills. MAD analysis features are different from features found in the traditional Enterprise Data Warehouse (EDW). The Traditional Enterprise Data warehouse approaches always dishearten the use of novel data sources not unless they data sources are cleaned and integrated (Erevelles, Fukawa & Swayne, 2016). As a result of data ubiquity, in modern data storage, the data surroundings need to be USING BIG DATA AND DATA ANALYTICS IN BUSINESS DECISIONS 4 attractive thus be able to attract all the sources of data, and this is regardless of data quality. Due to the growing number of sources of data and complex feature of data analyses, the significant data stowage should permit data analysts to be able to produce as well as acclimatize data speedily efficiently. The active database is required due to its feature on its intellectual and physical content that can familiarize in sync with fast data development. Lastly, because existing data evaluation uses sophisticated statistical methods, the data analysts need to learn massive datasets though penetrating up and down big data. Data repository should also be broad as well as serve a complex algorithmic runtime appliance (Silverman, 2016). A lot of people in the United States get employed through online. Hiring, promotion, management, and rewards decisions are all made through online. Most recruiters around the world access most of the information about potential workforce through social, media, online databases, online tests, and employment records as well as even contest results. The information that recruiters access on these sources helps them to assess leadership qualities, soft and hard skills as well as thinking skills (Silverman, 2016). Consumers are connected each and every day in digital platforms which makes an opportunity for a marketer to access the consumers through these digital forms. The information found in these devices is essential to the marketers for they are able to target their consumers well. Telecom providers always use the big data methods so as to reduce customer mix. By the use of big data technologies as well as analytics methods, the marketers are able to mine, combine as well as analyze both types of data easily (Erevelles, Fukawa & Swayne, 2016). Research Design USING BIG DATA AND DATA ANALYTICS IN BUSINESS DECISIONS 5 From the theoretical background as well as the overview of the literature on business improvement, it is essential to find out ways in which the critical resources, as well as capabilities, are relevant in big data analytics. It is essential to find out what strategies should business use in leveraging big data and data analytics to improve business decisions, marketing and hiring decisions (Kim, Trimi & Chung,2014). Conducting a literature review was done to build blocks of significant data analytics capability as well as possible hindrances to strengthen a business value. Coming up with research design helps in identifying the central concepts which underlie the theories that are used in the context of significant data; the research tried to come up with an explanation on the impact of big data and also the concepts that which firms have initiated in big data projects (Demchenko, De Laat & Membrey, 2014). The study will look use the semi-structured interview to collect data. The reasons for using this method to obtain data are because it is a two-way communiqué where the interview will help in looking for thorough as well as profound information. The second reason for using interview is because it asks questions that are semi-structured which aims at seeking clarity as well as gathering good data and enquiring follow up questions. The litheness in the semistructured interview helps in producing unexpected intuitions. The whole research procedure will be alienated into four stages which include, Questionnaire strategy, data gathering, data reporting as well as analyzing (Demchenko, De Laat & Membrey, 2014). The questionnaire used was divided into three sections where one section consisted of questions on big data on improving decision making, the second section consisted questions on marketing decisions and the last part contained questions on hiring decisions. Participants USING BIG DATA AND DATA ANALYTICS IN BUSINESS DECISIONS 6 Data collection will be done on 30 interviews got from experts from different backgrounds in regard to IT. Respondents will include people such as IT managers and IT directors together with Chief Information Officers, senior managers, IT consultants, marketing agents, marketing managers and human resource managers. The involvement of BI will be considered along with Business Development managers. The interviews will involve Audio records where the interviewees will permit transcriptions and later transcribe the Audios (Demchenko, De Laat & Membrey, 2014). The sample of the study came from several firms who demonstrated that they understood the big data analytics. This didn't matter if they had started or had invested considerably in effort and time in Data analytics processes. Selecting the sample involved selection of medium-sized to large size firms, and this resulted from the complex projects that were being undertaken by the companies (Hashem et al., 2015). The elaborate plans were one of the primary sources of getting the better understanding of spectrum or the needs of big data project. The firms chosen, operated in a competition as well as a highly dynamic market which made them adopt the use of big data to remain competitive and grow as a business. These means that the efforts involved in developing robust organization capabilities through the means of big data were hastened. The selection of companies was made regarding the type of industry which was within the given boundaries. The aim of selecting these industries was to do an in-depth analysis which will enable the comparison and contraction of possible differences. Instrumentation The use of interviews was the