Mixed Method Research PDF

Running head: MIXED METHOD RESEARCH 1
Mixed Method Research
Student Name
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1. Introduction: Industry
Big Data is an important tool that has been harnessed by major industries within the
country and internationally. Big data can be described as a set of large data that is derived from
various business process and platforms that can be analyzed to enhance the decision-making
process. With the increasing trends in the application of big data in industrial manufacturing,
technology together with other technologies such as artificial intelligence has increased the
productivity of many manufacturing industries. This has enabled many industries within the
international business context to develop policies that utilize the data output from the big data
analysis to improve on the industrial processes. In addition, through the establishment of various
data factories, the manufacturing industry is processing big data sets to understand the patterns,
trends and performance evaluation. This leads to process automation and increases efficiencies in
operations or productions. Big data is normally described based on various aspects such as the
types and nature of the data, and data output can be drawn into texts, images, videos, and audios
that are useful for the decision-making process. This implies that the output of the data analysis
process enables the evaluation, prediction, and planning of future efficiencies improvement. The
following paper, therefore, describes the opportunities and challenges that are presented by big
data used in industry.
Examples of big data
Various online businesses that attract numerous views and site visits are examples of
industry where big data has taken root. Some of the international companies that are involved in
big data analysis include Netflix, IBM, Microsoft, Apple, Google, and many other companies.

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As shown in the figure below, various types of data and complex data play an important role in
big data analysis (De Mauro, Greco & Grimaldi, 2016).
Figure 1: Global Data System
There are many different companies that are involved in the mining of online data that
are analyzed to determines the data patterns.
The big data process
Big data process is where the data from various media such as websites, social media and
other customer interactive platforms are collected using various software such as industry big

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data 4.0. this data is analyzed to drive patterns and readable results. An example of such
companies is Netflix that mines data as shown in the figure below (Hilbert & López, 2011).
Figure 2: How Netflix Mine Data
These data are obtained and take the form that is shown in the figure below. These data
can be analyzed to deduce conclusive customer behavior improvement (Wedel & Kannan, 2016).

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Figure 3: Example of Data obtained by Netflix
2. Big data 5V
Big data for industry composes of large data sets that are drawn from various aspects of
the industry that enhances the decision-making process. This data has various characteristics that
are important for understanding and analyzing the available data in international business. Big
data can be described using various characteristics that are termed as the 5Vs when applied in
international business. These 5Vs include Velocity, Volume, Value, Variety, and Veracity (Marr,
2014).
Velocity

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Big Data characteristic of velocity can be defined in terms of the speed at which these
large data sets are processes to deduce a trend. The velocity of the big data enables the
processing of data to meet the international business demand and real-time result. Industry data
requires a high speed of processing owing to the nature of data and the volume of data that
proves complex (Lee, Bagheri & Kao, 2014).
Volume
Volume, on the other hand, is the characteristic of big data that shows the quantity of data
that can be generated at a particular time. Industry data is considered one of the high volume data
that is complex to analyze in order to deduce conclusive output. Industries within the
international business context have a huge amount of data that can be processed to realize its full
potential when understanding the value of customer patterns (Everts, 2016).
Value
Value of the larger datasets that are analyzed under the big data technology matters as
this shows the usefulness of the output of the data analysis. The industry data, especially the
manufacturing industries, require high processing owing to the value of data in the evaluation of
the performance of the industry. The value is characteristics of big data that has a connection to
the variety of data since the nature and type of data determines the value of the output. For
instance, those data that are derived from market trends has a high value when used to enhance
online marketing of international businesses (Mayer-Schönberger & Cukier, 2013).
Variety

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Big data can also be described using its nature and type that also show the origin of the
data. The type and nature of the data that is being analyzed are tied to the various data output
types that include texts, videos, audio, and images. The type and nature of data enable the
analysts to link the missing data with those available for good prediction. One example of the
type of industry data include the production output data and this is useful in the understanding of
trend in productions (Lee, Bagheri & Kao, 2014).
Veracity
Veracity is another aspect of big data that shows the importance of quality of data that
enable the data analysis. Industry data need to be of high quality as this reduces the errors that
may occur in an automated process within the industry. The quality of data determines the
accuracy of the analysis since some data that are ‘dirty’ are not accurate and may give undesired
output. For instance, customer engagement data for international requires high-quality data that
can show the exact pattern of customer behavior (Reips & Matzat, 2014).

