PowerPoint Presentation On Best Practices

Powerpoint Presentation Which Will Be A Best Practices Presentation

PowerPoint presentation which will be a BEST PRACTICES PRESENTATION. - TOPIC: ' Business intelligence and big data '( Research best company practices for this topic ) - Include a cover slide, introduction slide at the beginning of the presentation, a conclusion slide at the end of the presentation, and a reference slide using APA format at the end of the presentation. - 10 references from scholarly sources…do NOT use Wikipedia or Patents - minimum of 16 slides which will include the cover and reference slides - at least one figure or one table in the presentation and format in APA style

Paper For Above instruction

Powerpoint Presentation Which Will Be A Best Practices Presentation

This presentation aims to explore the best practices in leveraging business intelligence (BI) and big data within organizations. It highlights how leading companies utilize these technologies to gain competitive advantage, optimize operations, and foster innovation. The presentation covers industry standards, successful strategies, technological frameworks, and practical recommendations, supported by scholarly sources, to guide organizations in adopting effective BI and big data practices.

Introduction

In an increasingly data-driven world, organizations are turning to business intelligence (BI) and big data analytics to inform decision-making, improve operational efficiency, and uncover new growth opportunities. Business intelligence involves the collection, integration, analysis, and presentation of business information, enabling informed strategic and tactical decisions. Big data complements BI by handling vast, complex, and rapidly growing data sets from diverse sources, such as social media, transaction records, sensors, and more. Leading companies such as Amazon, Google, and Microsoft exemplify best practices in deploying BI and big data, harnessing these tools to innovate and remain competitive.

Understanding Business Intelligence and Big Data

Business intelligence encompasses technologies and strategies used to analyze business data to support better decision-making. It involves tools like dashboards, data mining, reporting, and visualization. Big data refers to datasets that are too large or complex for traditional data-processing software, characterized by the three Vs: volume, velocity, and variety (Laney, 2001). Successfully integrating BI and big data requires a strategic approach that aligns with organizational goals, ensuring data quality, security, and privacy (Manyika et al., 2011).

Best Practices in Business Intelligence

1. Strategic Alignment and Leadership

Effective BI initiatives start with aligning BI strategies with organizational objectives. Leadership commitment ensures proper resource allocation, stakeholder engagement, and a clear vision (Negash, 2004). Top management involvement fosters a data-driven culture and facilitates change management processes.

2. Data Quality and Governance

Maintaining high data quality and establishing robust data governance frameworks are crucial. Best practices include data cleansing, validation, and establishing policies for data privacy and security, complying with regulations such as GDPR (Riggins & Wamba, 2015).

3. User-Centric Design and Accessibility

Designing intuitive dashboards and reports that cater to user needs enhances adoption. Ensuring data accessibility across departments promotes self-service analytics, empowering users to derive insights independently (Sharma & Deogun, 2007).

4. Technological Infrastructure

Investing in scalable and flexible BI platforms, including cloud-based solutions, enables organizations to handle growing data volumes efficiently. The integration of data warehouses, data lakes, and real-time processing tools reflects best practices in infrastructure design (Chen et al., 2012).

Best Practices in Big Data Applications

1. Advanced Analytics and Machine Learning

Leading organizations utilize advanced analytics, including machine learning and AI, for predictive modeling and insight generation. This fosters proactive decision-making and automation of routine data analysis tasks (Manyika et al., 2011).

2. Data Integration and Real-time Processing

Effective big data strategies emphasize seamless data integration from multiple sources and real-time data processing capabilities. Tools such as Apache Kafka and Spark facilitate real-time analytics, improving responsiveness (Zikopoulos et al., 2012).

3. Ethical Data Use and Privacy

Adhering to ethical standards and privacy regulations, such as GDPR, is essential. Leading firms establish policies for anonymization, secure storage, and responsible data sharing to maintain stakeholder trust (Culnan & Bair, 2003).

4. Continuous Learning and Skill Development

Organizations investing in employee training and professional development in areas like data science, cloud computing, and analytics tools stay ahead in the evolving big data landscape (Kogan et al., 2017).

Case Studies of Industry Leaders

Amazon exemplifies best practices by employing a robust big data infrastructure to optimize supply chain, personalize recommendations, and improve customer experience. Similarly, Google leverages big data for search algorithms, advertising strategies, and AI innovations. Microsoft invests heavily in cloud-based BI solutions, facilitating scalable analytics across enterprises.

Technological Frameworks and Tools

Popular tools like Tableau, Power BI, and QlikView exemplify best practices in visualization and user engagement. Open-source platforms like Apache Hadoop and Spark are integral to big data processing, supporting scalable, distributed analytics (Dean & Ghemawat, 2008).

Challenges and Solutions

Challenges include data privacy concerns, managing data quality, and integrating diverse data sources. Solutions involve adopting comprehensive data governance, employing data cataloging tools, and implementing secure data architectures. Organizational change management is also vital to foster a data-centric culture (Riggins & Wamba, 2015).

Conclusion

Implementing best practices in business intelligence and big data requires strategic alignment, technological investment, ethical standards, and ongoing skill development. Leading companies demonstrate that a comprehensive, well-governed approach can deliver significant competitive advantages. Embracing emerging technologies and maintaining a focus on data quality and privacy will ensure organizations stay agile and innovative in the data-driven era.

References

• Chen, H., Chiang, R., & Storey, V. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly, 36(4), 1165-1188.
• Culnan, M. J., & Bair, J. (2003). Corporate Ethical Codes: Barriers and Facilitators. Journal of Business Ethics, 46(2), 5-20.
• Dean, J., & Ghemawat, S. (2008). MapReduce: Simplified Data Processing on Large Clusters. Communications of the ACM, 51(1), 107-113.
• Kogan, A., Bleier, A., & Nasrabadi, A. (2017). The evolution of data literacy–a comprehensive review. Journal of Business Research, 80, 376-385.
• Laney, D. (2001). 3D Data Management: Controlling Data Volume, Velocity, and Variety. META Group Report.
• Manyika, J., Mahajan, D., Van Ballegooijen, A., & Dhawan, S. (2011). Big Data: The Next Frontier for Innovation, Competition, and Productivity. McKinsey Global Institute.
• Negash, S. (2004). Business Intelligence. Communications of the Association for Information Systems, 13, 177-195.
• Riggins, F. J., & Wamba, S. (2015). Research Directions on the Adoption, Usage, and Impact of Business Analytics in Organizations. International Journal of Information Management, 35(5), 543-556.
• Sharma, R., & Deogun, J. S. (2007). An Empirical Study of User Needs for Business Intelligence Development. Journal of Organizational and End User Computing, 19(2), 1-18.
• Zikopoulos, P., Parasuraman, K., Deutsch, T., & Giles, J. (2012). Harnessing Big Data: Strategies for Seizing Business Opportunity. McGraw-Hill.