efficient method in gathering rich, and empirical information. The reason for using it because the information is from respondents who come from USING BIG DATA AND DATA ANALYTICS IN BUSINESS DECISIONS 7 the firms. Ways to mitigate biases were used in the research. Data was gathered from both primary as well as secondary sources. The primary source included direct interviews from the IT managers and IT directors together with Chief Information Officers, senior managers, IT consultants of the companies on regard to their experience on the effect of big data which their business had taken. Marketing managers and human resource managers were also involved in the study. Their attitudes, beliefs as well as opinions were asked and measured. The interviewers used semi-structured interviews with the respondents who were fully involved in big data project (Power, 2014). The interviews were done face to face in a conversational way which opened a discussion on the nature of the business as well as continuing forward with the themes of the interview strategies. Whenever necessary, the questions were being clarified by the interviewer to encourage more accurate answers from the respondents. The discussions between the interviewers and interviews were recorded and later transcribed for later analysis. To corroborate the statements of the respondents, the information published about the companies in annual reports, websites and third parties which included online articles. A semi-structured case study protocol was further used to investigate cases as well as collecting data (Silverman, 2016). Procedure The semi-structured questionnaire was determined by extensive literature review in identifying critical issues which can help in answering research questions. The literature analysis led to an identification of several issues which were used in creating the interview questions. The questions included the implications of Big Data analytics for the better decision, combination of social media data with actual time's sales data to enable the analyzation of marketing campaign applied to consumer sentiment as well as purchasing behavior and hiring of new employees (De Mauro, Greco, & Grimaldi, 2015). The questionnaire was designed to contain two parts with 3 USING BIG DATA AND DATA ANALYTICS IN BUSINESS DECISIONS 8 subsections where the first part of the subsection consisted of employee’s details like job title, firm's size, type, and industry involved. The other part consisted of 20 questions which cover several issues in gathering a thoughtful of the insinuations of the big data analytics on the business improvement, hiring improvement and marketing improvement consecutively. Data processing and Analysis The empirical analysis was done through an iterative reading, coding as well as interpretation of the transcribed interviews as well as observation notes of the case studies done. The investigation was done in phases. The first phase involved identification and isolation of a lot of concepts by theoretical foundations discussed in the study (Wang, Gunasekaran, Ngai, & Papadopoulos, 2016). The second phase included the Standardized method which was used in quantifying the characteristics. Logically, the firms showed that they were able to manage the big data analytics and proved that they could present a firm-wide capacity in both utilizing and leveraging big data technologies in the direction of strengthening organizations abilities (Wu et al., 2014). Summary Big Data analytics contributes to a big opportunity in enhancing business value as well as development. Applications of Big data in business intelligence improve decisions making abilities, allow faster decisions were making process, enable the better understanding of customer needs, reduce customer complains as well as improve staff ‘s hiring process productivity and efficiency. Big data analytics has unprecedented opportunities as well as benefits to any business. In the recent days of the business world, the vast amount of data is being produced on a daily basis. Within the information delivered daily, essential details, as well USING BIG DATA AND DATA ANALYTICS IN BUSINESS DECISIONS 9 as patterns of knowledge, should be removed and used. Big data analytics can be useful in leveraging business adjustment as well as enhance the business decision making through the application of progressive analytic methods on big data and also showing hidden understandings as well as valued knowledge (Wang et al., 2016). To be able to know the effects of big data analytics on improving business decision making, questions on analyzing data analytics concepts were being asked and their significance to decision making. Characteristics and importance of big data were discussed. Examination of analytic methods and tools were also examined. There was a discussion of more analytics techniques which were further considered in the research. If analytics to big data are applied, valuable data can be got from the exploited and removed in enhancing the decision making as well as supporting informed choices. Several areas where the big data can help as well as is in making decision making was looked at, in which the big analytic data was found to provide vast horizons of chances in several requests and areas. These areas include customer intellect, supply and chain management, and fraud detection. The benefits of big data analytics can be helpful in several sectors as well as industries like the healthcare, telecommunication, hotel, and also manufacturing among others. The research has enabled so many people in the researched organizations to be able to apply the big data tools, techniques, and approaches. This provides the industries with ideas of what they can do to offer advanced solutions for big data analytics to support decision making. Lastly, slight novel technology can be appropriately used to bring numerous potential benefits as well as innovations to the company. Big data