Data Analytics Case Study

Data Analytics Case Study 1
Data Analytics Case Study
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Data Analytics Case Study 2
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

Data Analytics Case Study 3
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

Data Analytics Case Study 4
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