MBA504 Report: Analysis of the Startup Muster Data and Findings

This report provides a comprehensive analysis of the Startup Muster report, focusing on data analysis, statistical metrics, and data collection methods. The report begins with a synopsis of the Startup Muster report, highlighting key insights and offering a critique of the presentation methods and visualizations used. It delves into the statistical metrics derived from the report, exploring the gender distribution of startup founders, the educational backgrounds of owners, and the skills of the founders. The report also examines data management and collection processes, including the survey methodology, response rates, and data validation techniques. The report also summarizes the key points made by the guest speaker, providing a holistic understanding of the startup ecosystem and its data-driven aspects. The report concludes with a summary of the findings and recommendations for improvement, offering valuable insights for data analysts and business professionals. The report also offers alternative graphical or visual representations to enhance the presentation of the data.

Running head: START UP MUSTER
START UP MUSTER
Name of student:
Name of university:
Author’s note:

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Table of Contents
Introduction....................................................................................................................2
Discussion......................................................................................................................2
Statistical metrics.......................................................................................................2
Data management and collection...............................................................................3
Summary of key points..............................................................................................4
Conclusion......................................................................................................................5
Bibliography...................................................................................................................7

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Introduction
This report aims to discuss and evaluate the start-up muster report and provide
appropriate summary. An appropriate synopsis of the report is provided along with key-
insights, and some detailed comments on the methods that are used is provided in this report.
The presentation methods that are used in this report is provided in this report. The
appropriate statistical metrics from the report of start-up muster is provided in this report.
The start-ups plays a crucial role in the introduction of innovative services and
products in the markets, and finally creating opportunities for exporting, creating jobs and
then delivering economic prosperity for everyone.
Discussion
Statistical metrics
The Data61, the world leaders in the research of data science has been engaged for
creating an estimate on the statistics of the startups in the country, Australia on the five years
of the data of startup muster. It is essential that the Startup Muster manually reviews each of
the participant in the surveys and it only accepts the companies addressing a huge market in
any scalable methods that intends to eliminate the significant proportion of the self-identified
startups. After conducting the surveys, it is observed that the gender initiating the startups is
majorly the male gender as the statistics show. The aspect of good data needs to be both
actionable and accurate. The data from 178 distinct data points in the year, 2018 and it is
observed that majority of the data is the result of an organisation that wishes to gain
knowledge about any specific attribute for providing enhanced support to the startups. In the
year, 2015 954 new startups have been implemented and it is observed to be increased to
1085 in 2016 and the most number of startups that have been implemented in the last five
years is in the year 2017, with almost 1675 companies and then it decreased to 1465 in the

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year 2018. The education level of the owners of the startups are majorly Bachelors and then
Masters and the lowest knowledge of the people is in the industry accreditation. 77% of the
male gender are implementing the startups in the country whereas 22.3% of the female
gender are implementing startups. The skills of the founders that are supposed to be the
strength of the owners is highest in the general business operations with 55.8% and the lowest
surveyed skill that is found in the business owners is in the stakeholder relations that is
considered to have affected the companies at a significant level. The experience of the people
initiating the startups are discovered to be sufficient for gaining success in the new business
ventures. The majority of the skills that are found in the teams of the startups in the sector of
the general business operations. The industries that are connected with the startups are mostly
artificial intelligence where the opportunities of growth of the new business ventures is found
to be highest. With the requirement of artificial intelligence in almost all prospects of the
modern world the need of innovative products are not considered to decrease in the coming
times. It has been observed that the main reasons for outsourcing in the businesses are the
skills and expertise, run the business is cost effective methods, insufficient work for hiring,
flexibility, time and the challenge of discovering
Data management and collection
The period of online survey collection of the Startup Muster 2018 for the country, Australia
was available for completing online. In the time of the data collection of the survey, the
people were encouraged to participate in the survey who are thinking of launching the
businesses and startups. The respondents who previously participated in the period of
collection in 2017 were not required to participate in the surveys again as they were included
in the process of upgrading where the responses were verified by them and then it was
updated that were the new developments of the businesses. Combined almost 140,259
answers were obtained by the 1617 founders of startups, 1065 supporters and 803 founders of

