MBA504 Data Analytics for Business Assignment Sample
Word Count: 1500 Words (+/-10%)
Weighting: 30 %
Total Marks: 30
Due Date: Tuesday, Week 7 at 11.55 (AEST)
This assessment requires you to read and interpret a report written by McKinsey Global Institute. The report analyses trends following the impact of Covid-19, such as remote work and the uptake of digitalization and automation in work processes. McKinsey Global Institute research combines the disciplines of economics and management, employing analytical tools with the insights of business leaders. This report explores the post Covid work environment in 8 countries from a range of perspectives and metrics.
Assessment Instructions for assignment help
In this assessment, you need to:
Part A (1000 words)
• Study “The future of work after COVID-19” report from a data analytics perspective and provide an analysis incorporating the key points. A synopsis without analysis is insufficient to satisfy the requirements of the assessment.
a. The key insights, commentary on methods used, critique of the presentation methods and visualisations as well as possible improvements should be described. This should be addressed based on your learnings from the course.
a. Derive and quote and describe appropriate statistical metrics from the report
b. Suggest alternative graphical or visual representation
c. Comment on the data collection and management.
Part B (500 words)
• Reflect on the key takeaways from this report, specifically those regarding the importance of developing your data analytics skills.
The COVID – 19 Pandemic has had an impact on day-to-day life and approx all other aspects related to it. This also has its impact on world economies and globalization, as it directly hit the labour working and supply chain, which are interrelated with each other due to globalization.
Here, the report presents a study and analysis of the MGI (McKinsey Global Institute) report on “The future of work after COVID?19". This research examines several aspects of global economies which have a long-term impact on several work areas and diverse labour markets and states their pre and post-COVID conditions.
• In this physical proximity of work and work force along with businesses models and consumer behaviours or all taken into consideration, and the different diverse labour markets that are chosen for this study are United States, United Kingdom, France, Germany, India, China, Japan and Spain (Amankwah et al., 2021).
• It has shown different occupation transitions and their increment rate predicted in the report is 25% by 2030.
• Report shows demand shifting across occupations and their percentage change it’s shown in the report.
• Proximity scores over different kind of human interactions and work environment presented is also shown in form of a matrix and given out a score out of 100.
• Trends before and after and reducing the pandemic are shown in the report in various kinds of occupations and businesses.
• It is also shown the data that percentage of 2018 workforce and present demand for potential of remote work in higher advance economies along with number of workers in percentage who can work remotely for 3 to 5 day in a week, it is only 25-30 percentage of work force.
The key stakeholders are:
• Global Communities
While preparing a report on “The Future of work after COVID-19”, MGI have chosen different occupations and made a cluster of them, by preparing five metrics and dividing them into 10 arenas, they have followed both qualitative and quantitative approach.
The analysis of different trends that are obtained after COVID and pre-COVID trends both are examined and based upon them different graphical representations are made, showing various changes in terms of working habits of people, changes in demands, the potential for remote work, occupation transitions, labour growth demand, digital and automation adoption and industries which were more sustainable during such times.
This represents, change in trends and projected different upcoming trends, for workforce and businesses, and also provides a deep study about occupations transitions and their necessity to focus from now,
Critical Analysis of Methods and Improvements
The MGI report is prepared by assessing the potential impact of covid-19 on workforce and their analysis is based upon their occupation and work activity. While taken into consideration the different clustering of work arenas and their reflections are made.
In order to identify major impact more than 800 occupations are chosen and these are having their different criteria’s such as the required in-door or outdoor working or what kind of work practices they followed. In this potential is also taken out so that different activities and occupations that are required to be perform to physically now can be done via remote work or not.
After taking consideration of all such data certain graphical representations are made which again suggest some kind of figures and facts. This shows that different kind of human interactions and work environment requirements in various aspects such as medical care, personal care, on site customer interaction, Home support, computer based office work, transportation of goods etc or all taken into consideration and based upon them certain proximity scores are obtained (Blit, 2020).
Figure 1 Evaluation of demand over pandemic period
(Source: Lund et al., 2021)
In this world developing and making analysis of this kind of data bar graphs can also be developed as they have much easier and understanding and it is easier for reduced to extract facts and figures and data from those graphs. In this 6 major economy of world chosen which involves United States, China, Japan, Germany, United Kingdom, France and India.
Data Collection and Management
In this the data is being chosen from LinkedIn for number of members who moved to small cities as compared to the large cities into United States of America in a time span of 2020 as compared to 2019. Also for countries the data of recovery are chosen from October 20-20 report of MGI over consumer sentiments during corona virus pandemic. Apart from this the data is being chosen from the Federal Reserve Bank of Philadelphia September 2020 report upon showing early trends from survey population (Couch et al., 2021).
