Big Data and Decision Making Quiz

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Questions and Answers

What is big data primarily characterized by?

Limited access to online data sources

A rapid increase in the rate and quantity of data (correct)

A static amount of data

Data that is exclusively qualitative

Which aspect of decision making is most affected by the abundance of digital information?

Comparative decision types

The decentralization of decision-making processes

The ability to make informed decisions (correct)

Analysis and reporting tools

Which of the following distinguishes data mining from other data analysis techniques?

It uses only qualitative data

It requires no technical skills or tools

It focuses on discovering patterns and insights from large data sets (correct)

It is a one-time analysis

How do data warehouses differ from data marts?

Data warehouses store data for longer periods than data marts Signup and view all the answers

Which decision support tool is most closely associated with business intelligence?

Predictive analytics Signup and view all the answers

What type of decisions can be differentiated by their organizational level?

Strategic, tactical, and operational decisions Signup and view all the answers

Which of the following best describes the role of business intelligence in decision making?

It provides insights that enhance decision making Signup and view all the answers

In the context of decision support systems, which of the following statements is accurate?

They integrate data and analytical models to assist with complex decision making Signup and view all the answers

What is the first step in the decision-making process?

Recognize that a decision needs to be made Signup and view all the answers

Which type of decision is based directly on inputs and is commonly made?

Programmed decision Signup and view all the answers

What is a characteristic of heuristics in decision-making?

They save time for decision makers. Signup and view all the answers

What defines an unstructured decision?

It involves many unknowns and ambiguous criteria. Signup and view all the answers

Which of the following scenarios illustrates a semi-structured decision?

Selecting a candidate based on both skills and personality fit. Signup and view all the answers

Why is increasing effectiveness in decision-making critical?

It directly affects the firm's overall survival. Signup and view all the answers

What is a common issue with the decision-making process that individuals encounter?

Skipping steps or spending too little time on some. Signup and view all the answers

What role do information systems play in unstructured decision-making?

They assist in information gathering and collaboration. Signup and view all the answers

In decision-making, a programmed decision is typically associated with which of the following?

Retail inventory management. Signup and view all the answers

What can lead to adverse outcomes within a department under lower-level management?

Poor decision-making by managers. Signup and view all the answers

What is the expected amount of data created globally by 2025?

180 zettabytes Signup and view all the answers

Which of the following is NOT one of the three V's that categorize big data?

Value Signup and view all the answers

What does the 'Velocity' aspect of big data refer to?

The speed of data processing Signup and view all the answers

How does an increase in 'Variety' of data affect traditional analytical techniques?

It reduces the suitability of traditional techniques Signup and view all the answers

What is the primary implication of big data for managerial decision-making?

It complicates the data transformation process Signup and view all the answers

Which statement best describes the role of stakeholders in decision-making?

Stakeholders are all individuals/groups affected by an organization Signup and view all the answers

What is a primary challenge posed by large datasets in big data analytics?

The inability to move data easily from storage to analysis Signup and view all the answers

What can result from poor managerial decision-making?

Bankruptcy Signup and view all the answers

Why is it important for organizations to consider data characteristics when designing processes?

To facilitate better management decision-making Signup and view all the answers

What is indicated by the increasing amount of data collected by organizations?

Data storage costs are decreasing Signup and view all the answers

What type of decisions are typically made by top management teams?

Strategic decisions Signup and view all the answers

What is a primary function of a decision support system (DSS)?

To assist in making decisions using interactive models Signup and view all the answers

Which of the following is an example of a tactical decision?

How should we market the new product line? Signup and view all the answers

Which of these decisions is categorized as operational?

What should I say to customers about our new product? Signup and view all the answers

What type of information systems are typically used to support tactical decisions?

Decision support systems Signup and view all the answers

How can a decision support system create competitive advantage for an organization?

By assisting in wise decision-making regarding products and innovations Signup and view all the answers

Which group within an organization typically makes strategic decisions?

Top Management Teams Signup and view all the answers

What kind of decision-making challenges does artificial intelligence help address?

Semistructured and unstructured decisions Signup and view all the answers

What role does a manager play when using a decision support system?

Entering values to explore potential outcomes Signup and view all the answers

What would be an example of a strategic decision in a restaurant context?

