Impacts Of Big Data On Business Intelligence ✓ Solved

Impacts of Big Data on Business Intelligence This week's article

Discuss the company, its approach to big data analytics with business intelligence, what they are doing right, what they are doing wrong, and how they can improve to be more successful in the implementation and maintenance of big data analytics with business intelligence. Your paper should include an introduction, a body with fully developed content, and a conclusion. Support your answers with the readings from the course and at least two scholarly journal articles to support your positions, claims, and observations, in addition to your textbook.

Paper For Above Instructions

Introduction

In an era defined by the exponential increase of data, businesses are leveraging Big Data analytics to enhance their operational efficiency, improve decision-making, and ultimately gain a competitive edge. One notable example within the Fortune 1000 is Target Corporation, which has ingeniously integrated Big Data analytics into its business intelligence frameworks. This paper examines Target’s strategic usage of Big Data, evaluates what they are doing effectively, identifies areas needing improvement, and proposes recommendations for enhancing their implementation and maintenance of data analytics with business intelligence.

Target Corporation and Big Data Analytics

Target, one of the largest retail chains in the United States, operates with a robust Big Data analytics framework. The company uses data mining techniques to derive consumer insights, manage inventory, and optimize its supply chain operations. According to a study by Brynjolfsson and McAfee (2014), Target utilizes predictive analytics to anticipate customer behaviors and personalize shopping experiences. By analyzing large volumes of transactional data, web analytics, social media interactions, and loyalty card information, Target can offer tailored promotions and improve customer engagement.

For instance, in 2012, Target made headlines when it was able to predict a customer's pregnancy based on their shopping patterns, even before the customer herself was aware of it (Duhigg, 2012). The company analyzed buying behaviors, such as the purchase of unscented lotion and vitamin supplements, enabling them to send relevant coupons to soon-to-be mothers. This practice not only enhanced customer loyalty but also increased sales substantially.

What Target is Doing Right

Target’s success largely stems from its proactive approach to data integration and analytics. By adopting advanced analytical tools and hiring data scientists, the company ensures it can manage and utilize data effectively. Moreover, Target has invested significantly in its IT infrastructure, enabling it to handle large datasets efficiently (McKinsey, 2016). This commitment to technology and talent acquisition has allowed Target to create data-driven strategies that enhance decision-making at all levels of the organization.

Another facet of Target's effective approach is its focus on customer-centric strategies. The corporation leverages data analytics to enhance customer experiences, leading to improved satisfaction and retention rates. Research shows that personalized shopping experiences can lead to higher revenue (Lemon & Verhoef, 2016). Target's ability to analyze customer feedback and adapt its offerings accordingly shows a strong understanding of the market needs.

What Target is Doing Wrong

Despite its successes, Target faces challenges in its data analytics journey. One of the primary issues arises from data privacy concerns. The incident resulting from Target’s data breach in 2013 raised significant alarms regarding consumer trust. Hackers accessed the personal information of millions of customers, leading to a public relations crisis and financial losses estimated at $162 million (Riley et al., 2014). Target’s failure to protect consumer data not only harmed its reputation but also highlighted the gaps in its cybersecurity measures.

Moreover, while Target's attempt to predict consumer behavior is innovative, it also relies heavily on algorithms that may inadvertently reinforce biases. For instance, predictive analytics algorithms can create stereotypes based on past consumer behaviors and lead to homogenized marketing strategies that may alienate certain customer segments (O'Neil, 2016).

Recommendations for Improvement

To successfully advance its Big Data analytics and business intelligence integration, Target should focus on several areas for improvement. First, enhancing cybersecurity measures is crucial. Investing in advanced encryption technologies and conducting regular security audits can help protect consumer data. Furthermore, establishing transparent communication channels with customers about data usage and privacy can rebuild trust and mitigate potential backlash.

Second, Target should prioritize ethical data use. By assessing the algorithms used in predictive analytics for biases, Target can ensure its marketing strategies are inclusive and appealing to a broader audience. Incorporating a diverse team of data scientists can facilitate diverse perspectives, minimizing the risk of biased outcomes (Barocas & Selbst, 2016).

Additionally, fostering a culture of continuous improvement and learning within the data analytics teams can ignite innovation. Target could implement regular training sessions on emerging analytics technologies and methodologies, keeping their data capabilities robust and adaptable. Collaborating with academic institutions on research projects can also contribute to novel insights and methodologies.

Conclusion

Big Data analytics plays a crucial role in enhancing business intelligence and driving success in today’s competitive market landscape. Target Corporation's effective integration of analytics with its business strategies serves as a notable example within the Fortune 1000. Nonetheless, addressing privacy concerns and algorithmic biases is essential for the company's continued growth. By implementing improved cybersecurity measures, focusing on ethical data practices, and fostering a culture of innovation, Target can enhance its Big Data analytics implementation and further strengthen its competitive advantage.

References

  • Barocas, S., & Selbst, A. D. (2016). Big Data's Disparate Impact. California Law Review, 104(3), 671-732.
  • Brynjolfsson, E., & McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W. W. Norton & Company.
  • Duhigg, C. (2012). How Companies Learn Your Secrets. The New York Times Magazine.
  • Lemon, K. N., & Verhoef, P. C. (2016). Understanding Customer Experience Throughout the Customer Journey. Journal of Marketing, 80(6), 69-96.
  • McKinsey & Company. (2016). The Analytics Advantage: How Companies are Winning with Data.
  • O'Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown Publishing Group.
  • Riley, M., & et al. (2014). Target’s Data Breach: What Went Wrong and How to Fix It. Bloomberg Businessweek.

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