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startup in the future. The collected responses from the surveys were processed and filtered
including the reviewing of every participant manually for ensuring compliance with the
definitions. The complete sample of the statistics for the report consists of almost 777
substantiated founders of startups, the 321 future startups and the supporters of 654 startup.
The combined rate of response for every question differs as the responses of survey were not
made mandatory, the free written responses of texts were also allowed and the connection of
all these questions varied among the participants. The estimation that are provided in the
report are observed to be of cross sectional nature that refers to the figures for any provided
year and these are based on the sample of representative of response in vast population in that
particular year. The promotion of the survey was done using the platforms of social media
and the email and also using the personal interaction techniques. The Startup Muster
determines any startup as the early stage business, which consists of vast addressable market
where technology is utilised for capturing that particular easily. The main focus is on the
elements such as the speed, scale, timing and the technology that are commonly connected
with the startups. The estimation of the population was created using the partnership with
Data61 and the method of catch and release and the data of 5 years of Startup Muster for
building in time.
Summary of key points
The steps involved in the process of data science are the collection of data, assessment of
data, organising of the data, preparing the data, analysis of data and then reporting the data.
Some of the steps that are used in the data science are descriptive statistics, probability,
estimation, regression, classification, clustering, prediction/modelling, text analytics and
image analytics. It is commonly considered that the data can lie as it could mean anything or
absolutely nothing depending on the user of the data. Some of the data model are wrong and
some are observed to be useful. Some of the reasons why data are rendered useless are bias,

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sample size, data self-selection, noise, missing data, table or graph truncated, and
randomness. There are several consequences of using bad data in the businesses. The major
impact on the business who are using bad data is the loss of customers of business. The
businesses operating in the competitive environments gain minimal opportunities for
obtaining and retaining the customers. As soo as the customers feel betrayed by the
businesses they would leave. The damage on the reputation of the business is significant as it
would reduce the profits of the business and harm the future goals of the business. The
reputation of the company and the loyalty of the customers runs in parallel direction. If the
businesses provide damaged goods and services to the customers using due to the use of bad
data, it would lead to the loss of the customers from the business. It has been observed in
several situations that when any company uses the bad or damaged data, there is a significant
decrease in the revenue of the business. Some of the pitfalls for graph are observed to be
elastic axis, no axis, varying scale and variable not adjusted. The data can help the user as it
helps in the undertaking of decisions with confidence, enhances the efficiency of business,
gain insight about the customers, identifies the opportunities, and it offers a significant
competitive edge in the market. By utilising some cost effective methods, a lot can be gained
such as the free open source platform for analysing data, increased data that are publicly
available, collecting the respective data, investing in the business intelligence with
surrounding with any team or any data analyst, utilisation of specialised software, and
executing the competition using the scientists group or Kaggle.
Conclusion
Therefore it can be concluded that the startups in Australia are increasingly expanding
and the number of startups launched annually is increasing. The start-ups plays a crucial role
in the introduction of innovative services and products in the markets, and finally creating
opportunities for exporting, creating jobs and then delivering economic prosperity for

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everyone. It is essential that the Startup Muster manually reviews each of the participant in
the surveys and it only accepts the companies addressing a huge market in any scalable
methods that intends to eliminate the significant proportion of the self-identified startups. The
skills of the founders that are supposed to be the strength of the owners is highest in the
general business operations with 55.8% and the lowest surveyed skill that is found in the
business owners is in the stakeholder relations that is considered to have affected the
companies at a significant level. The respondents who previously participated in the period of
collection in 2017 were not required to participate in the surveys again as they were included
in the process of upgrading where the responses were verified by them and then it was
updated that were the new developments of the businesses.

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Bibliography
Anh, D.H. and Thuy, H.M., 2017. Innovative startup: experience from Australia. Journal
SCIENCE AND TECHNOLOGY POLICIES AND MANAGEMENT, 6(4), pp.97-114.
Bayer, L., 2017. Management of Supplier Relationships Specific Considerations for Startup
Companies.
Bliemel, M.J., Flores, R.G., de Klerk, S., Miles, M.P., Costa, B. and Monteiro, P., 2016. The
role and performance of accelerators in the Australian startup ecosystem.
Croll, A. and Yoskovitz, B., 2013. Lean analytics: Use data to build a better startup faster. "
O'Reilly Media, Inc.".
Harrington, K., 2017. Smart city leaders, champions, and entrepreneurs - the people part of
vibrant smart cities. In Smart Economy in Smart Cities (pp. 1005-1012). Springer, Singapore.
Kuhnke, R. and Schmidt, M., 2013. Update on the emergency paramedic law - jointly
developing concepts. rescue! , 2 (05), pp.298-302.
Miles, M.P., Battisti, M., Lau, A. and Terziovski, M., Policy Making Versus Policy Research:
The Case of the City of Sydney’s Tech Startups Action Plan.
Naughtin, C., McLaughlin, J. and Hajkowicz, S., 2017. Opportunities for growth: Driving
forces creating economic opportunities for Queensland companies over the coming decade.
Schallmo, D., 2013. Business Model Innovation. Basics, existing approaches, methodology
and B2B business models, Wiesbaden .
Stayton, J., Pitfalls of the Fast Startup.
Tassinari, L., 2016. Sustainability of Startups in Australia: a Policy-Maker Perspective.

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Universities Australia, 2017. Startup smarts: universities and the startup economy.