Data Analytics: Essential for Business Intelligence & Forecasting - Essay Sample

Introduction

Data analytics is an integral part that acts as an essential avenue towards achieving business intelligence. In this case, data analytics refers to the process through which organizations utilize the past and present information to forecast what may happen in the future (Duan & Xiong, 2015). Through data analytics, organizations can break down complex information about the markets and provide clear ways that help to understand the common trends that occur over time. Business intelligence, on the other hand, is the use of this data to make effective business decisions (Liang & Liu, 2018). After collecting data and identifying trends using data analytics, the business, therefore, develops a business intelligence initiative that helps to capture an in-depth understanding of this data. Consequently, it helps in making informed decisions that are driven by descriptive data as well as well proven trends in the market. These two aspects work hand in hand to enhance organizational competitiveness in the market (Selene Xia & Gong, 2014). In the current turbulent and highly competitive business environment, organizations are utilizing these two aspects to create a competitive edge. The paper, therefore, focuses on evaluating companies that utilize these statistical concepts (data analytics and business intelligence) to solve complex business problems as well as proposing how the companies can enhancing their competitive advantage using these concepts.

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Major Organizations that Utilize Data Analytics and Business Intelligence

With the current technological development and globalization, most organizations are seeking the help of statistical techniques such as big data analytics and business intelligence to make agile decisions that help these particular companies to remain competitive in the market (Sun, Sun & Strang, 2016). For instance in life sciences, clinical research utilizes big data analytics to conduct the study (Duan & Xiong, 2015). Through the help of artificial intelligence and advanced data analytics, specialists can enhance the speed and efficiency of the research process.

Consequently, the banking industry also utilizes the statistical aspects in enhancing their efficiency and making decisions. These financial institutions gather relevant historical data, analyses the data, and further uses the findings to develop sound business decisions. This process allow these banks to understand customer trends, investor decisions, as well as helps in eliminating any redundant data within the operating system. Manufacturing companies also rely heavily on data analytics to solve complex problems (Selene Xia & Gong, 2014). A major issue is making decisions on whether to produce or purchase a particular product (make or buy decisions). This is done by evaluating the cost and benefit associated with purchasing or producing the product by assessing the available data. In most cases, manufacturing companies wrestle with complex daily problems in regards to production, equipment breakdown, and the efficiency of the supply chain. Most of these problems are linked to financial, time, and labor constraints. Through the use of data analytics and business intelligence, the companies can, therefore, make decisions strategies that help allocating resources to enhance optimal production while minimizing cost. Additionally, it can help to identify any gaps that may exist which may in turn help in saving costs as well as identifying new revenue opportunities.

The healthcare facilities also utilize data analytics to enhance the efficiency of the operational activities as well as making complex decisions. Most of the health care organizations serve a wide range of customers who have different problems. Maintaining patient recording while linking them to healthcare plans, insurance coverage, and other relevant information can be a challenging task. Therefore, data analytics can help to analyze the large complex patient data effectively to ensure that the patients receive the highest quality of healthcare service (Sun, Sun & Strang, 2016). Additionally, utilizing the simplified data can help the healthcare providers to understand the patient history quickly creating a chance to provide a lifesaving diagnosis and treatment options which helps to avoid mistakes and delay in the treatment process.

Retail companies also utilize big data analytics and business intelligence to develop strategic decisions that help to remain competitive in the market. It is achieved by collecting large and complex customer data to study and understand customer trends (Sun, Sun & Strang, 2016). In most retail organizations, they value customer satisfaction as a critical element that helps to remain competitive in the market. Therefore, data analytics helps to understand the evolving consumer needs and preferences as well as using this data to modify the products and service so that they can match the different needs and prefers of the consumers. In the current market, these organizations need to understand that customer service is evolving and the consumer value is also changing. Thus using the previous data to understand customer trends is beneficial as it helps to predict the future demand and in turn, makes decision strategies to meet the demand. According to Sprongl, (2013), organizations that have an in-depth understanding about their customers study their habits, loyalty trends, and their attitudes which helps to meet the demand which enhances their profitability and competitiveness in the market.

Utilizing Data Analytics and Business Intelligence for Competitive Advantage

Looking at these organizations, it is evident that data analytics and business intelligence is highly valuable in making strategic decisions as well as solving complex problems. It is evident that most of the companies can utilize the data to provide a high quality of service to their customers to enhance the competitive advantage (Guarda, Santos, Pinto, Augusto & Silva, 2013). In this case, these companies need to invest highly in statistical tools to ensure that they acquire relevantly and up to date information to ensure that they match the trends within the market. More importantly, it is essential to understand that while they strive to provide customer value, they should also link the processes to the key goals and objectives of the company. This means that data analytics should help to provide high quality products and services while still integrating a production process that minimizes cost without compromising the quality (Sprongl, 2013).

Conclusion

From the above analysis, it is evident that a wide range of organizations ranging from life science, healthcare, banking, and retail industries utilize big data analytics and business intelligence to solve a complex business process. The key benefit is the utilization of historical data to understand customer trends and further aids in enhancing the efficiency of the operation process. While this business strives to streamline the operational activities, it is crucial to realize that these statistical concepts can also help to create a competitive advantage in the market. This can be done by understanding the evolving customer needs and services and in turn, develop products and services that meet the demand. This is because providing excellent customer service is fundamental in capturing a competitive advantage in the market.

References

Duan, L., & Xiong, Y. (2015). Big data analytics and business analytics. Journal of Management Analytics, 2(1), 1-21. doi: 10.1080/23270012.2015.1020891

Guarda, T., Santos, M., Pinto, F., Augusto, M., & Silva, C. (2013). Business Intelligence as a Competitive Advantage for SMEs. International Journal of Trade, Economics And Finance, 187-190. doi: 10.7763/ijtef.2013.v4.283

Liang, T., & Liu, Y. (2018). Research Landscape of Business Intelligence and Big Data analytics: A bibliometrics study. Expert Systems with Applications, 111, 2-10. doi: 10.1016/j.eswa.2018.05.018

Selene Xia, B., & Gong, P. (2014). Review of business intelligence through data analysis. Benchmarking: An International Journal, 21(2), 300-311. doi: 10.1108/bij-08-2012-0050

Sprongl, P. (2013). Gaining competitive advantage through business analytics. Acta Universitatis Agriculturae Et Silviculturae Mendelianae Brunensis, 61(7), 2779-2785. doi: 10.11118/actaun201361072779

Sun, Z., Sun, L., & Strang, K. (2016). Big Data Analytics Services for Enhancing Business Intelligence. Journal of Computer Information Systems, 58(2), 162-169. doi: 10.1080/08874417.2016.1220239