Data Analytics For Business Decision Making: A Case Study Of Kenya And South Africa

Importance of Data Analytics in Decision Making

In the present technological era, data seems one of the vital elements for an organization and helps to improve the process of decision-making process. According to Croll, and Yoskovitz, (2013), data analytics helps the firm with everything from personalizing the marketing pitch and optimizing the organizational process according to evaluation and findings. In the present data to build business decision, World Bank enterprise survey data has been evaluated and analysed. In order to analyse the data in proper ways, statistical tools and techniques are implemented that help to offer in-depth and insight information in efficient ways. The main objective associated with the present report is to provide an effective recommendation to the organization regarding opening a new branch in Kenya or South Africa.    

According to Wang et al., (2016), with development of the information and technology, data-driven decision-making has become one of the significant parts of the organization. An effective data-driven decision-making process has entitled to make decisions in proper ways and improve the decision-making process. The main objective of the implementation of the data-driven technology regarding information is that it helps to provide generalized information from a dataset that represents effectiveness for the organization. In order to find the decision in proper ways and make accurate decision data analytics tools and techniques including MS Excel is implemented (Ballou, et al., 2018). The main benefit of the implementation of the data analytics tools is that it assists to examine and evaluate a large amount of uncovered information in the short time period. Additionally, data analytics provide hidden pattern information; provide correlation, and employees behaviours which can aid to make an informed decision in better ways.

In context to the present situation, it has been obtained that in the dataset provided by the World Bank enterprise survey different attributes are included such as labour regulation, access to finance, taxation strategies, and so on. Evaluating and analysing the attributes in the database through statistical tools and techniques offer better results and help in decision making process. 

Selection of the question is more important in the present study that helps to provide more relevant information. During the selection of the country in African regions including South Africa or Kenya; there are different factors or questions have been selected. The first question which is used to find the results in efficient ways is the (Q1) L30a Labour Regulation. The data associated with the labor regulation provide clear information regarding the policies and laws implemented by the organization in both countries. Assessing and evaluating labour regulation has helped to provide information that labour regulation is favourable for a firm or not (Cole et al., 2019). 

The second factor or question selected from the database is the I30 Crime, theft and disorder and the main objective of the selection of this question is that it provides an in-depth report about the crime. Fourie, and Kerr, (2017), stated that large number of crime and theft has a significant negative impact on the performance of the firm. Therefore the data obtained from the study has indicated that which country has large number of theft and crime is not favorable for the organization. The third question for this assignment is the D30b Customs and trade regulations. Large custom and trade duties have impacted the sales and revenues performance of the organization. Therefore from this question, it has been determined which country offers better customer and trade duties to the firm. The fourth question is the J30e Political instability and it is a significant component of the organization. According to Adegboye, and Iweriebor, (2018), stated that political stability refers to the most important and vital to improving the performance of the organization.  J30f Corruption is another factor used in the present analysis as it provides information about the corruption reports in proper and better ways. According to Moyo, and Sibindi, (2022), stated that corruption deters the organization from engaging in public authority as well as limiting the expansion strategies. Apart from that J30c Business Licensing Permit factor is also selected for this study which helps to provide information regarding licensing and permitting information about the firm.      

Data Analysis of World Bank Enterprise Survey data

In order to analyse and evaluate the data statistical tools using MS Excel software is implemented which helps to determine the pictorial presentation of the information in proper and efficient ways. Additional in data analysis and evaluation section Mean, Median and SD is implemented which provide information insight manner. The coefficient correlation testing techniques also performed which aids to offer and provide the relationship between variables.       

Table 1South Africa respondent response about six factors

Finding and analysis: The above presented table and below graph has indicted that largest number of respondents have stated that no obstacles in context to labor regulations in South Africa (746 out of 1097). On the other hand, the finding also interpreted that majority of the participants i.e. 300 out of 1097 stated that labour regulation has minor obstacles, crime, theft and disorders has minor obstacles, and business licensing has minor obstacles on the performance of the organization. A very few respondents i.e. 12 out of 1097 have stated that these factors have significant obstacle on the performance of the firm.                    

