Today, leadership teams are expected to make faster and better decisions in an environment characterized by rapid change, increasing complexity, and new opportunities in AI. At the same time, companies have access to more data than ever before. But more data doesn’t automatically lead to better decisions. Real value only emerges when information is transformed into insights, recommendations, and action.
Previously, it was often enough to understand what had happened. Today, companies increasingly need to be able to predict what will happen, understand what actions should be taken, and in some cases allow systems and AI to support or automate parts of decision-making.
This places new demands on data, processes, and analytical capabilities. At the same time, it’s becoming increasingly clear that data itself rarely creates business value. Real value only emerges when it’s placed in its business context and connected to operational processes, goals, and decisions.
The five levels of data analytics
As the demands for faster and more data-driven decisions increase, it’s also important to understand which type of analysis is suitable for different situations.
Data analytics is often divided into five levels.
1️⃣ Descriptive analytics – What happened?
Basic analysis of historical data that shows how different key metrics have developed over time. It helps you understand how the business has performed, identify trends, and detect anomalies. It forms the foundation for all other analysis.
2️⃣ Diagnostic analytics – Why did it happen?
Diagnostic analytics, sometimes called root cause analysis, helps you understand why the development looks the way it does. Through drill-down analyses, segmentation, and identification of correlations, you can find underlying causes of different outcomes.
3️⃣ Predictive analytics – What will happen?
Here, historical data, statistical models, and algorithms are used to make forecasts about the future. This can include everything from demand, customer behaviors, and cash flows to material prices, maintenance needs, or employee turnover. The purpose is to give the business better foresight and the ability to act proactively.
4️⃣ Prescriptive analytics – What should be done?
Prescriptive analytics builds on descriptive, diagnostic, and predictive analytics to provide recommendations on what actions should be taken. By weighing different scenarios and outcomes, the business can make more informed decisions and in some cases automate parts of decision-making.
5️⃣ AI-driven analytics and AI agents – What can be automated?
Here, AI is used not only to analyze data but also to generate insights, recommendations, and in some cases execute actions automatically. Modern AI solutions can identify anomalies, suggest decisions, simulate different scenarios, and support users through natural language. In certain areas, AI agents can even perform parts of the work independently within defined parameters.
Examples of use cases include intelligent planning, fraud detection, advanced forecasting, supply chain optimization, and AI support in business processes.
When AI moves from analysis to action
Developments in AI are simultaneously blurring the boundaries between analysis, recommendations, and action. Where traditional decision support primarily helped the business understand what had happened, next-generation AI solutions increasingly focus on recommending actions, coordinating activities, and automating parts of the work.
This is also the direction development is heading—toward businesses where AI, business systems, and processes collaborate more closely, and where parts of decision-making occur automatically within defined parameters.
Different analytics for different decisions
The five levels should not be seen as a ladder where all organizations must reach the top. Different types of analytics serve different purposes and are suitable for different decision situations. A financial report doesn’t require the same analysis as an advanced forecasting model or an AI agent.
At the same time, the demands on organizations’ ability to move from understanding what happened to predicting, recommending, and in some cases automating decisions are increasing. This is also where many of the greatest business values can be found.
As AI becomes an increasingly integrated part of operations, the demands on data quality, business context, and the ability to translate insights into action also increase. Only then can data become the strategic tool for better decisions that many organizations strive for.
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