Deciding to improve customer service standards Signup and view all the answers

What is the primary purpose of inputting data into a decision support system (DSS)?

To analyze inputs and provide recommendations Signup and view all the answers

Which analysis technique in a decision support system helps determine the effects of changing one variable multiple times?

Sensitivity Analysis Signup and view all the answers

What distinguishes an Executive Information System (EIS) from a traditional decision support system?

EIS provides specific information tailored for executives Signup and view all the answers

Which feature is commonly associated with a Business Intelligence (BI) dashboard?

All key metrics displayed in one place Signup and view all the answers

What type of system utilizes artificial intelligence to provide similar advice to that of a human consultant?

Expert System Signup and view all the answers

Which analysis technique aims to find the necessary inputs to achieve a specific goal?

Goal Seek Analysis Signup and view all the answers

During the COVID pandemic, which tool was notably used for tracking the virus and vaccination rates?

GIS Hub by Esri Canada Signup and view all the answers

What is a key benefit of using predictive analytics in business?

Enhancing inventory management Signup and view all the answers

How does Optimization Analysis function within a decision support system?

It identifies the best possible value for a target variable Signup and view all the answers

Why are expert systems becoming more common in businesses?

They assist in complex problem-solving tasks efficiently Signup and view all the answers

What is the primary focus of data science?

Analyzing large data sets to find new knowledge Signup and view all the answers

Which of the following accurately describes data analytics?

It combines analysis and communication to explore past trends. Signup and view all the answers

What are the four main categories of analytics?

Descriptive, Diagnostic, Predictive, Prescriptive Signup and view all the answers

How does the focus of business intelligence differ from data analytics?

Business intelligence focuses on historical trends, while data analytics looks to the future. Signup and view all the answers

Which statement best describes data brokers?

They collect and sell data based on online activities. Signup and view all the answers

What role does data engineering play in the data process?

Designs, creates, and structures data and datasets. Signup and view all the answers

What kind of insights does predictive analytics provide?

Potential future events and outcomes. Signup and view all the answers

Which term refers specifically to the techniques used to refine and manipulate data?

Data Analysis Signup and view all the answers

Why is the data broker market flourishing in Canada?

Less safeguards on personal information. Signup and view all the answers

What is a primary task of a data scientist?

Building new models and algorithms to extract data in innovative ways. Signup and view all the answers

What is a primary advantage of using OLAP tools for data analysis?

They can provide fast access to pre-calculated data across multiple dimensions. Signup and view all the answers

How does data visualization aid in understanding large datasets?

It presents data graphically for quicker insights and understanding. Signup and view all the answers

What is a key characteristic of canned reports?

They provide regular summaries in a fixed format. Signup and view all the answers

What does 'slicing and dicing' refer to in the context of data analysis?

Analyzing data by manipulating multiple dimensions for deeper insights. Signup and view all the answers

What role does a data cube play in OLAP?

It pre-calculates and organizes data across multiple dimensions. Signup and view all the answers

Which statement best describes ad hoc reporting tools?

They allow users to create custom reports based on specific criteria. Signup and view all the answers

What is the primary purpose of dashboards in data visualization?

To provide an overview of critical indicators for managers. Signup and view all the answers

Why might historical sales trend analysis be more demanding on a database system?

It necessitates processing a large volume of transaction records for comparisons. Signup and view all the answers

What is a significant benefit of using tools like Tableau and Google Data Studio?

They enable the creation of intuitive graphical representations of data. Signup and view all the answers

What is the purpose of 'what if' scenarios in spreadsheet modeling?

To analyze different outcomes based on variable changes. Signup and view all the answers

What is the primary purpose of prescriptive analytics?

To analyze data and recommend actions Signup and view all the answers

Which factor is crucial for the success of data analysis projects?

Having a clear vision with business-focused objectives Signup and view all the answers

What challenge arises from using outdated legacy systems?

They can lead to duplication or incomplete information. Signup and view all the answers

Which aspect of data management is emphasized by data governance?

Ensuring data meets reliability and quality standards Signup and view all the answers

What is a critical question regarding data sourcing?

Is there a need to set up new systems or surveys? Signup and view all the answers

How should organizations handle data quality issues?

By scrubbing, calculating, and consolidating data Signup and view all the answers

Why is having an executive champion important in data analysis projects?

