Which type of analytics is right for your business? 

Businesses have access to more information than ever before. But knowing what to do with all that data? That’s where it gets tricky. Whether you’re just starting out or looking to sharpen your strategy, understanding the different types of data analytics can make a world of difference. Let’s break down the essentials—descriptive, predictive, and prescriptive analytics—so you can choose the right approach for your business. Our Data Science Essentials program dives into these topics and more.

Descriptive Analytics: What happened?

Descriptive analytics is all about understanding the past. It’s like looking in the rearview mirror to see what happened and why. Think of it as summarizing past sales trends, customer behaviors, or website traffic patterns. Tools like dashboards with for intance bar charts, and pie charts help make sense of the numbers, turning data into clear insights.

What’s needed? To get started with descriptive analytics, you’ll need clean, well-organized historical data—think spreadsheets of past sales or customer surveys. Basic skills in data visualization and tools like Excel or Tableau can go a long way. An analytical approach is essential—one that focuses on interpreting data accurately and connecting insights to meaningful business decisions.

Diagnostic Analytics: Why did it happen

Diagnostic analytics bridges the gap between descriptive and predictive analytics by answering the question: “Why did it happen?” By analyzing relationships and patterns in historical data, it uncovers the causes behind observed trends or events. Common techniques include correlation analysis, drill-down reports, and root cause analysis. To get started, you’ll need clean data and tools for exploratory data analysis, like SQL or advanced Excel functionalities.

Predictive Analytics: What will happen?

Now, let’s shift gears and look forward. Predictive analytics uses historical data to forecast what might happen next. It’s like having a crystal ball—only powered by data. This is where machine learning and statistical models come in, helping you anticipate customer needs, stock levels, or market trends. Picture a retailer using past purchase data to predict inventory needs for the next season. Predictive analytics helps you make smarter decisions, so you’re not just reacting but planning ahead.

What’s needed?

Here, you’ll need a dataset of sufficient size and quality to ensure reliable predictions. High-quality data that accurately represents the problem you’re trying to solve is more important than simply having a large volume of data.You’ll also need skills in programming (Python or R) and familiarity with machine learning libraries like scikit-learn or TensorFlow. Plus, computing power matters—cloud services like AWS or Google Cloud can handle heavy processing if your data is large.

Prescriptive Analytics: How can we make it happen?

Prescriptive analytics is like having a GPS for your business—it doesn’t just tell you what might happen; it gives you the best route to reach your destination. This type of analysis goes beyond predictions and provides recommendations on what actions to take. Think of it as a way to optimize your resources, find the best pricing strategy, or plan logistics. It’s about turning insights into action. For example, a logistics company might use prescriptive analytics to determine the fastest delivery routes, saving time and fuel.

What’s needed? To dive into prescriptive analytics, you need the data foundation from the previous stages, plus the ability to simulate different scenarios. Optimization software and advanced statistical knowledge are key. Skills in operations research or experience with tools like IBM CPLEX or Gurobi can make a big difference. Having access to data science expertise and a strong understanding of the specific business context is crucial for translating recommendations into real-world actions.

So, which one fits your needs?

These types of analytics build on one another. Descriptive analytics lays the groundwork by summarizing past data. Diagnostic analytics helps explain why trends occurred. Predictive analytics uses these insights to forecast what’s next. Finally, prescriptive analytics provides actionable recommendations based on those predictions. To implement prescriptive analytics effectively, you’ll need the outputs of the earlier stages to ensure accuracy and relevance.No matter where you are in your data journey, each type of analytics can help unlock new opportunities.

At JADS, we help you navigate the world of data with hands-on training and practical insights. Our Data Science Essentials program dives into these topics and more, equipping you with the tools to turn data into actionable strategies. Ready to take the next step? Let’s make data work for you.