LESSON 5: NETWORK MODELING Flashcards
In data-driven business practices, network modeling is used to which among the succeeding choices?
A) Create marketing campaigns
B) Optimize supply chain routes
C) Analyze customer feedback
D) Generate financial reports
Optimize supply chain routes
The primary goal of network modeling in data management is to which among the following?
A) Maximize data storage capacity
B) Minimize data security risks
C) Improve decision-making by modeling relationships among data elements
D) Enhance employee engagement
Improve decision-making by modeling relationships among data elements
Which of the following is a common application of network modeling in data-driven businesses?
A) Managing office supplies
B) Analyzing competitor pricing strategies
C) Optimizing transportation routes
D) Measuring employee productivity
Analyzing competitor pricing strategies
Network modeling techniques aim to uncover patterns and relationships in data to assist with:
A) Reducing operational costs
B) Identifying customer demographics
C) Managing financial assets
D) Creating social media content
Reducing operational costs
In data-driven business practices, network modeling can be utilized to optimize the flow of goods and services in:
A) Employee workspaces
B) Supply chains and logistics
C) Marketing campaigns
D) Customer service centers
Supply chains and logistics
Which of the following best describes the concept of network modeling in a data-driven business context?
A) Creating graphical representations of data relationships
B) Conducting market research
C) Monitoring employee attendance
D) Analyzing historical financial data
Creating graphical representations of data relationships
When using network modeling for optimizing supply chain routes, a key objective is to which of the aforementioned?
A) Maximize travel time
B) Minimize network complexity
C) Minimize transportation costs
D) Optimize office layouts
Minimize transportation costs
Network modeling techniques are commonly used to analyze data structures and relationships, making them valuable for:
A) Identifying market trends
B) Assessing customer satisfaction
C) Managing inventory
D) Understanding data dependencies
Understanding data dependencies
How does network modeling relate to prescriptive analytics in data management?
A) It provides insights into optimal decision-making based on data relationships
B) It generates customer surveys
C) It analyzes social media trends
D) It tracks employee performance
It provides insights into optimal decision-making based on data relationships
A data-driven business wants to optimize its transportation routes to minimize costs. Which technique is most suitable for this problem?
A) Descriptive analytics
B) Predictive analytics
C) Network modeling
D) Monitoring employee work hours
Network modeling
Network modeling is valuable for decision-making in a data-driven business because it promotes which of the following?
A) Reveals complex data relationships and dependencies
B) Tracks employee attendance
C) Measures customer loyalty
D) Analyzes competitor pricing strategies
Reveals complex data relationships and dependencies
Which of the following is a key concept in network modeling for data-driven decision-making?
A) Data silos
B) Customer demographics
C) Connectivity and relationships among data elements
D) Marketing budgets
Connectivity and relationships among data elements
Network modeling can assist in optimizing data flow by analyzing the connections and dependencies between:
A) Customer reviews
B) Employee training programs
C) Data elements or entities
D) Office layouts
Data elements or entities
In network modeling, the “edges” represent which of the subsequent choices?
A) Employee satisfaction scores
B) Social media posts
C) Relationships or connections between nodes
D) Competitor products
Relationships or connections between nodes
Network modeling can help data-driven businesses by providing insights into the flow of data and dependencies, aiding in:
A) Employee turnover management
B) Supplier negotiations
C) Data-driven decision-making
D) Social media engagement
Data-driven decision-making
