7117IBA Business Intelligence

7117IBA Business Intelligence

Assessment 1 – Annotated Bibliography

Name and ID

Word Count = 2006 words

Introduction

From the case study in Assessment 1, one of the main pressing issues and opportunities emerging is how to use data and business intelligence tools to increase sale activities and help businesses to specifically increase accuracy and timeliness of its sales forecasts in target markets. According to Kiani and Standing (2018), business intelligence allows an enterprise to combine a number of business activities such as infrastructure, business analytics, data visualization, data mining and data tools with best practices to help push firms towards better decision making. The 21st century business environment demands businesses to have a comprehensive perspective of the organization in order to use all kinds of data to effect change, reduce inefficiencies, and adapt to the many market changes that may occur. For example, well-prepared organizations have only had to use business intelligence as a tool to adapt to the changes that the COVID-19 pandemic has placed on global businesses. Business intelligence have helped organizations in different sectors to make critical decisions relating to the need to make changes, reducing inefficiencies as one way of responding to the loss of business, and how to rapidly adapt to the changes in market demand and supply. Fan, Lau, & Zhao (2015) see business intelligence as an opportunity to transform data into business opportunities because it allows the creation of business strategies based on the data available and the processes available. The following article will be analyzed in a way to identify how businesses use business intelligence, how they collect meaningful data, and how these elements can all be converted into business opportunities and a competitive advantage for the organizations. The analysis will include the aim of the study, findings, and the recommendations made for the organization.

Annotated Bibliography

Loureiro, A. L., Miguéis, V. L., & da Silva, L. F. (2018). Exploring the use of deep neural

networks for sales forecasting in fashion retail. Decision Support Systems, 114, 81-93.

In this article, the authors explore the highly competitive fashion retail sector. Their focus is on how business in this industry are continuously developing strategies focused on staying ahead of the competition through gaining a competitive advantage. One of the critical aspects explored in the research is the role of artificial neural networks and the role this plays in helping business perform more accurate and timely sales forecasts. The aim of the study is to analyse the use of deep learning strategy and business intelligence to forecast sales within the fashion retail industry, helping to predict sales of new products in the future. The authors use real datasets to create practical scenarios for a fashion company. The article identifies the main variables for organizations in the fashion retail industry as including family, colour, fashion, segments, store types, price, expectation levels, and size. These variables inform the decision making process of the customer, therefore holding a lot of value for companies in order to predict sales for future products. The findings reveal that data analytics and business intelligence play a vital role in ensuring that organizations in the fashion retail sector gain competitive advantage. The study concludes that business intelligence models are valid tools that can be applied in predicting sales and further help managerial decisions in creating market strategies and ordering within the fashion sector. The study proves that sales forecasting is an issue in the fashion retail industry that can be solved using innovative and effective means such as use of business intelligence to tailor make solutions for specific organizations.

Stefanovic, N. (2015). Collaborative predictive business intelligence model for spare parts

inventory replenishment. Computer Science and Information Systems, 12(3), 911-930.

The article begins by acknowledging the complexities of the current business environment. There is a great challenge to supply chains in every industry when making different decisions relating to supply and demand. Optimal inventory decision making is critical for the success of supply chains and their management (Stefanovic, 2015). Despite the knowledge on the need for accurate and timely decision making in the supply chain, the article notes that businesses and supply chains are still relying on the use of traditional inventory management techniques. These techniques are inadequate when it comes to forecasting sales and predicting market behaviour in order to inform the decision making process. The paper, therefore, aims to show how data mining techniques can improve the supply chain management. By describing the business intelligence model, the article reveals that up-to-date and accurate information is availed for businesses, thus, aiding with better inventory management policies and decisions. The variables identified in the research include costs, prices, sales amounts, inventory level, market changes, and promotional activities. The study finds that supply chains generate a lot of data that may be challenging to integrate, analyse, and process. However, the complexity of using these big data can be made simpler by using business intelligence models to narrow down usage as per the business objectives. Business intelligence models positively influence the decision making process by converting the data into opportunities to create efficiencies in the supply chain and inventory management processes.

Fan, S., Lau, R. Y., & Zhao, J. L. (2015). Demystifying big data analytics for business

intelligence through the lens of marketing mix. Big Data Research, 2(1), 28-32.

One of the greatest disruptive technologies that has reshaped how businesses use data and business intelligence is big data analytics. Business intelligence, specifically, relies on business data analytics to acquire insights that inform the decision making process. Recent revolutions in the technology world, such as social media and e-commerce, enable consumers to generate a lot of data and a t a faster rate than before. As a disruptive technology, big data and its use have given some organizations a competitive advantage, in their marketing decisions as well as in the prediction of sales. It helps to accurately forecast consumer behaviour based on data generated and gathered on their culture, patterns, and tastes and preferences. The current study uses recent literature to investigate the big data analytics landscape through a marketing mix lens. Data sources are then identified, methods and applications analysed, and different marketing perspectives explored. The main variables discussed include people, promotion, place, price, and product, which make the basis for marketing intelligence. The article also proceeds to discuss a number of challenges that big data analytics places on marketing strategies and how business intelligence may solve the problems in the future. The article finds that it is important for businesses to select data sources that are suitable and appropriate to their particular goals because the data available for an organization continues to increase. Marketing intelligence must be informed by business intelligence conclusions that are attained through the matching of critical business processes to the goals and objectives of a business.

