DATA ANALYTICS
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Types of Data Analysis
✅ 1. Descriptive Analysis – What Happened?
Descriptive analysis is the most basic type of data analysis. It answers the question: “What happened?”
This type of analysis looks at historical data to summarize and identify patterns or trends.
🔹Key Goals :
Summarize raw data into meaningful insights
Identify basic trends, totals, or averages
🔹 Tools & Techniques:
Excel (charts, pivot tables)
SQL queries (aggregations like COUNT, SUM, AVG)
Dashboards in Power BI or Tableau
🔹 Examples:
“Total sales in January were ₹5,00,000.”
“Our website had 10,000 visitors last week.”
“80% of our customers are from Delhi and Mumbai.”
Descriptive analytics helps stakeholders understand the current state of the business or system.
✅ 2. Diagnostic Analysis – Why Did It Happen?
Diagnostic analysis goes one step deeper and answers: “Why did it happen?”
It explores the reasons behind trends or anomalies found in the descriptive analysis.
This type of analysis looks at historical data to summarize and identify patterns or trends.
🔹Key Goals :
Identify causes of specific outcomes
Discover relationships and correlations
🔹 Tools & Techniques:
Drill-down in dashboards
Statistical analysis (correlation, regression)
Advanced Excel or Python analysis
🔹 Examples:
“Why did sales drop in March?”
“Why are customers leaving the website quickly?”
“What factors led to an increase in cart abandonment?”
This type of analysis is useful for identifying problems and making improvements.
✅ 3. Predictive Analysis – What Is Likely to Happen?
Predictive analysis uses historical data and statistical models to answer: “What is likely to happen in the future?”
This type of analysis forecasts future outcomes using trends, patterns, and machine learning models.
🔹Key Goals :
Forecast future trends or behaviors
Anticipate risks or opportunities
🔹 Tools & Techniques:
Regression analysis
Machine learning (Python, R, tools like Scikit-learn)
Time series forecasting
🔹 Examples:
“Sales are expected to grow by 10% next quarter.”
“Which customers are likely to cancel their subscription?”
“Which products will sell more during the festive season?”
Predictive analysis helps businesses plan better and stay ahead of the competition.
✅ 4. Prescriptive Analysis – What Should We Do?
Prescriptive analysis goes beyond prediction and answers: “What should we do about it?”
It recommends actions based on data insights to achieve desired outcomes or avoid risks.
🔹Key Goals :
Suggest the best course of action
Optimize decision-making
🔹 Tools & Techniques:
Optimization algorithms
Decision trees
AI and recommendation engines
🔹 Examples:
“Should we launch a discount campaign to boost sales?”
“What’s the best time to post on social media?”
“Which delivery route will save the most fuel?”
Prescriptive analytics supports decision-makers with data-backed recommendations.