is a complex to deal with for it needs good storage, mixing, cleansing, alliance, analyzing and processing. Big data analytics has a great importance in the era of the data overflow as well as provide excellent benefits as well as benefits to the decision makers in areas USING BIG DATA AND DATA ANALYTICS IN BUSINESS DECISIONS 10 of hiring employees and marketing products. The use of dispersed systems and Massive Parallel Processing (MPP) databases in delivering outstanding inquiry recital and well as podium scalability to an anti-relational database should be used. Development of non-relational databases like the SQL was developed to store and manage unstructured and non-relational data. NoSQL databases aim for big mounting, simplified application development, and data model flexibility as well as deployment. The reason for using NoSQL databases is the fact that compared to the national databases, they separate data management and the data storage allowing for data organization tasks that are to be written in the request layer in its place rather than having written in the databases particular languages (Power, 2014). Human resource managers need to store and access information about their employees. Due to the increase of data to be stored, the human resource departs are using modern ways in storing the information. Big data means big opportunity to the decisions makers, human resource department and marketing departments of a company. USING BIG DATA AND DATA ANALYTICS IN BUSINESS DECISIONS 11 References De Mauro, A., Greco, M. & Grimaldi, M. (2015, February). What is big data? A consensual definition and a review of key research topics. In AIP conference proceedings (Vol. 1644, No. 1, pp. 97-104). AIP. Demchenko, Y., De Laat, C. & Membrey, P. (2014, May). Defining architecture components of the Big Data Ecosystem. In Collaboration Technologies and Systems (CTS), 2014 International Conference on (pp. 104-112). IEEE. Erevelles, S., Fukawa, N., & Swayne, L. (2016). Big Data consumer analytics and the transformation of marketing. Journal of Business Research, 69(2), 897-904. Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A. & Khan, S. U. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information Systems, 47, 98-115. Kim, G. H., Trimi, S. & Chung, J. H. (2014). Big-data applications in the government sector. Communications of the ACM, 57(3), 78-85. Power, D. J. (2014). Using ‘Big Data’ for analytics and decision support. Journal of Decision Systems, 23(2), 222-228. Silverman, D. (2016). Qualitative research. Fourth Edition, Sage. Washington DC. Wang, G., Gunasekaran, A., Ngai, E. W. & Papadopoulos, T. (2016). Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International Journal of Production Economics, 176, 98-110. USING BIG DATA AND DATA ANALYTICS IN BUSINESS DECISIONS 12 Wu, X., Zhu, X., Wu, G. Q. & Ding, W. (2014). Data mining with big data. IEEE transactions on knowledge and data engineering, 26(1), 97-107. Running Head: PROJECT TITLE Strategies to Implement and Integrate Big Data and Data Analytics to Improve Business Decisions 1 2 PROJECT TITLE Results The results section is a major section of your research paper and, as such, is preceded by a level one heading formatted in accordance with the requirements in the Publication Manual entitled “Results”. The results section is intended to provide a detailed discussion of the data collected during your research effort (including the manner in which it was collected and from whom it was collected). This section also provides a detailed discussion of the methodology you used to analyze the data you collected, as well as a discussion of the key analysis results. This section should not include any discussion of your evaluation or interpretation of the data or analysis results. Refer to the Publication Manual for additional guidance regarding the contents of the results section of your paper. Discussion The discussion section is a major section of your research paper and, as such, is preceded by a level one heading formatted in accordance with the requirements in the Publication Manual entitled “Discussion”. Having presented the results from your research effort in the preceding results section, you are now in a position to discuss your evaluation and interpretation of the implications of the data you have collected, especially with respect to how the data and analysis applies to proving or disproving your hypothesis. This section of your paper is where you examine, evaluate, interpret, and qualify the results of your research, as well as draw inferences from them. This is an extremely important section in your research paper inasmuch as it demonstrates your critical thinking skills with regard to applying your research findings to creating a solution to your stated problem and answers to your stated research question(s). Refer to the Publication Manual for additional guidance regarding the content of the discussion section of your research paper. 3 PROJECT TITLE Conclusion The conclusion section is a major section of your research paper and, as such, is preceded by a level one heading formatted in accordance with the requirements in the Publication Manual entitled “Conclusions”. This section should clearly and concisely summarize your key findings, as well as discuss the benefits that will result from having conducted your research. The conclusion section must clearly and concisely discuss whether you proved or disproved your hypothesis. A good conclusion clearly brings the paper to closure. This is also the section of your research paper in which it is appropriate to mention if there is further study that you believe should be conducted, as well as identify the specific areas in which you think the additional research may be needed, based upon your research findings. Use a hard page break at the end of the conclusions section. 4 PROJECT TITLE References 1. 2. 3. 4. 5. 6. 7. 8.
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