Big Data and Analytics

Introduction to Big Data and Analytics

Big Data analytics plays a prominent role in business execution and planning complex data sets generated from digital platforms.(Oyinkan, 2024). The data sets have all the information, such as use, images, and textual content. The approach supports organizations in finding the trends and detecting the patterns within the large datasets. The system allows for making informed decisions and data-driven insights. Businesses have shifted from traditional business intelligence to advanced technologies. The major goal of big data in business practices is to improve the quality of decision-making (Adaga, et al., 2024). AI-powered tools help to analyze the inefficiencies in the operations and manage the production workflow in supply chain management. Moreover, the approach enables the smarter allocation of resources and reduces the cost of operations, which directly enhances productivity.

Benefits of Big Data and Analytics

Smart decision making

The primary advantage of big data is smarter decision-making. The analytical approach is better understood and helps leaders to make effective decisions by analyzing the internal processes. Moreover, allows them to make accurate and informed choices. Also, businesses improve and allocate the resources with quick response to market conditions.

Deeper customer understanding

Knowing what customer wants and their need is necessary. Data helps companies gather valuable insights and feedback to promote products and recommendations (Vesterinen, Mero, & Skippari, 2024). The tools, such as sentiment analysis and behavioral writing, where customers can drive the decisions. In addition, the organizations deliver personalized services through targeted marketing and enhance long-term engagement.

Personalized marketing strategy

Big data promotes personalized marketing strategies to attract the audience. The review of customer behavior and past purchases promotes recommendations (Lutfullayeva, 2023). In this case, the e-commerce platforms make suggestions based on past purchases. So, the personalized experiences engage the customer and increase revenue.

Risk management

In fact, big data helps in risk identification and fraud detection in businesses. The approach promotes and identifies suspicious activities and potential threats. Moreover, the approach analyzes unusual patterns in the businesses. There are banks and financial services that interact with various customers and maintain the data integrity.

Advantages of big data and analytics

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Challenges of Big Data and Analytics in Business Growth

Technical complexity

Sometimes, big data requires advanced technological expertise. Businesses need advanced resources and advanced computing systems to manage and analyze the huge data. Many organizations, such as small and medium enterprises, have complexity in maintaining the large infrastructure. Thus, there should be a specialized workforce readily available to reduce complex task.

Ethical and privacy concerns

There are various ethical considerations in using big data. The customers are now aware of various data; misuse of the data consequently leads to disturbance in privacy. So, best practices should be made to match the business with customer expectations.

Data quality issue

One of the major challenges is the data quality issue. The approach depends upon various platforms that need quality in transactional databases. In this case, if the data is outdated and incomplete, it misleads towards disruptions. Additionally, dealing with legacy systems that have poor integration could consequently lead to downsizing the business operations.

Challenges of big data

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Case studies

Health sector: patient care system with data insights

Memorial Sloan catering Cancer center

The system has a data technology with cancer. The system has examined large amounts of data with various medical records. The customized approach works towards better outcomes and helps to reduce medical expenses by avoiding treatment. Moreover, big data has helped in planning the treatments for individuals with personalized care.

Philips healthcare

In this case. Philips has developed predictive analytics that is designed for intensive care units. Philips has continuous support for patients who are suffering from heart issues such as cancer and blood pressure. The approach helps in the early detection of signs and prevents further damage from the predictive capability of the medical lab to improve the outcome of the patient. Additionally, contributed resources for more ICU stays for the patients.

Finance sector: Risk mitigation and customer personalization

Capital One

In the financial sector, Capital One has used big data to elevate customer engagement in the marketing effort. Moreover, the organization has analyzed the history and behavior of customers to deliver high personal product recommendations and engage the marketing content. Subsequently, the strategy has increased the satisfaction for customers and improved the return on investment for the business.

JP Morgan Chase

The JP Morgan Chase has used analytics to ensure financial security for the system. The company especially uses a sophisticated algorithm for detecting unusual behaviors. Moreover, company has maintained a reliable and minimal financial loss to enhance customer trust.

Retail sector optimizing customer experience and operations

Amazon

For example, Amazon has become a great application for big data to enhance customer experience. The organization has tracked past purchases and behavior to enhance product suggestions and promote offers (Ochuba, Amoo, Okafor, Akinrinola, & Usman, 2024). The strategy consequently led to an increase in sales and greater satisfaction for the customers and promoted long-term loyalty to the brand.

Walmart

For instance, Walmart has used a the approach in supply chain management. The company has made a significant change in sale trends according to the weather changes and seasonal changes. Walmart has promoted predictive management and towards the management of stock levels. Moreover, the company made sure that there were no product shortages and enhanced the efficient distribution across various stores.

Conclusion

Big data and analytics have become an important aspect of competitive advantage and business growth. Enhancing the collection of data provides an approach towards business enhancement. In conclusion, various industries adopted the approach to enhance their businesses. Various industries, such as finance, healthcare, and retail, have adopted the system to improve performance and increase customer satisfaction. The overall applications of big data are a major transformation in the operations of business. Since there are issues related to integration, privacy, and compliance. So, the issues are solved with effective data-driven decision-making, which enhances the business. To conclude, as a continuous involvement of technology, big data increases the success of digital management.

References

Adaga, E. M., Okorie, G. N., Egieya, Z. E., Ikwue, U., Oriekhoe, O. I., DaraOjimba, D. O., & Udeh, C. A. (2024). THE ROLE OF BIG DATA IN BUSINESS STRATEGY: A CRITICAL REVIEW. Computer Science & IT Research Journal, 04(03), 327-350.

Lutfullayeva, S. (2023). The Role of Big Data Analytics in Business Strategy and Market Insights. International Bulletin of Young Scientis, 01(01), 1-7.

Ochuba, N. A., Amoo, O. O., Okafor, E. S., Akinrinola, O., & Usman, F. O. (2024). STRATEGIES FOR LEVERAGING BIG DATA AND ANALYTICS FOR BUSINESS DEVELOPMENT: A COMPREHENSIVE. Computer Science & IT Research Journal, 05(03), 562-575.

Oyinkan, O. (2024, Feb 29). Leveraging Big Data for Strategic Business Decisions: A Business Analyst’s Guide. Retrieved from linkedin: https://www.linkedin.com/pulse/leveraging-big-data-strategic-business-decisions-oyemade-csm–wj8qe

Vesterinen, M., Mero, J., & Skippari, M. (2024). Big data analytics capability, marketing agility, and firm performance: a conceptual framework. Journal of Marketing Theory and Practice, 33(02), 310-330.

Keywords

Big Data, Business Intelligence, Customer insights, Personalized marketing, Data analytics

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