Figure 1South Africa respondent response about six factors

Table 2Kenya respondent response about six factors

Finding and analysis: The above figure and represented figure it has been interpreted that majority of the participant in favor of the obstacle i.e. out of total 997 about 182 stated that moderate obstacles, 66 stated that major obstacles and 346 stated that minor obstacles. Instead of this it has been obtained that all factor including Labour Regulation l30a, Crime, theft and disorder i30, Business Licensing Permit j30c, Customs and trade regulations d30b, Political instability j30e and Corruption j30f have moderate obstacle on the performance of the organization in Kenya.               

Figure 2 Kenya respondent response about six factors

In order to obtain the relationship and associated among the variables and its impact on annual sales performance coefficient correlation testing techniques is performed. According to Black, (2019), stated that value of coefficient correlation lies in between -1 to +1 and +1 here indicated that strong relationship and -1 indicated that no relationship. The finding from the assessment has indicated among these factors poetical factors(r=0.48), business licensing permit (0.46) and corruption (r=0.50) have major impact in process of decision making process in selection of South Africa as these factors have positively impacted the sales performance of the organization.   

Table 4Correlation between six factors and annual income of South Africa

From the coefficient correlation finding and analysis it has been determined that corruption and political instability i.e. r=0.29 and 0.25 have significantly impacted the performance of the organization in Kenya. Thus during the process of decision making process management of the organization needs to more focused on these factors.     

Table 5Correlation between six factors and annual income of Kenya

The present study is conducted the statistical analysis tools and techniques in terms of mean, mode and median. Mean is refers to the arithmetic mean whereas median value in statistical analysis technique is the middle reading when arranged in numerical order.  

Selection of Questions for South Africa and Kenya

Table 6 Mean mode and median South Africa Vs Kenya

From the above presented table it has been obtained that the man value of the labor regulations of South Africa is 1.29 and Kenya is 2.26 that shows that in South Africa there is minor obstacle in context to Kenya. While assessing and evaluating the above finding it has been obtained that custom and trade regulation has major obstacle [Mean=3.15, Median=3 and SD=1.287]; whereas in South Africa it was minor obstacle [Mean=1.37, Median=1 and SD=.653]. Instead of this it has been determined that political instability also seems one of the main factors which impacted the performance of organization in Kenya [Mean=2.77, Median=3 and SD=1.356] as compared to South Africa [Mean=1.76, Median=1 and SD=1.218].                

Figure 3 Obstacles South Africa Vs Kenya

The above represented figure has indicated that 68% of the total respondents in South Africa has stated that there is no obstacles for doing business, whereas remaining 28% stated that minor obstacles, 3% stated that moderate obstacles and 1% stated that major obstacle. Additionally, the finding obtained from the Kenya database indicated that 42% of the total respondents stated that minor and major obstacles whereas 18% of the total respondents have stated the moderate obstacles. Moreover the finding also indicated that 40% of the total respondents in favor of no obstacle. Thus assessing and evaluating the above facts and finding it has been stated that South Africa destination is the best for organization for opening new branch in Africa region.                     

The enterprise survey by worldwide bank has focused on different aspects of the firm environment. These factors can be constraining or accommodating for organization and plays the significant roles for organizational performance. The finding has indicated that political instability and crime, theft and disorder are the major factors in process of decision making process. In Africa region, South Africa’s political instability [mean=1.76, SD=1.2] and crime, theft and disorder [mean=1.67, SD=0.869] indicating minor obstacles. Whereas from the finding it has been obtained that in Kenya political instability mean is 2.77 and crime, theft and disorder mean is 1.97 indicating moderate level of obstacles(Jeble, et al., 2017).  Security expense are higher in Kenya as compared to the South Africa because higher crime rate and if the firm open its brand in Kenya then firm needs to pay for additional amount for security services. Apart from that the finding also indicated that loses due to theft and crime also declined sales performance of the firm. Additionally from the observation and finding it has been obtained that in South Africa 32% of the total respondents stated that major to moderate obstacle for organization to performance their organizational activities. Whereas 68% of the remaining respondents have stated that in South Africa has not obstacle for firm and organization doing its organizational activities in proper ways. On the other hand finding indicated that 60% of the total respondents in Kenya have stated that firm face obstacle for doing their operational activities whereas 40% stated that there is no obstacle. Considering this aspects the management of the organization needs to open their organizational activities and brand in South Africa as compared to Kenya.

