MBA633-Real-world Business Analytics and Management

Individual Case Study

Assessment 3

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Name of the Course Instructor

Executive Summary

The data-driven approach is hugely implemented in different organizations to improve their organizational decision-making, which is taken as the core topic of the present report. Analysis of this core concept was performed with the consideration of a technology hardware company, Nuheara. However, the aim of the report was to analyze the effectiveness of the data-driven decision-making process to mitigate the recent issues faced by the Company. Official websites of the Company, annual reports, and website of Stock exchange of Australia were taken as the source of information. Further analysis identified that the Company has issues with its revenue, profit margin and shareholders’ return, and new market entry decision. In such a context, the report was supposed to suggest detailed information related to the data-driven decision-making system. Textalyzer and Rapidminer were taken for the data preparation, where the SPSS tool was taken for the data analysis approach. Step-by-step analysis of usage of the SPSS tool was also represented, and the descriptive model of the evaluation was selected. The cluster map was suggested for the visualization of the data to the chief finance manager and marketing manager of the Company to make the decision data-driven. Further investigation of the effectiveness of the data-driven decision-making system mentioned that organizational performance would be improved with it as the organization will get an opportunity for self-improvement. Lastly, the paper recommended,

• To maintain the confidentiality and privacy of company information
• To respect the value of customers’ information
• To maintain the restricted flow of information,

as ethical consideration to the whole process of data-dependent decision-making approach.

Table of Contents

Introduction 3

Purpose 3

Background 3

Scope 3

Business understanding 4

Data Understanding 5

Type of Data 5

Data Sampling 5

Data Preparation 5

Data Analysis Method 6

Evaluation 7

Stakeholders 8

The Design of the System 8

Organizational Benefits and Consequences 9

Ethical Considerations 9

Limitations and challenges 9

Conclusion 10

References 11

Appendix 15

Share Performance of the Company 15

Industry data Set 15

Introduction

Purpose

Information, market insights, and environmental awareness are the bases to improve the decisions. Purposefully, the present report has aimed to focus on the problems faced by Nuheara Limited in its market operation and decision making to suggest the effectiveness of the data-driven decision-making process for the organization.

Background

Wu et al. (2020) specify data-driven decision making as a process of making a decision based on credible and actual real-world information rather than intuition and interpretation. Knowledge regarding the real-world scenario has improved the guidance to set organizational goals and objectives as per the need and conditions. As per Matzler et al. (2016), a collective intelligence has improved understanding of data-driven wisdom of followers towards the organizational goals and objectives. Nuheara Limited has selected Italy as a destination for revenue generation for the firm. Relating to the fiscal year till 30th June 2020, the Company has offered no dividend as per the directors’ preference as well as the total operational loss achieved in the same term has touched $11,690,733 (after-tax) (asx.com.au, 2020).

Graph 1 5 Years performance of the Company

(Source: Nuheara.com. 2020)

Starting from 2016, 5 years sales trend of the Company has been represented above, that has depicted the constant sales declination for consecutive two years.

Scope

Illustration of the business need and understanding about the solution has offered a scope to consider the data understanding and data preparation process into the present report along with the consideration of the data analysis process. Moreover, a detailed evaluation of the collected data has been performed to understand the stakeholders’ engagement and system understanding for consequential analysis.

Business understanding

• The review of the financial condition of the Company has shown a negative performance in net tangible asset booking performance. Comparison between the performances of 2019 has identified tangible asset booking as 0.01 cents per share, which reduced to 0.001cents per share (Nuheara.com. 2020). A data-driven financial decision-making process has been required to implement to mitigate the challenges. Srikanth (2014) opines that the statistical learning approach would be helpful to build a model or hypothesis for the transparent review of the hypothesis.
• Revenue for Nuheara has encountered a revenue decline of 1% in the last year, which amounted to $443658 (Nuheara.com. 2020). In spite of that much loss, Nuheara has raised $2.5 million for sales growth. However, the decline of the revenue has imposed financial pressure, where the additional raising created a market risk (Wsj.com.2021). Data based risk assessment approach has been required for the firm before taking the approach of crowd funding and capital raising program. Steele and Iliinsky (2010) comment that beautiful visualization of the collected information for the risk assessment has enhanced the understanding of the context.
• Focus on the last five years performance has identified that, the Company has observed a constant sales declination for three long years. Incurred loss for last year has touched the mark of $11,690,733, representing a decline of 6.25% and a per-share loss of 1.5 cents (De Freitas, 2020). Dean (2014) mentions the essence of big data technology for the business leaders for the improved decision-making process. Value creation for a business could be achieved with big data implementation to sense the vibe of the market.
• The approach of data-based algorithms and visualization process has been based on the retrieved data on the market strength (Younis, 2015). The Company has targeted to enter into the Italian market making a collaboration with the optical chain Vision Group (Tchetvertakov, 2021). Data mining and data collection regarding the collaborator could be collected for effective decision-making regarding the collaboration of the Company (Marr, 2015).

