Understanding the Four Types of Analytics and How to Use Them in Your Business

Data is the lifeblood of modern businesses, but raw data alone doesn’t tell you much. To unlock its potential, you need analytics—the process of turning data into actionable insights.

Analytics can be broken down into four key types: descriptive, diagnostic, predictive, and prescriptive. Each type answers a unique question about your data and plays a critical role in helping your business make smarter decisions.

In this post, we’ll explore what each type of analytics does, when to use them, and how they contribute to your organization’s growth. Plus, we’ll touch on how generative AI can supercharge your analytics efforts.

The Four Types of Analytics: A Roadmap to Data-Driven Success

Analytics isn’t just about crunching numbers—it’s about understanding the what, why, when, and how of your data to drive better outcomes. The four types of analytics—descriptive, diagnostic, predictive, and prescriptive—form a progression, often referred to as the analytics maturity model. Each type builds on the previous one, helping your business move from understanding the past to shaping the future.

Here’s a quick overview of the four types:

• Descriptive Analytics: What happened?

• Diagnostic Analytics: Why did it happen?

• Predictive Analytics: What will happen next?

• Prescriptive Analytics: What should we do about it?

  
  

Let’s dive into each type, explore how to get started, and see why they matter.

1. Descriptive Analytics: What Happened?

What is it? Descriptive analytics is the foundation of data analysis. It looks at historical data to answer, “What happened?” This is the most common type of analytics, used to generate reports and dashboards that summarize past performance. Examples include:

• How much revenue did we generate last quarter?

• What was our website traffic last month?

• How many customers stopped using our service?

  
  

Why it matters: Descriptive analytics gives you a clear picture of your business’s performance. It’s the starting point for any data-driven organization because it’s simple to implement and relies on readily available data.

How to get started: You’re likely already doing some form of descriptive analytics, whether it’s through spreadsheets, PDF reports, or basic dashboards. To take it to the next level, focus on:

• Standardization: Create repeatable processes, like automated weekly sales reports.

• Automation: Use tools to eliminate manual tasks (e.g., merging spreadsheets or running repetitive calculations).

• Visualization: Build dashboards with clear, effective visuals to communicate insights.

  
  

Pro Tip: Invest in a modern analytics tool (like Power BI or Tableau) to streamline reporting and ensure consistency. This sets a strong foundation before moving to more advanced analytics.

Why not stop here? While descriptive analytics is powerful for understanding the past, it doesn’t explain why things happened or what to do next. That’s where the next type comes in.

2. Diagnostic Analytics: Why Did It Happen?

What is it? Diagnostic analytics digs deeper into historical data to answer, “Why did it happen?” It helps uncover the root causes of trends or anomalies. For example:

• Why did our sales drop in Q2?

• Why are certain products outperforming others?

• Why are we losing customers in a specific region?

  
  

Why it matters: Diagnostic analytics adds context to your descriptive reports, helping you understand the drivers behind your data. It’s often overlooked, but skipping this step makes it harder to move to predictive or prescriptive analytics.

How to get started: If you’ve got descriptive analytics in place, you’re ready to layer on diagnostic tools. Many modern analytics platforms offer features like search-based insights or key driver analysis (e.g., Power BI’s Key Drivers or Qlik’s insight tools). To make diagnostic analytics work:

• Use tools to explore data relationships and identify patterns.

• Look for correlations, such as how marketing campaigns impact sales or how customer feedback ties to churn.

• Consider specialized platforms (like Sisu) for deeper diagnostic capabilities.

  
  

Pro Tip: Don’t rush to predictive analytics without mastering diagnostics. Understanding why something happened is critical before trying to predict what’s next.

3. Predictive Analytics: What Will Happen Next?

What is it? Predictive analytics uses historical data and machine learning to forecast future outcomes. It answers, “What’s likely to happen?” Use cases include:

• Predicting which customers are at risk of leaving.

• Forecasting equipment maintenance needs.

• Estimating future sales based on market trends.

  
  

Why it matters: Predictive analytics helps you stay ahead of the curve by anticipating trends and risks. It’s a game-changer for proactive decision-making.

How to get started: Predictive analytics requires a solid foundation in descriptive and diagnostic analytics. Here’s how to begin:

1. Define the problem: What do you want to predict? (e.g., customer churn, sales trends).

2. Prepare your data: Clean, organize, and ensure high-quality data for modeling.

3. Build models: Use machine learning tools to create predictive models. Start with a well-defined area, like sales, where your data is already reliable.

4. Test and refine: Validate your predictions and adjust as needed.

   
   

Pro Tip: Organizations with strong descriptive and diagnostic analytics are better positioned for predictive success because their data is already clean and well-structured.

4. Prescriptive Analytics: What Should We Do?

What is it? Prescriptive analytics is the most advanced type, combining insights from descriptive, diagnostic, and predictive analytics to recommend specific actions. It answers, “What should we do?” Examples include:

• Automatically adjusting pricing based on demand forecasts.

• Recommending training for employees based on performance data.

• Suggesting maintenance schedules to prevent equipment failures.

  
  

Why it matters: Prescriptive analytics takes the guesswork out of decision-making, guiding you toward the best course of action. It’s common in industries like healthcare, finance, and logistics, where precision is critical.

How to get started: Prescriptive analytics isn’t a standalone step—it builds on the other three types. To succeed:

• Ensure you have strong capabilities in descriptive, diagnostic, and predictive analytics.

• Clearly define the action you want to take and the criteria for triggering it (e.g., “If churn risk is above 70%, offer a discount”).

• Use advanced tools that integrate machine learning and decision-making logic.

  
  

Pro Tip: Prescriptive analytics is for mature organizations with well-defined use cases. Don’t rush into it without mastering the earlier stages.

Supercharging Analytics with Generative AI

The four types of analytics form a powerful framework, but generative AI is taking things to the next level. Unlike traditional analytics, which analyze existing data, generative AI creates new content or insights, enhancing how you interact with data.

What is generative AI? Generative AI uses machine learning to produce original outputs, like reports, predictions, or personalized recommendations. It makes analytics more intuitive by allowing you to explore data through natural language (e.g., asking, “Why did sales drop?”) and automating complex tasks.

How to get started:

• Align with goals: Ensure generative AI supports your business objectives, like improving customer experiences or streamlining operations.

• Assess your setup: Identify where generative AI can enhance your existing analytics (e.g., generating automated reports or uncovering hidden patterns).

• Engage stakeholders: Work with your team to define use cases, like creating personalized marketing content or automating diagnostic insights.

• Invest in tech and talent: Ensure you have the tools and skills to implement AI-driven analytics.

  
  

Why it matters: Generative AI doesn’t replace traditional analytics—it makes them better. It enables faster, more creative insights and empowers non-technical users to engage with data through conversational interfaces.

Key Takeaways

• Descriptive Analytics: Understand what happened with historical data. Focus on automation and clear visualizations.

• Diagnostic Analytics: Uncover why it happened. Use tools to dig into root causes and patterns.

• Predictive Analytics: Forecast what’s next with machine learning. Start with clean, reliable data.

• Prescriptive Analytics: Get actionable recommendations. Build on the other three types for success.

• Generative AI: Amplify your analytics with intuitive, creative insights and automation.

  
  

To truly unlock the value of your data, treat analytics as a journey. Start with descriptive analytics, build your capabilities step by step, and layer in advanced tools like generative AI to stay ahead. By moving up the analytics maturity model, you’ll transform raw data into a strategic asset that drives your business forward.

Ready to take your analytics to the next level? Share your thoughts in the comments or reach out to discuss how analytics can transform your business!