Full Project Paper Compile all 5 chapters into a single cohesive final project plan. The final paper must include a cover page, executive summary, table of contents (list of tables, list of images, if

Project Research

Phase 2 – Research and Recommendation

Project Research

Introduction

Technology and business development are two intertwined concepts in today's world. Technology has become an essential part of the business world and gets used in every aspect of modern business operations. Business development involves leveraging technology to help businesses grow, expand, and increase their efficiency and profits. Technology has enabled businesses to improve their operations, increase their reach and customer base, and develop innovative products and services. It has also opened up vast opportunities for businesses to explore new markets and increase their profits. Using technology in the industry has allowed firms to reduce costs and save time. Businesses can automate certain processes, which can help them improve their efficiency and productivity. In this paper, the advantages and recommendations associated with the integration of technology in business will get evaluated.

Decision Criteria

Business intelligence and analytics are becoming increasingly crucial for business development. As organizations continue to generate more significant amounts of data, they must utilize it to make informed decisions and gain a competitive advantage (Larson and Chang, 2016). Several criteria were used to identify business intelligence and analytics as vital for business development.

The first criterion is the ability to make more informed decisions. Business intelligence and analytics can provide organizations with insights into their customers, markets, and operations to help them make better decisions. By leveraging data from multiple sources, organizations can identify trends and uncover insights that would otherwise be difficult to detect. It can result in improved decision-making and a greater understanding of the business’s performance.

The second criterion is the ability to remain competitive in the market. By leveraging data, organizations can understand how their competitors are performing and ensure they are staying caught up. It can help organizations stay ahead of the competition and maintain their position in the market.

The third criterion is the ability to stay ahead of the curve. Business intelligence and analytics can help organizations anticipate changes in the marketplace before they occur. By leveraging data and insights, organizations can stay one step ahead of their competition and better prepare for the future.

The fourth criterion is the ability to improve operational efficiency. Business intelligence and analytics can identify opportunities for improvement and help streamline operations. It can improve efficiency and cost savings and increase organizational performance. The fifth criterion is the ability to identify new opportunities. Business intelligence and analytics can help organizations identify new markets, customer segments, and products that may benefit the organization. These insights can be used to develop new strategies and capitalize on new opportunities. It is important to note that business intelligence and analytics are not a one-size-fits-all solution. Organizations must be willing to invest in the right resources and technologies to leverage data and insights effectively. It includes having the right personnel, tools, and processes to leverage data and analytics effectively. Organizations must recognize the importance of creating a culture open to data-driven decision-making and being willing to take risks and innovate.

Further Analysis

David Larson and Victor Chang's review of agile, business intelligence, analytics, and data science provides valuable insights into the interconnectedness of these four disciplines. Their findings suggest that organizations should strive to create an integrated environment where agile, business intelligence, analytics, and data science can work together to provide the most value. I agree with the authors that organizations should invest in developing analytics capabilities and using cloud computing and other technological tools to facilitate collaboration and data-driven decision-making (Muller et al., 2018; Larson and Chang, 2016). I recommend that organizations prioritize data-driven decisions, use data governance tools to ensure integrity, and promote data literacy across the organization.

Organizations should also consider the importance of data security when developing their data-driven strategies. Data security is essential for organizations to protect their data and ensure that only authorized personnel can access it. Organizations should invest in analytics and data science to gain insights into customer needs, trends, and behaviors. By leveraging the power of analytics and data science, organizations can better understand their customer segments and tailor their offerings accordingly.

Moreover, organizations should also strive to foster a culture of continuous improvement and innovation. Organizations can quickly adapt their strategies to meet ever-changing customer needs by engaging in regular feedback sessions and agile development methods. Organizations should invest in developing business intelligence tools to identify opportunities and create actionable metrics.

Recommendation Comparison

The recommendations of Rachinger et al. (2018) are insightful and well-rounded. The authors have effectively highlighted the effects of digitalization on business model innovation, which is an increasingly relevant topic in the modern business world. Digitalization has enabled companies to create and deliver new services and business models, often combined with traditional products or services. It has allowed companies to compete in new markets and increase their customer base while reducing costs and increasing operational efficiency.

I agree with the authors' recommendations that digitalization has a significant and increasing influence on business model innovation. The authors have also identified how digitalization changes how firms interact with customers and manage their internal processes. Customers can now benefit from a more personalized experience, while firms can become more efficient and cost-effective (Larson and Chang, 2016). Companies should actively monitor and adjust their business models to keep up with the changing digital landscape.

Companies should leverage digital technologies such as artificial intelligence and machine learning to gain a competitive edge. Companies should also focus on developing their digital capabilities, such as data analytics and automation, to better understand their customers and optimize their operations (Grant et al., 2017). Finally, companies should be open to experimenting with new business models and strategies to stay ahead of the competition and keep up with the ever-evolving digital environment.

Challenges

One of the biggest challenges of Business Intelligence and Analytics is managing the data. Businesses must ensure that their data is accurate, up-to-date, and organized. Companies have to ensure that the data is properly stored and secure. This process can be time-consuming and costly, as businesses must ensure that the data collected is relevant and reliable.

Another challenge is data governance. Data governance involves setting policies and procedures that define how the data gets used, how it is shared and accessed, and how it is maintained. Data governance ensures that the data collected is aligned with the business's objectives and strategies. Organizations must ensure that their data governance program is well-defined and regularly updated.

Business Intelligence and Analytics require a high level of expertise. Organizations must hire skilled analysts and data scientists to analyze the data and make informed decisions (Larson and Chang, 2016). Businesses must ensure that their employees are appropriately trained in using Business Intelligence and Analytics tools and technologies.

There is challenging to stay up to date with the latest technologies and trends in Business Intelligence and Analytics. As the industry is rapidly changing, organizations must ensure that they get equipped with the latest tools and technologies. They must also invest in ongoing employee training and education to ensure they are using the most effective and efficient methods.

Critique

Some areas could be further explored. For instance, the authors could have extended their research to include a more comprehensive view of the business analytics landscape, including predictive analytics, machine learning, and artificial intelligence. The article could have discussed the potential costs and benefits of implementing business analytics initiatives (Ghobakhloo and Iranmanesh, 2021). The article provides an informative and valuable overview of the challenges and possible solutions associated with leveraging business analytics to create value.

Conclusion

Business intelligence (BI) is a technology-driven process for analyzing data and presenting actionable information to help executives, managers, and other corporate end-users make informed business decisions. BI encompasses a variety of tools, applications, and methodologies that enable organizations to collect data from internal systems and external sources, prepare it for analysis, develop and run queries against the data, and create reports. BI systems can provide historical, current, and predictive views of business operations, often using data gathered in a data warehouse or data mart and occasionally working from operational data. BI can also help companies determine which markets to enter, which products and services to offer, and help identify customer trends and behaviors. As such, BI helps to drive smarter, data-driven business decisions.

References

Ghobakhloo, M., & Iranmanesh, M. (2021). Digital transformation success under Industry 4.0: A strategic guideline for manufacturing SMEs. Journal of Manufacturing Technology Management, 32(8), 1533-1556.

Larson, D., & Chang, V. (2016). A review and future direction of agile, business intelligence, analytics, and data science. International Journal of Information Management, 36(5), 700-710.

Rachinger, M., Rauter, R., Müller, C., Vorraber, W., & Schirgi, E. (2018). Digitalization and its influence on business model innovation. Journal of Manufacturing Technology Management.

Vidgen, R., Shaw, S., & Grant, D. B. (2017). Management challenges in creating value from business analytics. European Journal of Operational Research, 261(2), 626-639.