Data Understanding

Type of Data

Data required for the decision-making will be collected from the secondary sources relevant to the Company and the industry in which it operates. The search strategy for the data collection will be based on the keyword search strategy, relating to the dependent and independent variables (Srikanth, 2014). The dependent variable will be a dividend to the shareholders and net profit of the Company, where the sales performance and revenue will be the dependent variable. Data from the Newspapers and the annual reports of the Company will be selected, where the financial information will be selected for the data set (Nuheara.com. 2020). Moreover, the interest of the data analysis will be to understand the data-driven financial decision-making for the Nuheara company.

Data Sampling

The website of the European Union and the official website of the Australian Stock Exchange (ASX) has offered the relevant information regarding the Company. Further investigation for the annual performance of sales and revenue will be collected from the annual report of the Company (Chandrakumaraet al., 2018). Additionally, ASX will be the source of the stock performance of the organization. Further detailed information will be collected through the open-source search engine, Carrot2 as it defines the results into the different sections based on the string type (Petersen, 2021). Carrot Workbench and Foam Tree Visualization have improved the performance of market analysis and data collection regarding Nuheara. The data would be collected from the official site of the European Union website, Europa.http://iri.jrc.ec.europa.eu/sites/default/files/contentype/scoreboard/202012/SB2020_World2500.xlsxwould be the source to the information related to the financial performance of the technology hardware and equipment industry.

Data Preparation

Kulkarni and Shivananda (2019) opine that the Tokenization of textual data is an impressive way of presentation. Textalyzer and Rapidminer have been used for the data separation process as these two separate the individual words and phrases as well as their occurrences (Ergün, 2017). Representation of data with an auto slotting process improves the performance of Tokenizer software, where the frequency and prominence of information are clearly stated with mentioning the expression count (Ergün, 2017). Rapidminer comes in a more sophisticated way as it includes the normalized measurement and frequency of the words or phrases relative to the other words and phrases. Ciechanowski et al. (2020) opine that the data representation can be more advanced with the utilization of the data science technology and implementations of the Meaning Cloud excel add-on. Further research on the excel add-on has indicated that it can adjust the visualization process, filtering process, and valuation of the different elements. Furthermore, text clustering and tableau are also two important aspects of the data visualization process for data representation. Goh et al. (2021) comment that tableau and clustering processes are effective in detecting anomalies in data.

Figure 1 Sample of text Clustering

(Source: Goh et al., 2021)

Data Analysis Method

Analysis of information related to the financial documents will be done through the quantitative analysis approach utilizing the SPSS tool. Astivia and Zumbo (2019), commented that the multiple regression approach of SPSS offers strength for the data analysis understanding the explanatory factors. Several ways of data analysis options have created a facility for analysts to use this tool for data processing and analysis (Watkins, 2021). It has two basic parts, one is the data step, and another is the analysis step. The first step of the data stage is related to the variable declaration regarding the different types of financial elements that will be selected for financial performance analysis (Watkins, 2021). The immediate next stage is to create an excel file and merge it with the raw data. After variable declaration, the excel file is filled with the value of variables, and merging it with the raw data creates an SPSS data file for analysis. The data modification approach is taken for the regression analysis in this context. Further, variance analysis is performed to plot the graph from the raw data.

Figure 2 Steps of SPSS

(Source: Watkins, 2021)

Evaluation

Var =∑i=1, N (xi−xa)/ n-1 (Huang, 2021)

Measurement of the central tendency of the collected data set will enhance the evaluation process of the collected information. Furthermore, the common evaluation process is performed through the identification of the Mean, Median, and Mode of the data set. Mode is termed as the most frequent data, where the median is the midpoint of the ordered array. Gregr et al. (2019) has identified that the independent data evaluation process minimizes the complexity and helps to predict far better forecasts about the efficiency of the data. However, the descriptive analytical approach of the data collection approach increases the independence of the data evaluation process, which includes the conception of Sample Variance and Standard Deviation (Huang, 2021). Sample variance is an evaluation process that considers an average of the square of the deviations of all data points with respect to the mean of the sample set.

The manual expression can be performed in the excel sheet using the common of “= var.s(first cell:last cell)” (Huang, 2021). Spread of the data set and wider the range of information, usage of standard deviation process is utilized to understand the square root of the variance. The standard deviation has identified the spread and wideness of the information (Green and Stowell, 2017).

sd = √(Var)

Compared to predictive analysis models, the descriptive evaluation process will be best suited for the education of the data set. Though Sun et al. (2018) comment that predictive analysis has a multidimensional approach still, the descriptive analysis offers insightful, detailed information for analysis.

