Expert answer:Research Paper on Big Data and Data Analytics
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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
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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
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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 Outdated methods of the organized data storage as well as data retrieval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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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of the results section of your paper.
Discussion
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results section, you are now in a position to discuss your evaluation and interpretation of the
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applies to proving or disproving your hypothesis. This section of your paper is where you
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of your resea …
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