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Questions and Answers
What distinguishes advanced analytics from traditional analytics?
What distinguishes advanced analytics from traditional analytics?
- Utilization of sophisticated technologies and big data (correct)
- Limited opportunities for organizations
- Focus on human knowledge only
- Longer history in the field
How can big data revolutionize the ways organizations operate?
How can big data revolutionize the ways organizations operate?
- By decreasing managerial decision-making
- By ignoring environmental conditions
- By eliminating the need for human judgment
- By providing new opportunities for decision-making (correct)
Why is it important for organizations to moderate the use of analytics and big data?
Why is it important for organizations to moderate the use of analytics and big data?
- To increase the challenges in data utilization
- To automate decision-making processes completely
- To ignore the potential of big data
- To balance with human knowledge and judgment (correct)
What type of decision-makers are discussed in Chapter 6?
What type of decision-makers are discussed in Chapter 6?
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Which chapter in the reading material covers 'Introduction to Advanced Business Analytics'?
Which chapter in the reading material covers 'Introduction to Advanced Business Analytics'?
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Study Notes
Types of Analytics
- Descriptive analytics: develops insights from data to find out root causes of problems using tools like query drills, agile and spatial visualization
- Predictive analytics: helps managers with predicting future conditions using simulation, optimization, and multicriteria decision modeling
- Prescriptive analytics: develops and assesses the optimized best course of action and solutions to business problems using machine learning, optimization, and multicriteria decision modeling
Forces Behind Analytics
- Analytics powered by humans for humans (traditional analytics)
- Analytics powered by math for processes
- Analytics powered by math for humans
- Analytics powered by math for human interaction and autonomous systems
Big Data and Analytics
- Big data and analytics derive their value from each other
- Without analytics, big data has little or no value
- Analytics without big data is similar to traditional data analysis, but with less value
Evolution of Analytics and Information Systems
- 1950s: Management Information Systems
- 1960s: Data Processing
- 1970s: Personal Computing
- 1980s: Enterprise System & Networking
- 1990s: Customer Focused
- 2000s: Big Data and Analytics
- 2010s: Autonomous Systems
- Generations of analytics: DSS (single decision-maker), EDW (data-focused approach), Real-Time Data Warehousing (operational decisions), and Next Generation (new ways of using data)
Business Analysis vs. Analytics
- Business analysis and analytics have been around for many years
- Advanced analytics powered by sophisticated technologies and big data are products of the 21st century
- They offer significant new opportunities for organizations to assess environmental conditions and support data-driven decision-making
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