One of the key indicators of an organization’s sustainability and growth is its performance. Veyrat (2016) in his article on business performance argues that apart from profit and loss being considered as the main performance indicators, there are other factors such as employee and customer attrition, stock value etcetera which are taken into consideration when measuring the performance of a business. As such, business performance is defined as “the operational ability of a business to satisfy the desires of the firm’s major stakeholders.” (Zulkiffli and Perera, 2011).
The systematic methodology of inspecting, cleaning, transformation and modeling of data aimed at obtaining important insights is known as data analysis (Bihani and Patil, 2014). In a business set up, such insights from data analysis are used to solve business problems and aid in digital operations.
Day to day operations in business firms are prone to change, such changes are brought about by external factors which include: competition, policy changes, seasonal effects on the market as well as internal factors such as management, employee attrition, etcetera. Business changes therefore require adjustment of the management actions to address the ever emerging business problems thus necessitating steps such as data analytics.
The purpose of the analysis is to examine the use of data analytics in a real life case scenario as given in the 2018 startup muster report
The paper is divided into three sections, that is: introduction, body which consists of the case synopsis. The case synopsis is divided into case summary which entails data collection and management, founder profile, founding team profile, business profile, and funding. The last section of the report provides a summary of the key points as noted by the guest speaker.
Use of secondary data forced reliance on the report as accurate which might not be the case. In addition, the problem of knowing exactly which visualization tool is the best for visualizing a given data aspect proved elusive.
Analysis of the startup ecosystem in Australia in the year 2018.
Data in the 2018 startup muster was collected through conducting an online survey open from 14th August 2017 to 13th August 2018. The sample data included 3476 respondents who answered a total of 140, 259 questions. However, upon respondent validation, only 1752 respondents were included in the final sample data. The data estimates are cross-sectional that is, figures for a particular year are inclusion of a representative sample of the whole population. The validated and cross-tabulated dataset is then used in descriptive analysis.
From the report analysis as of 2018, there were approximately 1465 startups in Australia with an estimated 782 launched between 2015 and 2016, 1291 launched between 2016-2017 and 712 launched between 2017 and 2018.
Whereas 77.1% of the startups’ founders are male, only 22.3% are by female (figure 3). From figure 4, the report uses horizontal bar graphs to indicate that most of the founders having a bachelor’s degree i.e. 25.4% followed by master’s degree holders accounting to 22.6% of start-up finders. Industry accreditation founders make up the least percentage i.e. 0.3%. However, another suitable graph would be a pie-chart due to its ability to outline proportions.
That is approximately 1351 of the respondents are male while 391 are female.
Founders of the age bracket 35 and 40 make up to 20.2% of the startup founders population while the least number of founders are those above the age of 60 at 6.3%. In addition, the report indicates that 40% of the future founders are from outside Australia while 35.7% of the startup founders are from outside Australia. Moreover, 20.1% of the future founders are still in school with most of the attended educational institutions by founders being as indicated in figure 5 where most founders are from New South Wales University and the least are from Adelaide University:
The report uses tree maps, bubble charts, horizontal graphs to examine the different factors related to the composition of the founding team profile. For instance, on average, there are approximately 29.5% of startups founded by a single member, 41.8% by 2 members, and 18.8%, 5.3%, 4.7 founded by 3 and 4 founders and 5+ founders respectively.
The use of the above tree map could have been more conveniently replaced with a bar graph since apart from indicating frequency, it also enables visual comparison.
Further analysis indicates that most of the skills held by majority of founder members are general business operations at 56.1% while very few original founders have event management skills at 6.1%.
The most common hindrance factor to founding of start-ups is life circumstances that necessitate a stable income accounting 37.5% of the startups founded. Use of a line graph is the best choice in demonstrating the frequency of hindrance factors.
Up to 84% of the employees by end of reporting year 2017-2018 had equity which is an all-time high with an increase from 78.9% in 2017 and 75.8% in 2016. A suitable alternative visualization method is the line graph which is used to indicate trend in data.
48.8% of the startups are located in the New South Wales region hence reflective of the number of founders who studied in the region’s university of New South Wales. Tasmania has the least number of startups which comprise 2.1%. The use of a map as in the report is good for indicating regions.
