Big Data Analytics Report: Tableau Strengths and Weaknesses

This report critically evaluates the strengths and weaknesses of Tableau in the context of big data analytics. It examines Tableau's ability to handle large datasets, create various visualizations, and provide mobile-friendly access. The report highlights Tableau's strengths, such as strong performance, data security, and extensive online resources. However, it also addresses weaknesses, including the lack of rollback options in older versions, the need for manual data updates, and security concerns with sensitive data. The report further discusses recommendations, such as using alternative tools like R programming and SQL knowledge for complex datasets. In conclusion, the report emphasizes the importance of managing large datasets effectively and suggests strategies for optimizing data analysis using Tableau and other relevant tools.

Big data analytics

Table of Contents
INTRODUCTION...........................................................................................................................3
Critically evaluating the strengths & weaknesses of the data analytics by making use of
Tableau and providing appropriate recommendations................................................................3
CONCLUSION................................................................................................................................5
REFERENCES................................................................................................................................6

INTRODUCTION
Big data analysis are used to examine large data set and big data in order to uncover the
information i.e. hidden pattern and undefined correlations as well as customer preferences. In the
same way, the current report will critically evaluate the strength and weakness of Tableau with
proper recommendations in the context of business and technical.
Critically evaluating the strengths & weaknesses of the data analytics by making use of Tableau
and providing appropriate recommendations
As reviewed by the Khalajzadeh and et.al., (2018), it has been indicated that by making
use Tableau, overall performance of the data can be measured in a strong and the secured
manner. It helps in handling millions of the rows in the data easily and also enables in
developing different kinds of the visualisation at one time or one shot. However, the main
weakness of using the Tableau in data analytics is that under this rolling back cannot be possible
in the old version. Moreover, this software seems as mobile friendly as there present an
accomplished app in the mobile that available for Android & IOS which in turn adds the mobility
to the Tableau users and facilitates them in keeping the statistics at their respective finger-tips.
On the other hand, By using Tableau, an individual does not get automatic option for refreshing
his or reports with help of the scheduling. Therefore, it requires some of the manual efforts in
updating data at the back end (Park, Ferris and DeMarco, 2019). However, data analytics could
have extensive resources for the customers as Tableau contains several comprehensive resources
online, training, online forums and guides. On the other state, assessing the data through Tableau
requires a manual efforts which means that in case of any change in the data, it must updated at a
time. This is because the tableau parameters are seen as inactive and in this only the single value
could be selected by making use of the parameter.
Dzuranin, Jones and Olvera, (2018), viewed that data analytics contains excellent
support of the mobile as the Tableau has huge percentage of the users who actively deploys
mobile and It helps applying more of efforts in context of building healthy mobile related
customer. However, security issues are faced in data analytics with the use of Tableau because
visualization solutions results in manipulating some of the confidential data. On other note, it
helps the users in thriving for community and the forum in the data analytics as through Tableau,

users could beef up their skills and knowledge with respect to the data parsing, reporting &
getting meaningful insights within this community.
According to Zion and Tripathy (2020) using Tableau, the project got high performance
such that user rate of this analytical tool is as strong and secure and that is why, it is help to drug
Misuse analysis and collect the relevant information as well. Moreover, in the business
perspective, this method has extensive customer resource such that it is engaging and
enthusiastic and provide various online resource as well as training session to their employees as
well. That is why, most of the top business also uses this method in order to provide effective
training session to their employees. On the other side, it is also evaluated by Johnson, Albizri and
Jain (2020) That a person needs SQL knowledge in order to create complex datasets from the
multiple data sources. Thus, it is an excellent option for the business user when they have skilled
workers who have enough knowledge related to SQL otherwise it is not beneficial.
In addition to this, Arfat and et.al., (2020) also stated that tableau has a user and
developer community where the queries are easily solved and in the same way, in order to
answer the question of misuse of Drug, this big analytics tool is used because it use the latest
release of software because the upgrades are easy to carried out and also generate the best results
while no other tool is supported. While it is also critically evaluated that the tableau parameter
are static and it is always select value as a parameter. Thus, when the data are suddenly change
then these parameter are also updated automatically and the user cannot do anything because it is
by default set within a software.
Recommendations
It has been recommended that there several other tools which enables the users in data
analytics that includes R programming, SAS, Qlikview, Rapid Miner, Apache Spark, KNIME,
Excel etc. These are the other top tools which can be used for analysing the data in an efficient
and effective manner other than the Tableau tool (Schneider, Reilly and Radu, 2020). Using
these open kind of source tools users can easily make data analytics as such tools does not need
any type of coding and manages in delivering the better results.
Among all, R programming is consider one of the most effective tool that has a variety of
options i.e. data manipulation, statistical modelling as well as graphics which assist to get
accurate results. Through this tool the developers are also easily write their own software and
also distribute in the form of the add- on package that will assist to get better results. For

overcoming such issues company can make use of the other tools that helps in making efficient
analytics of the data and also helps in evaluating accurate findings and the results
CONCLUSION
By summing up the above report it has been summarized that it is important to manage
and maintain larger data adequately so that important and suitable decisions of the business can
be taken in a better way. Moreover, by making use of the software called as Tableau, data
analytics contains many strengths such as flexibility, ease, accuracy etc. This helps in making an
assessment of the data and its reporting in effective way and also helps in maintaining the data in
an appropriate manner that is understandable and could be used for making further research.
Data analytics are associated with several weaknesses also that is security issues, costly,
inflexible etc.

REFERENCES
Books and Journals
Dzuranin, A. C., Jones, J.R. and Olvera, R. M., 2018. Infusing data analytics into the accounting
curriculum: A framework and insights from faculty. Journal of Accounting Education. 43.
pp.24-39.
Park, B., Ferris, M. C. and DeMarco, C. L., 2019. Benefits of Sparse Tableau Over Nodal
Admittance Formulation for Power-Flow Studies. IEEE Transactions on Power
Systems. 34(6). pp.5023-5032.
Schneider, B., Reilly, J. and Radu, I., 2020. Lowering Barriers for Accessing Sensor Data in
Education: Lessons Learned from Teaching Multimodal Learning Analytics to
Educators. Journal for STEM Education Research. pp.1-34.
Zion, G. D. and Tripathy, B. K., 2020. Comparative Analysis of Tools for Big Data Visualization
and Challenges. In Data Visualization (pp. 33-52). Springer, Singapore.
Johnson, M. E., Albizri, A. and Jain, R., 2020. Exploratory Analysis to Identify Concepts, Skills,
Knowledge, and Tools to Educate Business Analytics Practitioners. Decision Sciences
Journal of Innovative Education. 18(1). pp.90-118.
Arfat, Y. and et.al., 2020. Big Data Tools, Technologies, and Applications: A Survey. In Smart
Infrastructure and Applications (pp. 453-490). Springer, Cham.
Khalajzadeh, H. and et.al., 2018, July. A survey of current end-user data analytics tool support.
In 2018 IEEE International Congress on Big Data (BigData Congress) (pp. 41-48). IEEE.