They can illustrate objectives to secure support. Signup and view all the answers

Which of the following is a potential issue from mergers and acquisitions?

Operational systems may become incompatible. Signup and view all the answers

What is a key factor in determining data relevance?

How well the data aligns with current and future goals. Signup and view all the answers

Which of these is NOT a consideration in data analysis projects?

Data aesthetics and presentation style Signup and view all the answers

Which of the following best describes an expert system?

A system that provides advice similar to a human consultant. Signup and view all the answers

What characterizes legacy systems?

They do not support data sharing and are often outdated. Signup and view all the answers

What type of decision is categorized as structured?

A decision made often, based directly on specific inputs. Signup and view all the answers

Which of the following best defines unstructured decisions?

Decisions that involve a high degree of uncertainty. Signup and view all the answers

Online Analytical Processing (OLAP) is primarily associated with which feature?

Data sourced from standard databases and summarized in advance. Signup and view all the answers

What type of decisions do operational decisions refer to?

Daily decisions made to maintain routine operations. Signup and view all the answers

Which decision-making category involves known factors but is still impacted by human experience?

Semi-Structured Decision Signup and view all the answers

In what way do strategic decisions differ from tactical decisions?

Strategic decisions set the overall direction of the organization. Signup and view all the answers

What is the primary purpose of a data warehouse in an organization?

To support decision making through data analysis. Signup and view all the answers

What does ETL stand for in the context of data warehouses?

Extract-Transform-Load Signup and view all the answers

Which approach to data warehouse design involves creating small data marts first?

Bottom-up approach Signup and view all the answers

How is data in a data warehouse characterized in terms of time?

Time-variant with timestamps. Signup and view all the answers

Why is standardization important in a data warehouse?

To match up data from different sources. Signup and view all the answers

What is a data mart primarily designed to address?

Specific problems or business units. Signup and view all the answers

Which of the following is a key benefit of developing a data warehouse?

Encouraging a better understanding of collected data. Signup and view all the answers

What type of data does a data warehouse utilize?

Non-operational data. Signup and view all the answers

What challenge does a data warehouse aim to alleviate while running analytics?

System performance degradation due to transactional data. Signup and view all the answers

What role does the data loading process play in a data warehouse?

It prepares data for storage and analysis. Signup and view all the answers

What is a primary advantage of having a data warehouse for an organization?

It provides historical records for trend analysis. Signup and view all the answers

What is a major privacy concern associated with data mining?

Data mining can violate public trust. Signup and view all the answers

How does a decision support system (DSS) assist managers?

Through interactive models that simulate real-world processes. Signup and view all the answers

What is one disadvantage of legacy systems in data utilization?

They may not support current business needs. Signup and view all the answers

What is the main focus of business intelligence in an organization?

Analyzing data for competitive advantage. Signup and view all the answers

Which of the following describes what a data mart is designed for?

Addressing specific business unit concerns. Signup and view all the answers

What role does data visualization play in business analysis?

It enhances the understanding of data findings. Signup and view all the answers

What does the 'Volume' characteristic of big data refer to?

The sheer amount of data available. Signup and view all the answers

What is a key feature of expert systems in decision-making?

They offer advice similar to a human consultant. Signup and view all the answers

What prompts organizations to use data analytics?

To understand past data and predict future outcomes. Signup and view all the answers

What is the primary purpose of data mining?

To analyze data for previously unknown patterns Signup and view all the answers

Which of the following is a key area where businesses leverage data mining?

Customer segmentation Signup and view all the answers

Which condition is critical for effective data mining?

Clean and consistent data Signup and view all the answers

What challenge might arise from over-engineering a data mining model?

Creating a model that functions on a limited subset of data Signup and view all the answers

Which skill is NOT critical for a successful data mining and business analytics team?

Advanced graphic design Signup and view all the answers

What can lead to ineffective models in data mining?

Ignoring historical market trends Signup and view all the answers

Collaboration filtering in data mining is primarily aimed at achieving what?

Personalizing customer experiences using similar preferences Signup and view all the answers

What is the purpose of splitting data into different portions while building a mining model?

To validate the model's results against new data Signup and view all the answers

Identification of characteristics consistent with employee success falls under which data mining application?

Hiring and promotion Signup and view all the answers

What is a potential risk associated with the data mining process?

