Big Data Program Analysis Report

This report evaluates the use of big data analytics to manage the problem of data security. It outlines different types of data analytics, KPI, technology and tools, data visualisation and data governance techniques.

Big Data Program Analysis
Report

EXECUTIVE SUMMARY
Big data analytics is a concept which deals with analysing the data which is large in size but is
essential for the effective accomplishment of aim of data analysis. The current study evaluated
that data security is the problem which is commonly faced by many business organizations.
Hence, it is very important for the business to manage this problem in effective manner. The
study highlighted that use of data analytics of predictive was very helpful to business in
managing its problem. Further it evaluated that use of KPI is essential to improve performance
by setting some standard indicator. Further it examined the use of charts is best example of data
visualisation as it present data in more clear and attractive format.

TABLE OF CONTENTS
RECAP BUSINESS INITIATIVE..................................................................................................4
DATA ANALYTICS.......................................................................................................................4
Types...........................................................................................................................................4
KPI...................................................................................................................................................5
Identified KPI..............................................................................................................................5
Measurement...............................................................................................................................5
Communication...........................................................................................................................6
TECHNOLOGY AND TOOLS.......................................................................................................6
DATA VISUALISATION AND REPORTING..............................................................................7
Visualisation method and types..................................................................................................7
DATA GOVERNANCE, PRIVACY & ETHICS...........................................................................8
Challenges or issues....................................................................................................................8
Recommendation........................................................................................................................8
REFERENCES..............................................................................................................................10

RECAP BUSINESS INITIATIVE
The big data is the data which is large and is having a diverse information involving
different fields of data. With respect to big data research, the use of large data is being
implemented by the researchers. The use of big data is very assistive in solving the business
problem which the companies are facing in general (Lhatoo and et.al., 2020). The business
problem which the current plan will be identifying is related to data security. This is major
problem which almost every business is being facing currently as there is risk of managing the
privacy of data. The current analysis will assist the companies in managing and improving this
business problem. the current analysis will outline different types of data analytics along with
use of KPI. Further it will also outline different technology and tools and data visualisation and
data governance techniques. Hence, this will provide a plan that how business problem of data
security will be cleared.
DATA ANALYTICS
Data analytics is a concept which involves process of examining the data set in order to
draw inferences from the information or big data being gathered. For solving any kind of
problem it is very important for the companies to undertake and implement good and effective
data analytics technique to overcome the problem.
Types
There are several types of the data analytic techniques which are assistive to solve the
business problem and manage and interpret the data in proper and effective manner. The
different types of data analytics are as follows-
1. Descriptive- this is a type of analytic which involves answering the question relating to
what happened. In this method the data is being analysed by using the raw information
which is descriptive and only explains that whether the current problem is right or wrong.
2. Diagnostic- this is another type of data analytics which focuses in measuring historical
data against other data to identify that why the problem happened (4 types of data
analytics to improve decision- making, 2021). This technique provides an in- depth
analysis of the problem and company can gather detailed information when they require.

3. Predictive- this technique involves prediction relating to what will likely to happen
relating to the problem. this technique makes use of both diagnostic and descriptive. With
help of both these techniques researchers predict future trends which might take place.
4. Prescriptive- this technique will outline or prescribe the fact that what future actions can
be taken in order to eliminate the problem in future as well. this technique undertakes use
of advanced tools and technologies in order to solve the problem (Das and Dave, 2020).
With the above discussion, it is clear that use of all these analytics is essential for the analysis of
the data. But the most preferable analytics method for solving the issue of data security is
predictive approach. this is pertaining to the fact that it involves the combination of both
descriptive and diagnostic approach.
KPI
KPI that is Key Performance Indicator is a tool which assist company in analysing the
performance of the company. this tool involves making some indicator relating to performance
of the company and then to measure the performance against that measure.
Identified KPI
For undertaking KPI the major indicators being selected for measuring or solving the
problem of data security involves customer satisfaction, internal process quality, employee
satisfaction and financial performance index. All these element comprises of measuring the
performance of the company and find ways of improving the business. for managing data
security, in case customer are not satisfied with the service then threat of data security will
increase. In the similar manner when data security threat is high then the employees will also not
be high and financial performance will also be low. Hence, with comparison to these indicators
the company can identify that where actually they are working.
Measurement
For the measurement of the KPI, the standards will be compared to the actual
performance which is being undertaken by the company. the reason behind this fact is that when
the business is setting some indicator or standard then they have to work for attaining those
indicators (Kumar and Nagar, 2017). The measurement here will involve comparing the actual
performance with the set standards and this will be compared every month. The reason behind