Vukšić, V. B., Bach, M. P., & Popovič, A. (2013). Supporting performance management

with business process management and business intelligence: A case analysis of integration and orchestration. International journal of information management, 33(4), 613-619.

One of the most effective ways to conduct meaningful research is to review cases in order to see the application of theory and empirical elements. In this article, the authors found that to stay competitive, organizations are using different methodologies that enable the measuring, monitoring, and analysis of their performance against other organizations. Performance management structures are implemented as dynamic and balanced solutions to provide support to the processes of making decisions, through gathering, elaboration, and analysis of information relevant to an organization. The integration of business intelligence and business process management to performance management is expected to spearhead the next generation of top performing organizations. The aim of the current research is to understand the extent of the service industry’s use of business intelligence and business process management to predict performance and to manage progress. The research study also aims to identify the differences between implementation of business intelligence in performance management within different industries, telecommunication and banking, against late and early adopters of the said technological advancements. The article found that the service industry in Croatia rarely utilizes the potential of technological advancements such as business intelligence and business processes management in their performance management. Instead, the study found that the service industry still relies heavily on traditional forms of performance management. The use of performance-related data is still seen as a waste of resources to firms that have performed very well in the past. However, the authors warn of the disruptive nature of business intelligence and the need for every firm in the service industry to use it as a way of gaining competitive advantage. The study only investigated multinational firms, yet, literature shows that even smaller organizations have not fully learnt to integrate data analytics and other forms of business intelligence into their performance management, to predict patterns and create policies that would favour the organization in the long run.

Jin, D. H., & Kim, H. J. (2018). Integrated understanding of big data, big data analysis,

and business intelligence: a case study of logistics. Sustainability, 10(10), 3778.

The authors begin by noting that efficient decision making is one of the best ways for organizations to create an advantage in their industries. To attain this, using business intelligence becomes a vital tool to drive organizations towards superior market positions and to have sustainable growth. The information and technology sector makes rapid changes and developments, organizations have now found ways to collect and analyze big data to improve their bottom lines. A problem occurs, however, in the definition of business intelligence and how organizations can effectively se them to improve their position, whether in sales or marketing. That said, the authors found that the main problem is how small companies can use big data and business intelligence to acquire the same levels of business advantage compared to bigger firms and corporations. The purpose of the study was to review literature on business intelligence and big data and its analysis to sow their convergence towards being an integrated decision making system. The article also explores how enterprises use business intelligence and big data in a case study of the logistics and sorting processes to create an advantage. The study finds that the value of business intelligence depends on the type of data collected and how it is used. Despite the data used for the study being derived only for a small information range, it is enough to make a conclusion regarding the importance of data analytics, big data, and business intelligence to organizations. These elements have made it easier for businesses to make decisions regarding their sales, marketing, inventory management, supply chain management, promotion strategies, and logistics.

Discussion

The articles reveal the importance of business intelligence to the modern business setup. According to Loureiro, Miguéis, & da Silva (2018), data analytics and business intelligence play a vital role in ensuring that organizations gain competitive advantage. Collectively, articles by Vukšić, Bach, & Popovič (2013) and Stefanovic (2015) find that business intelligence models are valid tools that can be applied in predicting sales and further help managerial decisions in creating market strategies and other firm processes within a given sector. The articles concur in defining big data and its use as a disruptive technology that provides organizations with competitive advantage, in their marketing decisions as well as in the prediction of sales. Similarly, Fan et al. (2015) interpret business intelligence to be an opportunity to transform data into business opportunities because it allows the creation of business strategies based on the data available and the processes available. With these affirmations identified, it is important that every organization work towards creating an environment that uses business intelligence to gain an edge over their competition through understanding their customers better.

Recommendation

It is recommended that ABC Retail Company uses big data and business intelligence approaches to improve its sales prediction, accuracy of information, and timeliness of information intended towards making future decisions. At present, the organization only uses a fraction of the business intelligence model and would improve its position by integrating business intelligence to its sales prediction and improving strategies that would affect its market.

Reference List

Fan, S., Lau, R. Y., & Zhao, J. L. (2015). Demystifying big data analytics for business

intelligence through the lens of marketing mix. Big Data Research, 2(1), 28-32.

Jin, D. H., & Kim, H. J. (2018). Integrated understanding of big data, big data analysis, and

business intelligence: a case study of logistics. Sustainability, 10(10), 3778.

Kiani Mavi, R., & Standing, C. (2018). Cause and effect analysis of business intelligence (BI)

benefits with fuzzy DEMATEL. Knowledge Management Research & Practice, 16(2), 245-257.

Loureiro, A. L., Miguéis, V. L., & da Silva, L. F. (2018). Exploring the use of deep neural

networks for sales forecasting in fashion retail. Decision Support Systems, 114, 81-93.

Stefanovic, N. (2015). Collaborative predictive business intelligence model for spare parts

inventory replenishment. Computer Science and Information Systems, 12(3), 911-930.

Vukšić, V. B., Bach, M. P., & Popovič, A. (2013). Supporting performance management with

business process management and business intelligence: A case analysis of integration and orchestration. International journal of information management, 33(4), 613-619.