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Figure 4: Data characteristics
3. Opportunities
Big data has increased business opportunities that range from individual, business or
organizational. The opportunities also cut across all industries within the international business
context leading to the improvement of various process and productions. Big Data has several
opportunities within the current business environment as its usability within business increase
with time. Some of the opportunities of Big Data include enable increasing customer
engagement; enhance marketing, prediction of certain patterns of a specific problem,
identification of security issues, and business modeling (Reinsel, Gantz & Rydning, 2017).
Firstly, Big Data opens the door for predicting certain patterns that are critical for
business phenomena. Certain patterns of issues such as security and customer’s engagement are

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used to predict patterns. Data for various customer patterns and trends are used to predict the
future trend or customer behaviors. In addition, the nature and type of data enable the company
management to be able to make a decision on future trends in business (Goes, 2014).
Secondly, the Big Data enhances the marketing of company products to customers as the
company can use data to understand the customers purchasing or behavior patterns. The output
of the data analysis especially on the marketing data derived from online platforms for an
understanding of marketing behaviors. The industry has the opportunity to use customer
engagement data to determine the taste, behavior, and requirement of the customer that they
intend to sell the products manufactured (Couldry & Turow, 2014).
Thirdly, Big Data enhances the business modeling process that is used by management to
increase its decision-making process. There is various modeling software that uses the trends
derived from big data to use for modeling business. This is important for business process
automation that enables management to speed up various internal business processes. Business
modeling has helped many international industries to determine the volume of the output, work
with efficiencies and predict the trend in the production process. Moreover, business modeling
also assists the management to make a decision on the most likely importance of process
automation (Reichman, Jones & Schildhauer, 2011).
Fourthly, the Big Data open opportunity for business and organizations to use the
available data and patterns available in data to predict security risk. Many organizations such as
health care organizations have used data on infectious diseases to predict the spread of such
diseases. Similarly, many international industries that handle a huge number of customers can
use data to identify the likely hood of some phenomenon to occur within the system especially

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problems that can interfere with service delivery to customers. For instance, security data can be
used to identify some areas that have security linkages that can be risky (L’Heureux, Grolinger,
Elyamany & Capretz, 2017).
Lastly, data can be used to test a hypothesis in order to automate business processes.
Automation of business processes is important for contemporary business since this enables
efficient control of the internal process. Many industries currently are automating their
production process in order to test and manufacture various products before selling them to
customers. In addition, many companies with strong research and development are utilizing big
data to test the hypothesis on the trend before actually implementing them (Breur, 2016).
4. Challenges
There are many different challenges that affect the adoption and implementation of big
data technology. Some of the challenges include scalability, lack of skills, actionable insight,
data quality, security, cost management, high investment, training of organization staffs on the
use of data, proper utilization of data. In order to understand these challenges, there is a need to
analyze two challenges (Kimble & Milolidakis, 2015).
Data quality
Data quality is an important challenge that highly affects the Big Data technology that is
currently trending in business. Storage of data in original form and quality data is a challenge
that affects the data analytic companies. Keeping low quality or dirty data is costly and
associated with duplicates, data errors and incorrect data linkage. In addition, data that are
normally collected sometimes are incomplete and full of many data errors. Linking of data sets is
an example of ways in which data error occurs making data have low quality most likely to cost

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company using this data huge amount of money. This causes data in accuracy and requires data
cleaning to ensure that data is of high quality (Karolin, Schrape, Lena & Weyer, 2018).
Data security
Secondly, data security is another concern as keeping data safe is continuously becoming
a problem to those firms using data. Data security is a particular concern at the point of accessing
data from team members. In addition, recording the histories of accesses to data stored by Big
Data requires high enhanced regulations as this also reduces access to data by unauthorized
persons. One such challenge that affects the data is the application of encryption to data stored or
transit data as there is a need to maintain the high security of this data. Authentication all team
members so as to access the data stored in another security challenge that affects the big data
hence can compromise the integrity of the data (Dedić & Stanier, 2017).
Rebuttal
Despite that challenges that affect the implementation of the Big Data in industry, the
opportunity and benefits of the Big Data suffice the challenge. Big Data is a trending
phenomenon that has proved to have far advantages to business as it helps the business
especially the management to make a decision on the likely trend and patterns in the market. In
order to harness the benefits of the Big Data, the industry needs to invest in the training of staffs
on the benefits of Big Data and how these data can be analyzed and utilized. In addition,
industries can also implement strategies that reduce data error to increase the quality and
accuracy of Big Data that are analyzed (Hilbert, 2014).

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Figure 5: Example of Big Data contribution
5. Conclusion
In conclusion, big data is an important aspect of company data that shows high prospects
when utilized to improve the performance of industries. Various types of data that are sued in big
data analysis show disparities in characteristics and quality. Big data can, therefore, can be
described based on 5Vs that include velocity, volume, variety, value, and veracity. Big data has
open doors for many different opportunities that cut across industries, organizations, and
businesses. Big data also has many different challenges that are also affecting the adoption and
implementation of big data in industries through management.

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