Business Intelligence: Descriptive Analytics

What is descriptive analytics?

• Descriptive analytics is the process of predicting future events based on historical data.

• Descriptive analytics is the process of analyzing historical data to understand past events and trends.

• Descriptive analytics is the process of analyzing real-time data to understand current events and trends.

• Descriptive analytics is the process of analyzing data to understand potential future events and trends.

What are the key components of descriptive analytics?

• Qualitative research, survey design, and hypothesis testing

• Statistical analysis, predictive modeling, and machine learning

• Key components of descriptive analytics include data aggregation, data mining, and data visualization.

• Financial forecasting, trend analysis, and decision support systems

How is descriptive analytics different from predictive analytics?

• Descriptive analytics predicts future outcomes, while predictive analytics summarizes historical data.

• Descriptive analytics and predictive analytics are both used for analyzing real-time data.

• Descriptive analytics and predictive analytics are the same thing.

• Descriptive analytics summarizes historical data, while predictive analytics makes predictions about future outcomes.

Give an example of a descriptive analytics technique used in business intelligence.

• Machine learning

• Statistical analysis

• Data mining

• Predictive modeling

What are the benefits of using descriptive analytics in business?

• Descriptive analytics only focuses on real-time data

• Descriptive analytics provides insights into historical data and helps in understanding past performance and trends.

• Descriptive analytics has no impact on understanding past performance

• Descriptive analytics predicts future trends

Explain the process of data aggregation in descriptive analytics.

• Data aggregation in descriptive analytics involves combining and summarizing data to provide a high-level view of the information.

• Data aggregation in descriptive analytics means ignoring and discarding data to provide a high-level view of the information.

• Data aggregation involves separating and complicating data to provide a detailed view of the information.

• Data aggregation involves encrypting and securing data to provide a high-level view of the information.

What are some common tools or software used for descriptive analytics?

• AutoCAD

• Microsoft Word

• Adobe Photoshop

• Microsoft Excel, Tableau, Power BI, Google Analytics, SAS

Discuss the role of visualization in descriptive analytics.

• Visualization has no role in descriptive analytics

• Descriptive analytics does not require any visualization

• Visualization only confuses the interpretation of data

• Visualization helps present data in a clear and understandable way, allowing for the identification of patterns, trends, and outliers.

How can descriptive analytics help in decision-making for businesses?

• By creating new products and services

• By predicting future market trends

• By analyzing customer feedback

• By providing insights into past performance and trends

What are some challenges or limitations of descriptive analytics?

• Ability to predict future outcomes accurately

• Insights into causality

• Suitability for complex or unstructured data

• Lack of insights into causality or future outcomes, and unsuitability for complex or unstructured data