Data Analysis, Problem Solving and Digital Operations: Report Synopsis

This report is a synopsis of the Startup Muster Annual Report, focusing on data analysis, problem-solving, and digital operations within the Australian startup ecosystem. It begins with a foreword from the Minister for Industry, Science and Technology, highlighting the government's support for startups and introducing the Startup Muster's mission to assess the opportunities and challenges facing the Australian startup landscape. The report examines the growth and progress of the Startup Muster, detailing the methods used for data collection, which included an online survey with a large number of responses from founders, supporters, and potential initiators. The report's findings are presented using various visual methods, such as line graphs, pictorials, bar graphs, and pie charts, to analyze the profile of Australian startups, including their founders, founding teams, business profiles, funding, and future prospects. The report also critiques the presentation methods and discusses the data collection and management processes. The data was collected online, and then underwent a comprehensive post-survey validation and cleaning process to ensure accuracy and reliability. Overall, the report provides valuable insights into the current state and future of the Australian startup ecosystem.

Running head: REPORT SYNOPSIS 1
Data Analysis, Problem solving and Digital operations
Institution
Student
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Data Analysis, Problem solving and Digital operations
Report synopsis
Key insights
The author of this report starts with a foreword from Hon Karen Andrews MP, Minister for
Industry, Science and Technology who is expressing her gratitude to the Coalition Government
for supporting startups. She also introduces the Startup Muster, some of its accomplishments,
purpose and mission. As she states, Startup Muster was initiated to draw attention to the
opportunities, progress, and problems facing Startup ecosystem in Australia. In this report, the
Startup Muster team also expresses its sentiments regarding the growth progress of Startup
Muster and the rate at which they are achieving their mission. Every year the team is setting a
new record in terms of achievements in improving Australia’s ecosystem as well as the number
of participants who help them gather data for their annual reports (Startup Muster, 2018).
This report, based on online survey, seeks to determine the number of startups in Australia, their
founder’s profile, and founding team profile among other pertinent subjects such as the current
team profile, business profile, funding, future of startups, hindsight, and resources where to find
help. Startup Muster in this report has engaged Data61, global leaders in data science research to
provide approximations on the Australia’s number of startups for a period of five years. Besides,
statistics of the people starting startups have been compiled including their gender, age, levels of
education, their skills, and places of origin among others. Founding team profile has also been
examined with key statistics like their experiences being investigated. From the statistics of this
report, it is evident that startup ecosystem in Australia is accelerating and as Margaret Maile

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Petty Executive Director, Innovation and Entrepreneurship, UTS, says, the future is very
promising considering the anticipated innovation of graduates (Startup Muster, 2018).
Commentary on methods used
Data used to compile this report was collected online for a period of one year wherein 67% of
responses were received. Main respondents included startup founders, future startup initiators,
and startup supporters like educators, government, and investors. A total of 140,259 responses
were provided and all of them went through thorough post survey substantiation and cleaning
process. Startup Muster used various techniques to promote this survey including LinkedIn,
Facebook, email, and Twitter among other platforms. 5 years of Startup Muster data and Catch
and release method was used to develop the population estimate in conjunction with Data61
(Startup Muster, 2018). To some extent, the methods applied to collect data for this survey can
be said to have been effective and reliable. They cut across by enabling every individual,
organizations, and bodies in association with startups to contribute in the survey process. In
addition, Startup Muster provides a definition which perhaps was used to put all participants
through a sieve before considering their responses. The exhaustive post survey validation like the
one conducted in this investigation gives the surveyor an opportunity to provide a rationale or
underlying principles of their final statistics (Nigel, Fox, & Hunn, 2009). In this regard,
therefore, it is correct to argue that the statistical estimates of this report are accurately
supported. They provide a trustworthy indication of improvement pace of Australia’s startup
ecosystem.
Critique of the presentation methods and comment on the data collection and management

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Results of the online survey are presented using various methods such as line graphs, pictorials,
bar graphs, and pie charts among others. The report author has also used visual representation
technique to present the findings of the research. For instance, estimated number of startups, by
survey year is presented using a line graph and from the depiction from 2015 to 2017, the
number increased significantly but fall again from 1675 to 1645 in 2018. Agreeably, most of the
methods the author uses to represent the findings are very easy to understand because they are
straightforward. It is so very easy for even a layman to comprehend the founder profile statistics
since the presenter uses a mixture of visualizations like pictorials and simple circles to display
this information. For example, from the pictorials, it is easy to understand that out of the total
number of people starting startups, 77.1% are male while the rest, 22.3% are females (Startup
Muster, 2018).
However, the author of this report uses some presentation techniques and visualizations which
might be difficult for a layperson to understand fully what is being presented. For instance, the
presenter has used pyramids which might require the user of the information to have exceptional
interpretive skills for him/her to comprehend information being conveyed. Take for instance, the
representation of financial runaway in months. The way this information is portrayed might
appear easily understandable to an expert only, however, to an inexperienced user it might be
complicated. Though I agree it is an efficient approach of representing survey findings, it is
worth noting that not everybody who will be in a position to comprehend the details of the
visualization. Use of diagrams, symbols, illustrations, and photography would have served well
as alternative graphical or visual representations (Quillin, & Thomas, 2015). In addition, the
information presenter can make his representation methods more vivid by say making them more
comprehensible and straightforward. Startup Muster can reduce the number of characters for

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every presentation. For example, instead of using 18 hindrances to founding a startup, use 10 of
them. In so doing, the presenter will make it possible for every user to understand the
information being disseminated with much ease.
Data collection in this survey was carried out online and main respondents included people
engaged in startups, people who offer support startups and in general respondents of this survey
constituted a list of start-up businesses who had been ‘captured’ for investigation purposes. The
process of data collection relied on five assumptions which if violated could affect the validity of
the survey results. After collection, the data was subjected to a comprehensive post survey
validation and cleaning process in order to augment its correctness and reliability. Needless to
say, data collection and management by Startup Muster was an effective and undoubtedly it
enabled the team to achieve its survey objectives.

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References
Nigel, M., Fox, N., & Hunn, A. (2009). Surveys and Questionnaires. The NIHR Research Design
Service for Yorkshire & the Humber. www. rds-eastmidlands. nihr. ac. uk.
Startup Muster. (2018). Startup Muster Annual Report. Accessed on 30th December 2018.
Quillin, K., & Thomas, S. (2015). Drawing-to-learn: a framework for using drawings to promote
model-based reasoning in biology. CBE—Life Sciences Education, 14(1), es2.