this fact is that every month company will be in position to identify the areas in which they are
lacking every month and will be having enough time to improve their business.
Communication
Just comparing the data and taking corrective action is not enough, rather company has to
communicate the working with other stakeholders as well (Popchev and Orozova, 2019). the
reason pertaining to the fact is that when the communication will be clear then only company
will understand how they will be improving the performance. hence, for communication the use
of written communication that is with help of emails will be undertaken by the company. the
reason behind the use of written communication is that it can be used by the company for future
reference as well.
TECHNOLOGY AND TOOLS
For solving any issue or business problem it is very essential for the company that they
must collect relevant and good data relating to problem. hence, the solution to the business
problem is totally dependent over the data being collected. Thus, there are different tools and
technology which can be used by company for collecting and analysing data. These technologies
are as follows-
 Internet survey- this is a type of tool wherein the company can gather the data with help
of survey which is conducted online. This is a good tool as it will assist the company in
gathering data without physically interacting with the participant.
 Internet interview- this is another technique which is helpful in gathering data relating to
business problem. the reason underlying this fact is that interviews can also be conducted
with help of internet and data can be gathered from there.
After collecting the data, storage of data is very important because if data is not stored
properly then it can be misused. Thus, for managing data storage it is essential to understand the
requirement of data and then store it. For instance, is data is required very frequently then it must
be stored in such a place that it can be accessed any time (Cai, Li and Wang, 2021).
After the storing of data, the analysis of the data is very essential because is it will not be
analysed in proper and effective manner then this will not provide actual result. Hence, for
analysing the data there are many different techniques which are as follows-

 Regression- this is a sciatica tool which is used in order to identify the relationship
between two variables wherein one is dependent and other is independent. This statistical
tool is assistive to company in analysing the relation between the variables and to identify
the manner in which the problem can be solved (Park, Park and Kim, 2021).
 Time series analysis- this is another statistical tool which is also assistive in the
management and analysis of the data. This involves analysing the cycle and trend relating
to the business problem and to identify the steps in which the problem can be sorted and
solved.
 In addition to this statistical tools, the use of other data analytic tool can also have
implemented within solving the business problem (Guan, 2021). The tool of R
programming which is a leading analytic tool within the industry. This assist company in
manipulating the data and present that in many different manners.
Further providing access to the relevant person is also very essential as this will acknowledge
other people in using the data. This is necessary because same data will be helpful for some other
person as well. due to this it is necessary that all the data must be easily accessible. Hence, for
this, the use of cloud storage or other google drive will be implemented. This is pertaining to the
fact that from here all the related people can have proper access to the data.
DATA VISUALISATION AND REPORTING
Visualisation method and types
After the analysis of the data, it is necessary for the business to present and visualise the
data. Data visualisation is very important because it deals with presenting the information with
help of graphical representation. This is necessary because of the reason that this graphical
presentation will assist the person in directly analysing the situation with help of data presented
in pictorial format. Hence, this will assist company in understanding the actual situation by just
looking at the picture being provided by summarising the data in form of chart or graphs.
Charts- this is a type of method wherein the data is being presented with help of the
charts. These charts can be bar chart, line chart or any other type of chart (Van Hertem and et.al.,
2017). This method of visualisation is very helpful to the business as all the key points will be
presented in form of chart and this will make it more clear and easy for reader to understand.