Stakeholders

The excellence of stakeholders in data processing and data management has improved the performance of data-driven decisions. However, the conversion analyst and data evangelist will be involved in the decision-making process of the company to tackle the issue mentioned (Arsenault, 2017). Data evangelists will help to rein and shape the data analysis process with the help of a conversion analyst. Moreover, data insight miners and sense makers will work for the understanding of the data and their analysis. The representation of the information will be submitted to the chief financial manager of the organization as well as the CEO, Justin Miller. The marketing department and Chief marketing manager, as well as the finance manager, will be responsible for the decision-making process (Arsenault, 2017).

The Design of the System

The interpretation of textual data from its designed process after the preparation has created an understanding of the data analysis model (Assarroudi et al., 2018). Moreover, the cognitive ability for the data analysis can be leveraged with the effective usage of the visualization process, understanding the data volume, data audience, and types of data included in the datasheet. Maps and Network type representation are the two best ways of representation for effective data analysis. Specifically, the Aduna Cluster Map Visualisation offers a scalable prospect for the decision to improve the data analysis process (Verbertet al., 2016).

Figure 3 Sample of Cluster Map Visualization

(Source: Verbert et al., 2016)

Organizational Benefits and Consequences

1. Sentiment analysis of an organization is the identification of the market impression about the Company. Utilization of internet-based data mining process of data presentation will help to perform the sentiment analysis, and market polarity can be understood to improve the product portfolio and performance (Rice et al., 2017). For example, using Survey-monkey tool or MTurk are two of them.
2. The text Clustering process will help to analyze the relevance of the Company in the global aspect. DBDM practices using provides an intervention on overall business goal and achievement (Faber et al., 2018). Nuheara has planned to move into the Italian market, where this process of data analysis will be helpful to understand the market performance of the Company (De Freitas, 2020).
3. Descriptive analysis and consideration of data variance will improve the business understanding for the Company to modify the organizational leadership for the improvement. The presented result will offer a chance to assess the reasons behind the losses and decline of revenue. It will be a chance for self-assessment (Hoffmann, 2019).

Ethical Considerations

Data is the sensitive element to disrupt an established business operation. The storage of information is subject to strong privacy and confidentiality protection (Gregr et al. 2019). Leakage of information can cause a serious data breach and a major threat to the Company. Hence, the ethical duty of the data sciences employed for the data analysis is to protect the confidentiality and the privacy of the information. Customer research for the data collection does not include the personal information of customers (Rice et al. 2017). The selling of customer information is strongly condemnable and a matter of criminal offense. Moreover, the flow of information should be contained within the managerial level and administrative boards. The information will not be shared with the common employees.

Limitations and challenges

1. Hoffmann (2019) comments that overreliance on the data-driven decision will elicit a blind trust over the information, rather than having reliance over the conception. A sense of self-doubt will be created with over-reliance on the data. Humanitarian workplace culture will be impacted.
2. Minor issues in the sample data set and a small amount of error will lead to a dangerous conclusion and will cause stakeholders to get trapped into misconceptions about the whole scenario (Faber et al., 2018).

Conclusion

The data-driven decision offers more reliability in understanding the effectiveness of a solution. Contextually, the information from credible sources has identified Nuhera has shown a net loss of $11,690,733 in the last financial year after the tax cut. The present share price of the Company has been regressed to 0.42, where the market capitalization has reached $72.37 million. Strategic analysis of contexts and evidence has identified some of the basic business needs of Nuheara. Textalyser and SPSS analysis will be helpful for the employees to understand the organizational capability to work in the market. Moreover, the organization will get a chance to review its decisions taken after a huge loss. Lastly, the new market entry decision will be verified with the data-driven decision-making process.

References

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Assarroudi, A., HeshmatiNabavi, F., Armat, M.R., Ebadi, A. and Vaismoradi, M., 2018. Directed qualitative content analysis: the description and elaboration of its underpinning methods and data analysis process. Journal of Research in Nursing, 23(1), pp.42-55.http://eprints.mums.ac.ir/17211/1/Directed%20qualitative%20content%20analysis%20the%20description%20and%20elaboration%20of%20its%20underpinning%20methods%20and%20data%20analysis%20process.pdf

Astivia, O.L.O. and Zumbo, B.D., 2019. Heteroskedasticity in Multiple Regression Analysis: What it is, How to Detect it and How to Solve it with Applications in R and SPSS. Practical Assessment, Research, and Evaluation, 24(1), p.1.https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1331&context=pare

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Ergün, M., 2017. Using the Techniques of Data Mining and Text Mining in Educational Research. Participatory Educational Research, 4(1), pp.140-151.https://dergipark.org.tr/en/download/article-file/776987

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Appendix

Share Market Performance of the Company

Figure 4 5 years’ Share Market Performance

(Source: WSJ.com 2021)

Industry data Set

(Source:http://iri.jrc.ec.europa.eu/sites/default/files/contentype/scoreboard/2020-12/SB2020_World2500.xlsx)