Data Analytics Case Study

This case study highlights the procedures required for data-driven decision making and improving efficiencies in businesses. It discusses the current mode of operation at Startup Muster, possible inefficiencies, data analysis techniques, and the use of business intelligence tools. The report also explores the role of data analytics in digital marketing and its potential in making strategic and tactical decisions.

Data Analytics Case Study 1
Data Analytics Case Study
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Introduction
The purpose of this report is to highlight the procedures that are required to enable a data
driven decision making with regards to Startup Muster (Facione and Gittens 2015). Startup
Muster is a company based in Australia whose aim is centered on measuring and publishing the
progress, challenges and opportunities surrounding Australian startup ecosystem. The company
is largely dependent on survey which is an element of data analytics in order to enable
meaningful and timely connection between businesses and startups. The objective of this report
is to describe the current mode of operation at Startup Muster, possible inefficiencies that may
hinder performance as well as available data that may be used to provide efficiencies according
to techniques that are used in data analysis, problem solving and digital operations (El-Nasr,
Drachen and Canossa 2016). As a result, this report aims to introduce data analytics and the draw
insight into the big data revolution, introduce data analysis, and discuss how statistical metrics
and data analysis is interpreted as well as discussing business intelligence tools. The knowledge
derived from understanding these operations will be used to examine the possible inefficiencies
that hinder data decision making in order to provide a framework which can be used to improve
efficiencies of the concept that are discussed under data analytics.
Startup Muster is considered to play a major role in ensuring that businesses are able to
introduce new products and services to the market. In essence, the goal of the company is to
open up new export opportunities, create jobs and contribute to the growth of Australian
economy (Startupmuster.com. 2019). The company is the largest in Australia under the Startup
ecosystem and has grown to attract stakeholders who include the government, entrepreneurs and
other programs that assist businesses to introduce and market their products across the globe.
Startup Muster alludes that artificial intelligence is one of the biggest industries in Australia that

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continues to grow over the years. Startup Muster mainly focuses on survey methodology as its
primary tool. There are numerous services that the company can provide comfortably (Van Aken
and Berends 2018).
Current Mode of Operation
Key operations that Startup Muster is involved in aims to show the connection between
data analytics and business intelligence. In this regard, there are various skills that are necessary
to carry out data analysis. Core skills include quantitative, qualitative, visualization and decision
making skills. At Startup Muster, data plays a significant role as it can be used for business
intelligence, it is an emerging technique that that delves into sets of data without prior
hypothesis, it helps provide meaningful trends or intriguing findings as well as enable instant
decision making criteria based on information observed (Nelson 2016). The differences between
data analytics and statistical analytics is that statistical is based on mathematical techniques and
uses theoretical approaches in order to determine level of significant to validate statistics.
On the other hand, data analysis is pegged on data mining techniques to develop
relationships and trends and seeks visualization to validate the data (Witten et al 2016). Startup
Muster can use various techniques when approaching a client’s needs. This information is
particularly significant in contextualizing things like margin of a company over time.
Relationships focuses on comparing two different sets of data to be able to explain differences
and similarities while composition constitutes segmentation of a given data set (Sekaran and
Bougie 2016). Besides, variations is centered on the degree of inconsistency of a given data
which does not indicate positive results in business context.
Further, logical consistency validates why a given set of data and decision should be
justified. Data analysis information can be communicated in different ways depending on the

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purpose and ease of understanding. Importance of data with regards to Startup Muster is centered
on helping businesses make decisions pertaining their businesses, identification of needs and
problems to determine viable solutions, offers insights into business environment and helps
various stakeholders to take action based on available data.
The data used by Startup Muster is often categorized into two quantitative data and
qualitative data. Quantitative data comprises numerical, counted and data that can be compared
in scale. Such data can be used by Startup Muster to evaluate demographics, answer survey
questions among other data related to standardized instruments. On the other hand, qualitative
data is essential for Startup Muster in relation to carrying out interviews, open-ended surveys,
focus groups and observations. Before data analysis, the data has to be collected and there are
several ways that are used in the collection of data by the company. Muster uses various steps in
conducting their surveys which include creating questions, asking questions, counting and
analyzing data and reporting the data to relevant stakeholders (Startupmuster.com 2019).
Another key operation that Startup Muster undertakes is data management. This is because the
data is often classified in terms of structured and unstructured data. Efficient data management is
characterized as proactive in nature and Startup Muster has to consider how to deal with certain
elements of the data (Laudon and Laudon 2016).
Possible inefficiencies
Possible inefficiencies with regards to Startup Muster is related to common inefficiencies
that surround the workflow of data analysts. These inefficiencies can be found in two main
classification of the workflow which include cleaning data and understanding its oddities and
nuances as well as collaborating with other stakeholders. Cleaning data that is significant in
making decision for businesses can be time consuming which often requires automated