Tabular presentation- this is another form in which data can be presented that is using the
tables. Under the table all the data is being presented and people can evaluate and analyse the
relevant data for the effective management of the work (Pika and et.al., 2021). This is essential
for the reason that when the tables will have analysed then this will assist the person in
developing good knowledge relating to the problem. Hence, this will assist the company in
developing ways to reduce the problem and to find effective solution to the company.
Out of all, the graphical presentation that is with help of chart is more helpful and useful.
The reason pertaining to the fact is that this will assist the person by just viewing the graph and
understand what actual problem is (Rahlf, 2017).
DATA GOVERNANCE, PRIVACY & ETHICS
Challenges or issues
For the data analysis to be successful the most essential aspect is the proper taking caring
of the governance, privacy and ethics while analysing data. The reason pertaining to the fact is
that when the data is not managed properly then it will not work effectively.
The major challenge which the companies might face while managing the data and its
privacy is that cost of maintaining the data is very high. the reason pertaining to the fact is that
when the cost will be high then company will think of other alternate in order to manage data
privacy.
Another challenge relating to managing effective governance is the accountability issue.
This is pertaining to the fact that when the company is not having proper accountability then they
will not be in position to govern the business in proper and effective manner.
Recommendation
Thus, it is recommended to companies that they must evaluate the different methods
through which data privacy can be effectively managed and maintained. This is necessary
because of the reason that when the alternate methods will be evaluated then this will outline the
method which is cost effective.
In addition to this, another suggestion to the companies in order to manage governance,
ethics and privacy is that all the work must be allocated to the employees along with authority
and responsibility. This is necessary because of the reason that when every person will be having

their own responsibility then they will be accountable for the task which they are allotted. Hence,
this will improve effective governance within the business.
Along with this, it is also advisable to the companies that they must ensure that all the
ethical principles are being followed effectively by the companies. The reason pertaining to the
fact is that when the ethical principles are being followed the company will ensure that working
is being effectively undertaken within the business.

REFERENCES
Books and Journals
Cai, Y., Li, D., & Wang, Y. (2021). Medical big data intrusion detection system based on virtual
data analysis from assurance perspective. International Journal of System Assurance
Engineering and Management, 1-11.
Das, A. V., & Dave, T. V. (2020). Demography and clinical features of chalazion among patients
seen at a Multi-Tier eye care network in India: an electronic medical records driven big
data analysis report. Clinical Ophthalmology (Auckland, NZ). 14. 2163.
Guan, Y. (2021, April). Research on the Reform of Ideological and Political Theory Courses in
Colleges and Universities in the Big Data Era. In Journal of Physics: Conference Series
(Vol. 1852, No. 2, p. 022061). IOP Publishing.
Kumar, M., & Nagar, M. (2017, July). Big data analytics in agriculture and distribution channel.
In 2017 International Conference on Computing Methodologies and Communication
(ICCMC) (pp. 384-387). IEEE.
Lhatoo, S. D., Bernasconi, N., Blumcke, I., Braun, K., Buchhalter, J., Denaxas, S., ... & Wiebe,
S. (2020). Big data in epilepsy: clinical and research considerations. Report from the
epilepsy big data task force of the international league against epilepsy. Epilepsia. 61(9).
1869-1883.
Park, S., Park, B. D., & Kim, Y. (2021). Big-Data Analysis for Manufacturing Processes of
Medium Density Fiberboard Production. In 한한한한한한 한한한한한한한 (Conference Proceedings)
(Vol. 2021, No. 1, pp. 41-41). 한한한한한한한.
Pika, A., ter Hofstede, A. H., Perrons, R. K., Grossmann, G., Stumptner, M., & Cooley, J.
(2021). Using big data to improve safety performance: an application of process mining to
enhance data visualisation. Big Data Research. 25. 100210.
Popchev, I., & Orozova, D. (2019). Towards big data analytics in the e-learning space.
Cybernetics and information technologies. 19(3). 16-24.
Rahlf, T. (2017). Data visualisation with R: 100 examples. Springer.
Van Hertem, T., Rooijakkers, L., Berckmans, D., Fernández, A. P., Norton, T., & Vranken, E.
(2017). Appropriate data visualisation is key to Precision Livestock Farming acceptance.
Computers and electronics in agriculture. 138. 1-10.
Online
4 types of data analytics to improve decision- making. 2021. [Online]. Available through:
<https://www.scnsoft.com/blog/4-types-of-data-analytics>