D491 Introduction to Analytics
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Free D491 Introduction to Analytics Questions
1. Imagine you are a data steward in a healthcare organization. How would you address a situation where you discover that patient data is being inconsistently recorded across different departments?
- Ignore the inconsistencies as they do not affect overall data quality
- Implement standardized data entry protocols and conduct training sessions for staff
- Focus solely on analyzing the data to identify trends
- Report the issue to upper management without taking further action
Explanation
Inconsistent data recording undermines data accuracy, reliability, and compliance—especially in healthcare, where decisions rely heavily on precise information. As a data steward, the best course of action is to establish standardized data entry protocols and ensure all departments follow uniform procedures. This should be accompanied by comprehensive training for staff to reinforce data governance principles and minimize future discrepancies. By doing so, the organization maintains high-quality data, supports patient safety, and ensures that analytics derived from the data are trustworthy and actionable.
2. Imagine a company is struggling to make data-driven decisions. Which step in the data value chain should they prioritize to improve their analytics capabilities?
- Enhancing data visualization tools
- Improving data gathering techniques
- Focusing on data analysis methods only
- Increasing the number of data managers
Explanation
If a company is struggling to make data-driven decisions, the most crucial step to prioritize is improving data gathering techniques. Reliable and accurate data collection forms the foundation of the entire analytics process. Without high-quality data, even the most advanced analysis or visualization tools will produce misleading results. Effective data gathering ensures that the organization has access to relevant, timely, and complete information, which can then be analyzed to produce actionable insights that support informed decision-making.
3. A retail company wants to improve its inventory management by predicting customer purchasing behavior. Which type of analytics should they utilize, and why?
- Descriptive analytics, because it summarizes past sales data
- Predictive analytics, because it identifies future purchasing trends based on historical data
- Prescriptive analytics, because it recommends specific actions
- Diagnostic analytics, because it analyzes why sales fluctuated
Explanation
The company should use predictive analytics because it leverages historical sales and customer data to forecast future purchasing behavior. Predictive analytics uses statistical models, algorithms, and machine learning to identify trends and patterns that can anticipate customer demand. This enables the company to optimize inventory levels, reduce overstocking or shortages, and align supply with expected demand. While descriptive analytics explains what happened in the past, predictive analytics provides a forward-looking perspective essential for strategic inventory management.
4. What is the goal of Data Architecture?
- To bridge the gap between business strategy and execution
- To help organize a business strategy
- For ease of data management
- To structure and organize data
- To facilitate smooth execution
Explanation
The goal of Data Architecture is to structure and organize data in a way that supports the organization’s overall data strategy and analytics objectives. It defines how data is collected, stored, integrated, and accessed across systems, ensuring that information flows efficiently and securely. A well-designed data architecture provides the foundation for consistency, scalability, and quality in data handling, enabling data analysts and business leaders to derive accurate insights for decision-making.
5. Explain how data gathering contributes to the overall analytics process.
- It allows for the storage of data without any further processing.
- It ensures that data is readily available for analysis, which supports informed decision-making.
- It focuses solely on the collection of data without considering its quality.
- It is the final step in the analytics process before making decisions.
Explanation
Data gathering is a foundational stage in the analytics process, as it provides the raw information needed for subsequent analysis. By systematically collecting relevant, accurate, and comprehensive data from various sources, organizations ensure that they have the necessary inputs to derive meaningful insights. High-quality data gathering enables analysts to identify trends, detect patterns, and make evidence-based decisions. Without reliable data collection, even the most advanced analytical techniques would yield inaccurate or misleading results, undermining the overall effectiveness of the analytics process.
6. If a retail company wants to improve its sales strategy, how might it utilize data mining to achieve this goal?
- By analyzing customer purchase patterns to tailor marketing efforts
- By increasing the amount of data collected without analysis
- By focusing on historical sales data without considering trends
- By limiting data access to only upper management
Explanation
A retail company can leverage data mining to improve its sales strategy by examining customer purchase behaviors and identifying patterns that reveal preferences, buying habits, and seasonal trends. These insights allow the company to develop targeted marketing campaigns, personalize product recommendations, and optimize inventory management. Data mining transforms raw transactional data into actionable intelligence, helping the business make informed decisions that align with customer needs and market dynamics. This strategic use of analytics ultimately enhances sales performance and customer satisfaction.
7. What is data created by a machine without human intervention?
- machine-generated data
- human-generated data
- big data
Explanation
Machine-generated data refers to information produced automatically by devices, sensors, or systems without direct human input. Examples include data from IoT sensors, web server logs, and GPS tracking systems. This type of data is typically high in volume, velocity, and variety, making it essential in modern analytics and machine learning applications.
8. What is the primary function of data warehousing in the context of analytics?
- To create unstructured data for analysis
- To manage structured data for efficient retrieval and analysis
- To eliminate the need for data analysis
- To store data without any retrieval capabilities
Explanation
The primary purpose of a data warehouse is to store large volumes of structured data in a centralized repository that supports efficient retrieval and analysis. Data warehousing integrates data from various sources, ensuring consistency, accuracy, and accessibility for business intelligence and analytics applications. It allows organizations to perform complex queries, trend analyses, and reporting to support informed decision-making. Without a well-organized data warehouse, accessing and analyzing large datasets across departments would be inefficient and error-prone.
9. What is the primary function of a decision support system in analytics?
- To create data from scratch
- To assist organizations in making data-driven decisions
- To gather data from various sources
- To analyze data without human intervention
Explanation
The main function of a Decision Support System (DSS) in analytics is to help organizations make data-driven decisions by integrating data, analytical tools, and models. A DSS provides managers and analysts with the ability to assess complex information, evaluate alternatives, and choose optimal solutions. It combines data analysis with visualization and reporting features to support both structured and unstructured decision-making processes. Rather than replacing human judgment, it enhances it by supplying relevant insights that lead to more accurate and timely business decisions.
10. Your professor is considering purchasing a self-driving car that can figure out the best route and the optimum safe way to drive there without human intervention. What kind of analytics is the car using to do this?
- Prescriptive analytics
- Explanatory analytics
- Descriptive analytics
- Forecast analytics
Explanation
A self-driving car uses prescriptive analytics to determine the best possible route and driving strategy without human input. Prescriptive analytics goes beyond descriptive and predictive methods by recommending specific actions based on data analysis. It evaluates multiple scenarios, considers constraints such as traffic, road conditions, and safety, and suggests optimal decisions that achieve a desired outcome. This allows the vehicle to act autonomously while maximizing efficiency and safety.
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