1. Descriptive Analytics
What Happened?
This is the foundation of data analysis. It summarizes historical data to provide a clear picture of past events. By organizing data into understandable formats like charts and reports, descriptive analytics helps businesses track key performance indicators (KPIs) and identify initial trends.
The bar chart shown here is a classic example. It clearly visualizes website traffic from different sources over a month, allowing stakeholders to quickly see which channels are performing best. This is a purely factual representation of past data.
Example: Monthly Website Traffic
2. Diagnostic Analytics
Why Did It Happen?
Diagnostic analytics takes the next step by delving deeper to understand the root causes of the outcomes found in descriptive analysis. It involves techniques like correlation analysis and hypothesis testing to uncover relationships and dependencies in the data.
The scatter plot is a powerful diagnostic tool. Here, it visualizes the relationship between advertising spend and website clicks. By observing the pattern, analysts can diagnose whether higher spending leads to more clicks, helping to explain the "why" behind marketing performance.
Example: Ad Spend vs. Clicks
3. Predictive Analytics
What Might Happen?
This advanced stage uses historical data, statistical algorithms, and machine learning techniques to forecast future outcomes. Predictive analytics identifies the likelihood of future events, enabling businesses to anticipate trends, identify risks, and seize opportunities.
A line chart is commonly used to visualize predictions. This example shows actual sales from previous quarters and extends into a forecast for the upcoming year. This allows for proactive planning in inventory, staffing, and marketing based on expected demand.
Example: Quarterly Sales Projections
4. Prescriptive Analytics
What Should We Do?
The final frontier of analytics, prescriptive analytics goes beyond prediction to recommend specific actions to achieve a desired outcome. It uses optimization and simulation to advise on possible decisions and their implications, guiding businesses toward the best course of action.
This flowchart demonstrates a simple prescriptive model. Based on a customer's predicted churn risk (from a predictive model), the system recommends a specific action—either offer a discount or escalate to a retention specialist. This automates and optimizes decision-making.