MIS & Analytics

What is the primary goal of Business Intelligence?

• To provide insights and analysis of data to help organizations make better decisions.

• To waste time and resources without any tangible benefits

• To make decisions based on random guesses and gut feelings

• To create confusion and chaos within organizations

Explain the process of Data Mining and its significance in business.

• Data mining is a process of deleting unnecessary data from a database

• Data mining is a process of extracting minerals from the earth

• Data mining helps businesses make better decisions by identifying patterns, trends, and relationships in their data, leading to improved marketing, sales, and customer satisfaction.

• Data mining is a process of creating new data from scratch

How is Predictive Analytics different from Descriptive Analytics?

• Predictive Analytics uses future data to make predictions, while Descriptive Analytics focuses on analyzing past data

• Predictive Analytics and Descriptive Analytics are the same and can be used interchangeably

• Predictive Analytics only focuses on analyzing past data, while Descriptive Analytics uses historical data to make predictions about future outcomes

• Predictive Analytics uses historical data to make predictions about future outcomes, while Descriptive Analytics focuses on analyzing past data to understand what has happened.

What are the key components of a Decision Support System?

• Data management, model management, user interface, and knowledge-based system

• User authentication, user authorization, user management, and user interface design

• Data analysis, data visualization, data storage, and data security

• Hardware management, software management, network management, and database management

Discuss the concept of Big Data Analytics and its impact on businesses.

• Big Data Analytics is the process of analyzing small and uniform data sets to uncover obvious patterns and correlations.

• Big Data Analytics has no impact on businesses and is only useful for academic research.

• Big Data Analytics only provides basic information and has no relevance to customer preferences or market trends.

• Big Data Analytics is the process of examining large and varied data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful business information. Its impact on businesses includes improved decision-making, better customer insights, enhanced operational efficiency, and the ability to create new products and services.

What are the different types of Business Intelligence tools?

• Customer relationship management tools, supply chain management tools, and human resource management tools

• Reporting tools, data visualization tools, self-service BI tools, and advanced analytics tools

• Social media tools, email marketing tools, and project management tools

• Accounting software, inventory management tools, and payroll processing tools

What are the common techniques used in Data Mining?

• Data Sampling, Data Aggregation, Data Validation

• Data Sorting, Data Filtering, Data Visualization

• Data Encryption, Data Compression, Data Decryption

• Clustering, Classification, Regression, Association Rule Mining, Anomaly Detection

Give an example of how Predictive Analytics is used in real-world scenarios.

• Designing a new product

• Predicting the weather

• Creating art

• Forecasting customer behavior, optimizing marketing strategies, improving operational efficiency, and preventing fraud.

How can Decision Support Systems help in improving decision-making processes?

• By providing irrelevant information and confusing data analysis

• By making decisions for the users without their input

• By limiting access to information and data analysis tools

• By providing relevant information, data analysis, and modeling tools to support decision makers in evaluating options and making informed decisions.

Explain the challenges associated with implementing Big Data Analytics in an organization.

• Data security and privacy concerns, data integration, scalability of infrastructure, and the need for skilled data analysts and data scientists.

• Low demand for data analysis in the market

• Limited data storage capacity

• Lack of interest from top management