Descriptive Analytics
Definition:
Descriptive Analytics refers to the process of analyzing historical data to understand trends, patterns, and insights that have occurred in the past.
It focuses on answering the "what happened" question by summarizing data into understandable formats like reports, dashboards, or charts.
Key Techniques in Descriptive Analytics:
- 1. Data Aggregation: Data aggregation involves summarizing large datasets to draw meaningful insights. This can be done through measures such as sums, averages, and counts.
- 2. Data Visualization: Using visual tools like charts, graphs, and heat maps to represent data allows for easy interpretation of trends and patterns.
- 3. Reporting: Routine or ad-hoc reports are generated to analyze and present historical performance data to stakeholders, enabling a comprehensive overview of key metrics.
Benefits of Descriptive Analytics:
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1. Improved Decision-Making: By reviewing historical performance, businesses can make informed decisions based on past data, minimizing risk and uncertainty.
- 2. Identifying Patterns and Trends: Descriptive analytics helps uncover recurring patterns and trends, providing insights into customer behavior, sales cycles, or operational processes.
- 3. Actionable Insights for Optimization: Businesses can use descriptive analytics to recognize areas that need improvement, such as low-performing campaigns or underutilized resources, allowing for timely adjustments.
Other Terms:
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