Deriving insights too quickly and prematurely Signup and view all the answers

What characterizes a data mart compared to a data warehouse?

It focuses on specific problems or business units. Signup and view all the answers

What is the primary function of data analytics in business?

To analyze past data for insights and predictions. Signup and view all the answers

Which statement best differentiates canned reports from ad hoc reporting?

Canned reports are generated periodically; ad hoc reporting is on demand. Signup and view all the answers

What must be present for effective data mining?

High data quality and appropriate analytical techniques. Signup and view all the answers

Which of the following is a key distinction between business intelligence and data science?

Business intelligence focuses on historical data; data science emphasizes predictive analytics. Signup and view all the answers

How do OLAP reports differ from conventional reports?

OLAP reports allow for multi-dimensional analysis; conventional reports are typically two-dimensional. Signup and view all the answers

What is a primary component of data visualization?

Graphical representation of data. Signup and view all the answers

Which skill is essential for a competent business analytics team?

Knowledge of analytical methods and techniques. Signup and view all the answers

In what way do decision support systems enhance the decision-making process?

By presenting data in a simplified format for easier interpretation. Signup and view all the answers

Study Notes

Chapter Overview

Focuses on decision support in organizations, emphasizing the importance of data in managerial decision-making.
Highlights the ongoing transformation in business practices due to an influx of digital data, especially during the COVID-19 pandemic.

Big Data

Global data creation is projected to reach 180 zettabytes by 2025, illustrating rapid data growth.
Big data characteristics include Volume (size of datasets), Variety (types of data), and Velocity (speed of data processing).
Organizations face challenges in handling and analyzing vast amounts of diverse data due to limitations of traditional systems.
Companies must consider data characteristics when designing decision-making processes to derive informed insights.

Managerial Decision Making

Decision-making is a continuous process that significantly affects organizational effectiveness and stakeholder interests.
Effective decisions can drive long-term success, while poor decisions risk bankruptcy or adverse outcomes.
The decision-making process includes six systematic steps: recognizing the need, generating alternatives, analyzing, selecting, implementing, and evaluating.

Decision Types

Decisions vary in structure:
- Structured Decisions: Frequent, based on clear inputs (e.g., inventory reorder levels).
- Semi-Structured Decisions: Combine known factors with human judgment (e.g., hiring process).
- Unstructured Decisions: Largely unknowns with ambiguous criteria, requiring creative thinking (e.g., handling labor issues).
Decision-making can also be categorized by organizational levels:
- Strategic Decisions: Made by top management; set long-term direction (e.g., mergers, new product lines).
- Tactical Decisions: Made by managers; focus on operational execution (e.g., team integration post-merger).
- Operational Decisions: Day-to-day choices made by employees impacting workflow (e.g., managing customer complaints).

Decision Support Systems (DSS)

DSS aids managers in decision-making through interactive models, analyzing real-world processes.
Types of DSS include predictive analytics, which uses data to inform inventory management and customer targeting.
Key analysis techniques:
- What-If Analysis: Examines effects of variable changes.
- Sensitivity Analysis: Analyzes impacts of variable modifications.
- Goal Seek Analysis: Identifies required inputs to meet a goal.
- Optimization Analysis: Finds best values for target variables.
Executive Information Systems (EIS): Tailored for executives, providing strategic insights via dashboards displaying key metrics.

Business Intelligence & Data Analytics

Business intelligence (BI) involves processes to gather and analyze data for competitive advantage.
Organizations use data warehouses and purchase insights from data brokers to enhance market understanding.
Differentiation between terminologies:
- Data Engineering: Focuses on data structure and creation.
- Data Analysis: Techniques to understand and manipulate data.
- Data Communication: Flow of data from sources to users.
Data Science: Integrates engineering, analysis, and communication, centering on large data sets to generate new knowledge, often employing machine learning techniques.### Data Analytics Overview
Data analytics combines analysis and communication to understand past data and make future predictions.
It focuses on answering questions through trend analysis and visual representations like dashboards.
Differentiates from data science, which concerns figuring out the right questions about data.
Business intelligence analyzes past data while data analytics can forecast future outcomes.