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applications that reduce the workflow. This is often the case because data generating processes
are often messy and analysts often lack control over the processes of mining the data. Therefore,
inefficiencies in data analysis can be summed up to complex data decision making process (Jin et
al 2015). This problem is attributed to the interpretation of statistical metrics in the process of
analyzing the data. Statistical data are often classified into two broad areas which include
descriptive statistics and inferential statistics.
With regards to Startup Muster, the report focuses on descriptive statistics. Descriptive
statistics is majorly concerned with describing data using tables and graphs which include
categorical variables. There are also numerical description of center and variability position
which concerns quantitative variable as well as bivariate descriptions (Menke 2018).
Inefficiencies are also brought about by frequency of data distributions which are characterized
by numerical presentation of quantitative data, summarizing large sets of data values, showing
the frequency and relation categories. If the information cannot be visualized, it often presents
challenges that bring up inconsistencies in data interpretation and decision making (Xu,
Frankwick and Ramirez 2016). This is because it has become increasingly difficult to manage
large volumes of data and information keeps on growing. Humans have the ability to process
large amounts of information in a parallel manner which has to be backed by concrete data.
Inefficiencies can also be reduced by adopting sophisticated graphical software and
hardware to increase visualization effect. Due to difficulty in understanding large sets of data for
decisions making, data visualization presents a viable solution to summarize the data and make
meaningful conclusion about the large volumes of data. Other elements that can be used to
enhance visualization include animation, color to make distinction and aesthetics to make the

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process appealing. Organization of data can also bring challenges of inefficiencies and this can
be solved through identification of ways to organize different sets of data (Chambers 2017).
For instance, data can be organized according to task, user insight need, data that is
required, data that requires transformation among other techniques that can be significant in
visualizing information. In data analysis, there are main types of visualization that Startup
Muster uses to enhance visualization which include charts, tables, graphs, geospatial maps and
network graphs. These types include ways that are significant in reducing inefficiencies of data
interpretation which is significant in decision making. Also, they can be used in interpretation
through tree graphs, hierarchies, taxonomies and genealogies.
For starters, there is temporal visualization type which is aimed at answering the question
when something is likely to happen. This commonly used in predictive data and eliminates
inefficiencies of timeline in decision making. Nonetheless, temporal visualization type can be
significant in understanding the distribution of data, growth rates and patterns in a time series
data such as seasonality and trends. These types of data can be demonstrated as increasing,
decreasing, stable and cyclic. Visualizing knowledge helps to facilitate knowledge sharing and
creation in order to transfer attributes such as experience, attitudes, values, expectation and
prediction (Card 2017).
Startup Muster uses business intelligence in its operation in order to improve efficiencies.
Business intelligence (BI) refers to a technology that is used to analyze data in order to provide
actionable information to assist various stakeholders including the public, managers and business
decision makers. Business intelligence comprises a variety of tools, applications and
methodologies that enable companies like Startup Muster to collect data from systems and
external sources for purposes of preparing them for analysis. Elements of a business intelligence

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system includes queries, reports, dashboards and data visualization for the purposes of making
decision making easy. Business intelligence is often used to describe analytics of big data that
have meaningful impact in decision making of a business (Höchtl, Parycek and Schöllhammer
2016). Therefore, business intelligence can be termed as advanced data analytics. Business
intelligence is significant because its potential stems from tools that aim at accelerating and
improving decision making, optimizing internal business processes, increasing operational
efficiency, driving new revenues and helping businesses gain competitive advantage.
Further, business intelligence helps companies to identify market trends and problems
that may require solutions to enhance efficiencies in the business. Business intelligence data
comprises different sets of data that enable Startup Muster to make strategic and tactical
decisions making process. Business intelligence technology also encompasses data visualization
software that is significant in designing charts among other visualization elements. These data
visualization tools have become standards in various business environment (Ward, Grinstein and
Keim 2015).Moreover, business intelligence programs may also incorporate forms of advanced
analytics such as data mining, predictive analysis, text mining, statistical and big data analytics.
Business intelligence data is typically stored in data warehouse. Different sources of data have to
be integrated together before they can be used. Also, they have to be cleansed in order to ensure
that they provide accurate and consistent information.
In this report, we focus in Tableau as a business intelligence tool with a keen focus on
how it helps companies such as Startup Muster is facilitating easy decision making with data
visualization (Tableau Software 2019). Tableau is structured to helps users understand data.
Tableau estimates that the amount of data is going to increase by a significant margin and within
this data exists opportunities for human advancement. However, in order to tap into such power,

