Business Research: Tableau's Role in Analytics and Decision Making

This report explores the application of Tableau in business research analytics, highlighting its interactive visual analytics platform and user-friendly interface. It discusses how Tableau enables businesses like Vodafone NZ to analyze customer service data, perform segmentation and cohort analysis, and conduct what-if scenarios and time-series analysis. The report also evaluates Tableau's effectiveness in business performance management, data mining, statistical assessment, and business reporting, emphasizing its ability to integrate data from various sources and generate globally recognized visuals. The document is contributed by a student and available on Desklib, a platform offering study tools and resources.

Running head: BUSINESS RESEARCH ANALYTICS: TABLEAU
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Business Research Analytics: Tableau
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Business Research Analytics: Tableau
Part I
Visualization of Data
Tableau is a very interactive visual analytics platform that allows easy user-interface by
supporting drag and drop functionality when it comes to the presentation of data. As a result,
Tableau allows the researcher to make numerous alterations in the assessment criteria i.e.
changing x and y variables or sorting by color. Tableau allows for the provision of
comprehensive visual feedback that promotes the researcher to make sound and informs
conclusion. It is every easy for the data analyst to manipulate customer service figures from a
company like Vodafone New Zealand (Coe, et.al. 2017). The company analyst is able to assess
customer services based on region because the BI software supports world map data
representation format. As a result, the company is able to gather information on regions that
observed excellent and sub-standard customer service over the past financial year. Tableau is
preferred by many businesses because of the availability of numerous visual tools negating the
need to utilize numerous BI software to get all the necessary charts, tables, and regional maps.
Finally, Tableau has a dashboard visual presentation feature that allows for the presentation of
multiple visual tools in the same window as a way to draw cross tabulation and summarize
company dealings. Designing a dashboard is straight forward due to informative templates and
the useful drag and drop functionality. A company like Vodafone NZ (New Zealand) can
tabulate several items on a single dashboard to get a clear picture of the business’ performance in
terms of pricing, customer services, network coverage, value of money, and accessibility (Coe,
et.al. 2017). Below is an example of dashboard generated in tableau:

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Segmentation and Cohort Analysis
In order for a business like Vodafone NZ to generate a plausible hypothesis about
company performance they need to first create data segments and carry out cohort analysis. The
data experts in the company can formulate several questions about different segments as a way
of understanding data and drawing conclusions on suitable hypothesis. For example, data experts
at Vodafone NZ can ask “How many teens utilize our services within the Auckland
metropolitan?”. Tableau allows data scientists to clearly visualize projects and create models that
will promote continued growth by highlight problem and success areas (Kamkolkar, et.al. 2014).

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Tableau is preferred by most people as the BI tool of choose due its ability to allow easy retrieval
of data from different sources and format. Tableau allows you to access data regardless of where
it is store. This feature is vital to companies where majority of their data is store in different
locations or in cloud servers. Moreover, the ability of the software to utilize data created in other
software like SPSS, SAS, R, and Microsoft Excel means that the data scientist does not have to
worry about the type of data creation analytic tool used by different company offices across New
Zealand. For instance, one store could be using Excel to record data and another store could be
using SPSS. However, when both datasets are set to the main analysis center they will be easily
accessed and analyzed in Tableau (Kamkolkar, et.al. 2014).
What If Scenarios and Time-series analysis
Sometimes, company executives in large companies like Vodafone NZ are interested in
how changing the values associated with a give variable with affect the overall output. By so
doing, companies are able to explore different possibilities, evaluate multiple theories, and
highlight ideal business scenarios. As such, Tableau provides a what-if analysis feature that
allows the researcher to redefine base values, alter conditions, and make adjustments to
employee quota. Another powerful analysis function found in Tableau is the time-series analysis.
The software allows for the real-time analysis of data patterns based on historic data and the
utilization of that data to make sound future prediction. Vodafone NZ can use the time-series
analysis feature to plot out data on product pricing overall the years against customer satisfaction
(Kamkolkar, et.al. 2014).
Part II
Effectiveness of Tableau in Analysis

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Web support offered by Tableau allows organizations like Vodafone NZ to store, and
share their data with other companies. For example, an online community of businesses create by
Tableau allows the sharing of company records for the sake of market research and
benchmarking purposes. In addition, online support feature allow companies to generate and
share their analysis findings on their website platforms and social media due to in-built feature
and coding languages that allow for rapid sharing of information. Online analytical process
(OLAP) and interactive visualization tools are normally employed by business in the assessment
of Business Performance Management (BPM) through the exploration of interesting elements in
the data being investigated (Chen, et.al 2012). Tableau is one of the very few software that are
able to effectively generate a visually pleasing dashboard that comprehensively showcases the
various performance metrics. According to researcher, a good BI tool has to support data mining
techniques, statistical assessment, and business reporting modules. Tableau fits all the
characteristics of a good BI tool allowing for effective regression analysis, predictive modeling,
cluster assessment, data segmentation, and association validation. Tableau has incorporated those
and many more data evaluation and processing technologies as a way to compete effectively will
with industry leaders like Oracle and IBM. Most people are only willing to purchase BI tools
that are highly communicative with other BI platforms; Tableau fulfils this quality allowing for
the usage of data created, manipulated, and saved in other programs as discusses earlier (Chen,
et.al 2012).
Another feature that is sort out by data experts is the ability of BI tool to generate visuals
that can be related with everyday business events. In addition, these visuals can be easily
adjusted to demonstrate sensitivity to diverse customer population and pricing strategies. Unlike,
some advanced BI platforms, Tableau cannot be used to model very specific business operation

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function; such activities are supported by tools like SAP Predictive Analytics (Chen, et.al 2012).
The ability to employ IT in the restructuring of business operation is only made possible through
the usage of effective BI tools, high-speed networks, and global supply chain. An organization
like Vodafone NZ is able to monitor and improve customer services and product pricing due to
sound data investigation. Internet has fostered global connectivity and as such has allowed for
the formulation of data presentation standards. As a result, a BI tool like Tableau has to generate
visual diagrams that bear the same mean across the world and can be easily interpreted by any
data analyst regardless of where they are in the world. Tableau does generate acceptable visual
that are globally recognized (Chen, et.al 2012).

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References
Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big
Data to Big Impact. MIS Quarterly , 30 (4), 1-25.
Coe, I., Beran, B., & Stull-Lane, P. (2017). Advanced Analytics with Tableau. Seattle, WA:
Tableau.
Kamkolkar, N., Fields, E., & Rueter, M. (2014). Tableau for the Enterprise: An Overview for IT.
Seattle, Washington D.C.: Tableau.