In the question regarding funding of the startups, most of the startups are funded by the founders i.e. 64.4% with private equity in Australia funding up to 29% startups. The local and federal government account for the funding of 3.2% and 6.3% respectively.
the tree map indicates that there is a difference in the equity splitting among the founding team of the startups. Such a difference in frequency can be alternatively be visualized using a bar graph.
Data analysis acts as a tool to enable development of an evidence-based mindset in business problem solving. Majorly, data analysis skills comprise quantitative, qualitative, visualization and decision making skills. As such, there is a difference between data analysis and statistical analysis. Some of the differences are given in table 1 below:
|
Data analytics |
Statistical analysis |
|
Employs data mining in analyses |
Employs statistical and mathematical procedures in analyses |
|
Examines relationships between different data parameters including trend etc. |
Its uses is founded on well-laid theories |
|
Relationships and trends are mainly examined through data visualization |
Utilizes statistical significance levels to prove or disprove pre-defined hypotheses. |
In business, data analytics uses techniques such as: trend, relationships, composition, variation and logical consistence between data variables.
Data can be viewed as a composition of items which take a number of forms through which its analysis and interpretation provides useful insights. Hence, data is helpful in the sense that it aids in: business decision-making, identification of shortcomings, needs, issues and as such can be useful in the process of problem solving.
There is a number of collecting both continuous and discrete qualitative data or quantitative data which can be used in analysis. In sample choice after preparing a survey, one ought to select people randomly to avoid bias. Surveys are therefore one of the popular means to collect data on various issues requiring opinion. After data collection, the data can be presented and analyzed through various means such as statistics and data visualization.
There are two groups of statistics that is, descriptive and inferential statistics.
Descriptive statistics concerns itself with describing quantitative and qualitative data thorough use of tables and graphs. Moreover, bivariate and numerical descriptions provide information on data averages, variability and analysis of more than one variable. In descriptive statistics, frequency descriptions are a dominant way of presenting quantitative data. In most cases, tables and graphs are employed in frequency distribution analysis. One such graph is a histogram which is a bar graph of frequencies indicating how frequent a given data point occurs. Particularly, histograms are useful in giving the general distribution of the dataset that is through interpretation of its shape i.e. bell-shaped, right-skewed, left-skewed, and bimodal. Other important descriptive statistics include the mean, median, variance, range and mode.
Business intelligence is described as theories, procedural methods, architectures and technology which enable transformation of native data into sensible and useful insights for business use. Hence business intelligence involves various systems through which it conducts data collection, data storage and data analysis. Humans play a role in BI through conducting: descriptive, predictive as well as diagnostic analytics. BI facilitates seamless and efficient process of data collection, analytics, human input, decision generation and suggestion of applicable business actions.
For BI to be efficient it requires:
After BI establishment, analysis and content creation is conducted, this involves: embedded advanced analytics, analytics dashboards, interactive visual exploration and lastly follows the discovery, exploration and sharing of findings.
The most common BI tools are:
Each BI tool has their own weakness and strength and hence choice of BI is through consideration of available budget, business needs and insights sought as well as reliability.
Conclusion
In general, the report employs the use of frequency distributions in providing descriptive data analysis on various aspects of Australian startups in the year 2018. As seen earlier, a number of visual representations aid in the visualization of the frequency distributions.
Conclusively, the use of visualizations and data analysis enables gaining of critical insights regarding various business aspects which facilitate understanding of the whole business ecosystem as in the case of Australia’s startup ecosystem. Moreover, exposure to the data analysis module and use of a wide range of resources has enabled me to increase my data and statistical analysis skills such as use of descriptive statistics to examine data distribution.
References
Bihan, P. and Patil, S. (2014). A Comparative analysis of Data Analysis Techniques. Emerging Trend and Technology in Computer Science, 3(2), pp.1-7.
Veyrat, P. (2016). Five Strategies for Improving Business Performance Management [Online]. Available from: https://www.heflo.com/blog/business-management/improving-business-performance/ [Accessed 21st December 2018]
Zulkiffli, S. and Perera, N. (2011). A literature analysis on business performance for SMEs- Subjective or Objective measures. Social Interdisciplinary Business Research [Online], 3(12), pp.1-9. Available from: https://pdfs.semanticsscholar.org/d953/5161b7e941f101015bd63d7dd44104cffd3.pdf [Accessed 21st December 2018].
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