Types of Data Analytics

Analytics can be categorized into four types:
- Descriptive Analytics: Answers "What happened?" by summarizing historical data (e.g., mean, median).
- Diagnostic Analytics: Explores "Why did it happen?" through techniques like scatter plots to identify outliers.
- Predictive Analytics: Estimates "What might happen?" using regression analysis to forecast future trends based on historical data.
- Prescriptive Analytics: Advises "What to do next?" leveraging decision support systems and machine learning for optimized solutions.

Data Analysis Projects

Initiate projects with clear, business-focused objectives to gain executive support and drive technology choices.
Considerations for projects include:
- Data Relevance: Identifying necessary data to reach current and future goals.
- Data Sourcing: Determining where to obtain data, from internal systems to third-party sources.
- Data Quantity & Quality: Assessing the amount and reliability of data, ensuring it is clean and accurate.
- Data Hosting: Establishing infrastructure for data storage and analysis.
- Data Governance: Implementing rules to manage data throughout its lifecycle, addressing privacy and security concerns.

Data Governance

Essential for ensuring data reliability and value within organizations.
Strategies include various processes and standards to tackle bad data, such as duplication or outdated systems.
Legacy systems pose risks due to incompatibility and can hinder data communication, especially during mergers.

Analysis and Reporting Tools

Tools are designed to help managers query and report on data efficiently.
Canned reports provide regular information, while ad hoc reporting tools allow custom report creation.
Online Analytical Processing (OLAP) enhances reporting speed, enabling quick comparisons across multiple dimensions.

Data Visualization

Represents data graphically (charts, graphs, maps), making large data sets more understandable.
Software examples include Tableau and Google Data Studio, which aid in summarizing data visually.
Dashboards present critical indicators at a glance, facilitating effective decision-making.

Data Mining

Involves analyzing data to discover unknown trends and patterns for informed decision-making.
Applicable areas include customer segmentation, marketing targeting, and fraud detection.
Requires clean, consistent data to be effective, and must reflect current and historical trends.

Model Considerations for Data Mining

Success hinges on having reliable data and avoiding over-engineering of models.
Continuous verification through data division can help validate model results.
Essential skills in a data mining team include IT, statistics, and business knowledge for insightful analysis.

Data Repositories

Data warehouses serve as centralized storage for analytical purposes, enhancing reporting speed and data consistency.
They extract and aggregate data from multiple sources, providing a historical record for trend analysis.
Data marts address specific business issues, complementing the larger data warehouse.

Privacy Concerns

Data mining can raise privacy issues as it combines information across sources, often handled by data brokers.
Awareness of privacy protections is crucial when handling extensive data sets about individuals and organizations.

Key Takeaways

Big data encompasses volume, variety, and velocity, posing challenges for processing and analytics.
Decision-making processes can leverage systems like DSS and EIS for enhanced managerial support.
Business intelligence integrates various processes and technologies to analyze data for competitive advantage.### Data Science and Data Analytics
Data Science: Involves analyzing large datasets to discover new knowledge and insights.
Data Analytics: Focuses on examining past data to understand events and make future predictions.

Challenges of Legacy Systems

Legacy Systems: Often hinder data utilization due to poor data sharing capabilities, incompatibility with new technologies, and misalignment with current business needs.
Transactional Databases: Typically not designed for simultaneous access for reporting and analysis.

Data Warehousing

Data Warehouse: A collection of databases that supports organizational decision-making by consolidating data from various sources for fast querying and exploration.
Data Mart: A specialized subset of a data warehouse, focusing on specific business problems or units (e.g., marketing or product quality).

Data Privacy

Concerns about data privacy have intensified with the exponential growth of digital data being generated and stored.

Reporting and Visualization

Data Visualization: Graphical representation of information can enhance understanding and lead to new insights (includes charts, graphs, and maps).
Canned Reports: Pre-formatted reports providing regular updates, but often difficult to modify.
Ad Hoc Reporting: Enables users to create custom reports by selecting fields and parameters as needed.

Decision-Making Processes

Structured Decisions: Frequently made and based directly on clear inputs.
Semi-Structured Decisions: Knowledgeable factors involved, but human experience plays a role.
Unstructured Decisions: Characterized by unknown factors and complexities.

Business Intelligence vs. Data Analytics vs. Data Science

Business Intelligence: Process of collecting and analyzing data to gain a competitive edge.
Data Analytics: Examines historical data to understand and predict trends.
Data Science: Explores large datasets, often pushing the boundaries of current analytical methods.