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the data has to be easy to interpret in order to make decisions. Tableau transforms the way people
use data to solve their problems by making data analysis easy and fast. This application software
can be used by different groups of people depending on their data needs. Tableau was made to
assist people to see and understand data as one of the most fundamental aspects of the 21st
century.
There are numerous application in real life where data analysis with the help of Tableau
can be used in making strategic and tactical decision making in business. To begin with, data
analysis is the primary tool for digital marketing and the first rule of experienced digital
marketers is to tests the market rather than making assumptions. Digital marketing can be
classified under a digital marketing strategy which is grouped into internet marketing and social
media marketing (Kannan, 2017). Digital marketing strategy has to make use of the channels that
support the mission, vision, objectives and activities of a particular organization such as Startup
Muster. A digital marketing strategy has to be interactive and have the ability to spark
conversations among consumers in order to draw in more customers.
The types of indicators that can be used for this purpose are aimed at benchmarking,
campaign, forecasting and budgeting (Williamson 2016). A more sophisticated approach in
determining key performance indicator of a website campaign is Google analytics. The tools
measures various aspects of how users interact with a particular site by demonstrating through
visualization the number of new and returning visitors to a site, unique visitors that visited a give
site, the number of pages that were visited, average number of page visited, time taken to visit
the pages and the number of visitors who visit a particular site once and leave. Besides, there are
other techniques that are used in digital marketing that require data analysis and visualization
such as A/B testing. A/B testing is a technique that is used to compare two sets of data mostly

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web pages in order to determine profitability and comparison with regards to the number of
users.
Conclusion
There are numerous potential in data analytics which can be attested by Startup Muster in
how it uses data analytics skills to improve the performance of businesses. For instance data
analytics is significant in improving efficient. Companies collect huge information that may not
necessarily be significant and most business analyze the data internally. With the advancement of
technological tools these data can be mined and help other people improve efficiency in their
sectors. This is because the decision making is not made by making assumptions rather relying
on concrete information that is analyzed through data analysis. Data analysis also helps to meet
and satisfy customers’ needs by maximizing the feedback generated from a survey. Lastly, data
analysis helps to inform a business about the nature of the market and provide more knowledge
that facilitate decision making.

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Reference List
Card, S.K., (2017). The psychology of human-computer interaction. CRC Press.
Chambers, J.M., (2017). Graphical Methods for Data Analysis: 0. Chapman and Hall/CRC.
El-Nasr, M.S., Drachen, A. and Canossa, A., (2016). Game analytics. Springer London Limited.
Facione, P. and Gittens, C.A., (2015). Think critically. Pearson.
Höchtl, J., Parycek, P. and Schöllhammer, R., (2016). Big data in the policy cycle: Policy
decision making in the digital era. Journal of Organizational Computing and Electronic
Commerce, 26(1-2), pp.147-169.
Jin, X., Wah, B.W., Cheng, X. and Wang, Y., (2015). Significance and challenges of big data
research. Big Data Research, 2(2), pp.59-64.
Laudon, K.C. and Laudon, J.P., (2016). Management information system. Pearson Education
India.
Menke, W., (2018). Geophysical data analysis: Discrete inverse theory. Academic press.
Nelson, B.L., (2016). ‘Some tactical problems in digital simulation’for the next 10
years. Journal of Simulation, 10(1), pp.2-11.
Sekaran, U. and Bougie, R., (2016). Research methods for business: A skill building approach.
John Wiley & Sons.
Startupmuster.com. (2019). Startup Muster 2018. [online] Available at:
https://www.startupmuster.com/about [Accessed 14 Jan. 2019].
Tableau Software. (2019). Tableau: Business Intelligence and Analytics Software. [online]
Available at: https://www.tableau.com/ [Accessed 14 Jan. 2019].
Van Aken, J.E. and Berends, H., (2018). Problem solving in organizations. Cambridge
University Press.

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Williamson, B., (2016). Digital education governance: data visualization, predictive analytics,
and ‘real-time’policy instruments. Journal of Education Policy, 31(2), pp.123-141.
Witten, I.H., Frank, E., Hall, M.A. and Pal, C.J., (2016). Data Mining: Practical machine
learning tools and techniques. Morgan Kaufmann.
Xu, Z., Frankwick, G.L. and Ramirez, E., (2016). Effects of big data analytics and traditional
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