Questions and Answers
What is the primary purpose of an iterative analytics process in customer relationship management?
Which types of data sources are essential for improving the accuracy of analytic models?
In which situation is advanced analytics particularly crucial?
What describes the role of algorithms in cognitive computing systems?
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What role do predictive models play in decision-making processes?
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Which concept is essential in pattern identification within machine learning?
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What is the essence of supervised learning in machine learning systems?
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Which of the following best describes automated trading in investment banks?
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Which is a key feature of machine learning within cognitive computing?
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How do machine learning algorithms aid in automated trading?
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What distinguishes automated decision-making in analytics from human decision-making?
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What aspect of predictive modeling is highlighted in the context of cognitive computing?
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Which is true about the diversity of data sources in cognitive systems?
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What is a key advantage of using advanced analytics for decision-making in high-risk situations?
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What type of decision-making do cognitive computing systems facilitate through their analytics?
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In what area could machine learning be particularly applied within trading systems?
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What advantage does machine learning provide in analyzing social media data?
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How do automated trading algorithms potentially create problems in financial markets?
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What is one of the key benefits of using SaaS offerings that implement machine learning?
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What can be a consequence of automated decision-making without human oversight?
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What role do media buyers play in using social media analytics?
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What is indicated by machine learning providing 72 hours of advanced warning?
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What is the purpose of using analytics in improving service level agreements (SLA)?
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What is a significant risk of relying solely on automated trading algorithms?
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Study Notes
Customer Relationship Management and Analytics
- Iterative analytics processes enhance customer satisfaction by integrating current data from customer interactions for quick decision-making.
- Diverse data sources, including structured, unstructured, and streaming data, are essential for improving predictive analytics.
- Key data sources include operational databases, social media, web logs, sensors, and videos, helping to refine analytic models.
Advanced Analytics Applications
- Advanced analytics is increasingly used in high-stakes areas such as patient health management and machine performance.
- Accurate predictions can save lives and prevent crises, showcasing the importance of advanced analytics.
- Machine learning automates processes to manage large and fast data volumes, providing a competitive edge.
Automated Decision-Making
- In environments like investment banking, electronic platforms enable algorithmic trading without human intervention.
- Trading algorithms execute buy/sell orders based on predefined rules, utilizing both historical and real-time data inputs.
- Quick adjustments based on social media trends can enhance trading performance but may also lead to erroneous trades if inaccurate information is processed.
Proactive Customer Issue Management
- Companies leverage SaaS platforms employing machine learning to monitor social media for emerging customer issues.
- Such platforms analyze trends against historical data, offering over 72 hours of advance warning on customer concerns.
- Early detection allows marketing and PR teams to respond rapidly, preserving the company's brand integrity.
Performance Analysis and Service Level Agreements (SLA)
- Cognitive computing systems utilize a variety of analytical tools for descriptive, predictive, and prescriptive tasks.
- Standard components embedded in cognitive systems support enhanced performance analysis to meet service level agreements.
Machine Learning Fundamentals
- Continuous learning without reprogramming is vital to cognitive computing solutions, driven by machine learning algorithms.
- Machine learning integrates concepts from computer science, statistics, and psychology for effective data analysis.
- Algorithms search for patterns in data, prompting actions or recommendations based on identified similarities.
Learning Approaches in Machine Learning
- Supervised learning teaches systems to recognize data patterns based on training examples, honing their problem-solving capabilities.
- The training dataset includes various examples that illuminate potential patterns the system may encounter in real-world applications.
- Learning by example enhances system proficiency in tackling complex challenges through effective modeling techniques.
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Description
Explore the essential concepts of customer relationship management and the pivotal role of analytics in enhancing customer satisfaction. Understand how diverse data sources contribute to predictive analytics and decision-making processes. This quiz delves into advanced applications and automated decision-making in various high-stakes environments.