Form the finding and assessment following there are following recommendation which the management of the firm need to implement before opening new branch in South Africa;

• First and foremost recommendation is that management needs to perform additional market research before selection of the destination means firm needs to conduct external environmental analysis using pestle tools which aids them to provide clear picture about the country(Adegboye, and Iweriebor,  2018)
• Instead to reduce the corruption issues in their organizational activities firm needs to conduct proper training and development program and also incorporate better monitoring tools
• Organization needs to implement better security protocol and strategies in order to reduce the crime, and theft in their organizational activities          

Conclusion 

From the finding and observation  it has been concluded that South Africa is the best destination as per the world bank enterprise survey as compared to the Kenya and the main reason of this is the better political stability and custom and trade regulations. Additionally, the finding also indicated that before making decision about the planning and opening new branches in different countries management of the organization needs to focus on the corruption, political instability, custom and trade regulations, business licensing permits, crime, theft and disorder and labour regulation strategies. The finding also summarized that in Kenya political instability; custom trade regulations and labour regulations are the main obstacle for firm and impact the origination sales and revenues performance in negative manner.

Reference

Adegboye, A.C. and Iweriebor, S., 2018. Does access to finance enhance SME innovation and productivity in Nigeria? Evidence from the World Bank Enterprise Survey. African Development Review, 30(4), pp.449-461.

Ballou, B., Heitger, D.L. and Stoel, D., 2018. Data-driven decision-making and its impact on accounting undergraduate curriculum. Journal of Accounting Education, 44, pp.14-24.

Black, K., 2019. Business statistics: for contemporary decision making. John Wiley & Sons.

Cole, R.A., Dietrich, A. and Frost, T., 2019, December. SME credit availability around the world: Evidence from the World Bank’s Enterprise Surveys. In Midwest Finance Association 2013 Annual Meeting Paper.

Croll, A. and Yoskovitz, B., 2013. Lean analytics: Use data to build a better startup faster. ” O’Reilly Media, Inc.”.

ENTERPRISE SURVEYS WHAT BUSINESSES EXPERIENCE South Africa. 2022. enterprisesurveys. [online] Available at: <https://www.enterprisesurveys.org/content/dam/enterprisesurveys/documents/country/South-Africa-2020.pdf> [Accessed 23 April 2022].      

Fourie, F. and Kerr, A., 2017. Informal sector employment creation in South Africa: What can the SESE enterprise survey tell us?. Research Project on Employment, Income Distribution and Inclusive Growth (REDI3X3), University of Cape Town.

Janssen, M., van der Voort, H. and Wahyudi, A., 2017. Factors influencing big data decision-making quality. Journal of business research, 70, pp.338-345.

Jeble, S., Kumari, S. and Patil, Y., 2017. Role of big data in decision making. Operations and Supply Chain Management: An International Journal, 11(1), pp.36-44.

Moyo, B. and Sibindi, A.B., 2022. Does bank competition affect credit access in Sub-Saharan Africa? Evidence from World Bank informal firms surveys. Journal of African Business, 23(1), pp.180-198.

Wang, H., Xu, Z., Fujita, H. and Liu, S., 2016. Towards felicitous decision making: An overview on challenges and trends of Big Data. Information Sciences, 367, pp.747-765.

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