Descriptive analytics involves analyzing historical data to understand what has happened in the past. It provides insights into trends, patterns, and performance metrics.

• Process:
  • Data Preparation: Collect and clean historical data from relevant sources.
  • Data Analysis: Use statistical techniques and tools to analyze the data. Identify trends, patterns, and key performance indicators (KPIs).
  • Visualization: Create visualizations, such as charts, graphs, and dashboards, to present the findings in an easily understandable format.
  • Reporting: Generate reports that summarize the insights and provide actionable recommendations.
• Purpose:
  The goal of descriptive analytics is to provide a clear understanding of past performance and trends, enabling organizations to make informed decisions.
• Outcome:
  Insights into historical performance and trends that inform decision-making and strategy development.
• Challenges:
  Ensuring data accuracy and completeness can be challenging. Additionally, interpreting the results and translating them into actionable insights requires expertise.
• Best Practices:
  • Use a mix of quantitative and qualitative data to gain a comprehensive understanding of trends.
  • Regularly update and validate data to ensure accuracy.
  • Use visualization tools to present findings in a clear and engaging way.
  • Focus on actionable insights that drive decision-making.