While talking about human proximity data over workplace enter requirement of physical proximity and retailing with customers face to face discussions and environment over the work are all scored based upon us department of labour data. Also for standard occupational classification code the graphical representation given in this report uses 6 digit standard occupational codes and based upon them it provides percentage points over different countries on a wage criteria basis.
The 5 characteristics that are shown in report are chosen from the data by O*NET online and similar different sources. Based upon this data collection, the graphical representation of countries showing proximity scores for work places are presented.
Alternative Graphical or Visual Representation
For developing this report line charts can be used for displaying different kind of current trends, as line graphs are popularly recognised for their demonstrate capability of different trends in a concise and Swift manner. They are also helpful in indicating various kinds of representations over a single graph. Along with this bar charts can also be used in compare in different values and clusters that are made for different occupations and cities data represented in report (Chung et al., 2020).
Also to compare side by side values and data column charts are preferred and can be used in report show that visualisation of change of the market and business in terms of growth and their other characteristics. Also area charts are can be used in this to represent change in work culture and different kind of new occupations and work practices that are required and also comparative study of them before and after the COVID - 19.
COVID – 19 was the worst pandemic that has hit the world in recent decades, and its impact was also high, such that supply chains are gets affected by this, and many countries are having high inflation and employment rates due to this pandemic. Even after two years, the impact of this is clearly visible.
Here, the physical proximity of work and work force along with businesses models and consumer behaviours are all taken into consideration, and the different diverse labour markets that are chosen for this study are United States, United Kingdom, France, Germany, India, China, Japan and Spain (Hodder, 2020).
The people and companies have changed their way of working, and instead of work which required physical presence, works which can be done remotely have attracted more people. As remote work looks like a more compatible and flexible methods of working, its demand was high, companies are also investing less in preparing workspaces and are focusing of remote work.
The physical dimension of work, Businesses policies and Workforce transitions in large scale and so on are included in factors which are impacted with COVID -19 pandemic.
Figure 2 Human interaction and work environment score
(Source: Lund et al., 2021)
The report shows that high wages occupation jobs are increasing and declining in a low wage occupation. This suggest that the scale and nature of transition of work force are very challenging in upcoming years and approx 107 million workers are needed to find different occupations by the year of 2030, as they are not able to compete with change in the demands and trends after Covid-19.
Figure 3 Representation showing potential remote work without losing effectiveness
(Source: Lund et al., 2021)
The above graphical representation shows that percentage of workforce who can do work from remote places, without losing effectiveness. And this chart shows that in developing countries, people can bear more load and not lose much effectiveness as compare to developing countries (Butterick et al., 2021). More than 800 occupations are chosen and these are having their different criteria’s such as the required in-door or outdoor working or what kind of work practices they followed.
Figure 4 Yearly Growth in ecommerce representation
(Source: Lund et al., 2021)
This graph represent year over growth of e-commerce retail sales and the period of 2019 - 2020 is chosen and annual average of 2015 - 19 is also shown and projected growth based upon these trends are presented.
There is a necessity for workers to learn different emotional and social skills as well as technology skills so that they can move to different high growth opportunities and higher wage bracket.
It is typical for the people to make transitions between occupations as they need to learn the different opportunities which are evolved and learn new skills for them. Also this report suggest that less educated workers, immigrants women’s and ethnic minorities are needed to make more occupation transition after this pandemic and they may need to face more challenges as compared to other people.
Chung, H., Seo, H., Forbes, S. and Birkett, H., 2020. Working from home during the COVID-19 lockdown: Changing preferences and the future of work.
Couch, D.L., O'Sullivan, B. and Malatzky, C., 2021. What COVID?19 could mean for the future of “work from home”: The provocations of three women in the academy. Gender, Work & Organization, 28, pp.266-275.
Blit, J., 2020. Automation and reallocation: will COVID-19 usher in the future of work?. Canadian Public Policy, 46(S2), pp.S192-S202.
Amankwah-Amoah, J., Khan, Z., Wood, G. and Knight, G., 2021. COVID-19 and digitalization: The great acceleration. Journal of Business Research, 136, pp.602-611.
Hodder, A., 2020. New Technology, Work and Employment in the era of COVID?19: reflecting on legacies of research. New technology, work and employment, 35(3), pp.262-275.
Butterick, M. and Charlwood, A., 2021. HRM and the COVID?19 pandemic: How can we stop making a bad situation worse?. Human Resource Management Journal, 31(4), pp.847-856.
Lund, S., Madgavkar, A., Manyika, J., Smit, S., Ellingrud, K., Meaney, M. and Robinson, O., 2021. The future of work after COVID-19. McKinsey Global Institute, 18.