At a glance
- One of four main categories of data analysis: descriptive, diagnostic, predictive, and prescriptive analytics.
- Uses methods such as regression analysis, decision trees, and neural networks.
- AI and machine learning technologies increase prediction accuracy and model flexibility.
- Reduces risks, optimizes resources, and increases organizational agility.
- Used in controlling, marketing, financial planning, supply chain management, and healthcare.
What would you like to know about Predictive analytics?
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Definition of predictive analytics
Predictive analytics refers to the application of statistical models, algorithms, and modern data analysis techniques such as data mining and machine learning to predict future events or behavioral patterns. Historical and current data are analyzed to identify trends that can be projected into the future. The aim is therefore to create data-driven forecasts that support informed decisions and strategic measures.
Predictive analytics uses large volumes of data, often originating from big data systems and continuously refined through technological advances in machine learning and artificial intelligence. Algorithms and models are used to identify patterns and relationships in large datasets and generate precise predictions.
| Type of analysis | Question | Example |
|---|---|---|
| Descriptive Analytics | What happened? | Sales development in the last quarter |
| Diagnostic Analytics | Why did it happen? | Causes of a decline in sales |
| Predictive Analytics | What will happen? | Sales forecast for the next quarter |
| Prescriptive Analytics | What should we do? | Recommended measures to achieve a target |
„We integrate our forecasts directly into ongoing reporting, allowing us to identify and assess developments earlier. The suggested values for planning and forecasting noticeably save us coordination effort in day-to-day work.“Head of Controlling
Industrial company
Why is predictive analytics important? – Relevance and benefits of predictive analytics
Predictive analytics is important because it enables companies to predict future trends more accurately and act strategically. A key benefit of using Predictive Analytics is its ability to automatically analyze complex relationships and derive targeted recommendations for action. Predictive analytics enables companies to reduce costs, make better-informed decisions, reduce risks, and capitalize on opportunities more effectively. Ultimately, forecasting future developments creates competitive advantages and increases efficiency.
Depending on the area of application, even more specific benefits can be achieved. Predictive analytics is increasingly being used in areas such as marketing, risk management, and supply chain optimization. This enables companies to respond agilely to market changes, proactively minimize risks, and optimize supply chains.
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Is predictive analytics artificial intelligence?
Predictive analytics can use statistical methods such as regression analysis or time-series models that do not necessarily require artificial intelligence (AI). However, AI and machine learning are particularly valuable for predictive analytics when analyzing large volumes of data and complex patterns. AI algorithms such as neural networks can be used to increase the accuracy and flexibility of predictions.
Predictive analytics and AI can therefore be used in combination, but they are not necessarily connected. Predictive analytics is an overarching concept for generating forecasts and does not necessarily have to involve AI. However, when machine learning and AI algorithms are used, this is referred to as an ai-powered predictive analytics solution.
| Aspect | Conventional predictive analytics | AI-powered predictive analytics |
|---|---|---|
| Methods | Statistical methods: regression, time-series models | Machine Learning, neural networks, Deep Learning |
| Data volume | Also works with smaller datasets | Particularly powerful with large volumes of data |
| Pattern recognition | Linear and known relationships | Also non-linear, complex patterns |
| Learning capability | Static model | Continuous improvement through autonomous learning |
| Interpretability | Generally easy to understand | Limited depending on the model (black box) |
An AI-powered predictive analytics solution offers benefits such as the continuous improvement of forecasting models through autonomous learning and the ability to identify non-linear relationships in real time. This enables companies to generate more precise predictions and gain deeper insights into market trends and customer behavior. Predictive analytics therefore does not necessarily require AI, but becomes significantly more powerful through the integration of AI technologies and enables dynamic, data-driven decisions.
How does predictive analytics work? – Predictive analytics process, methods, and techniques
Predictive analytics is based on a variety of methods for forecasting future developments and events based on historical data. Different analytical techniques and model approaches can be distinguished:
Main types of predictive models in predictive analytics
- Classification models predict categorical values (e.g., customer profiles).
- Regression models predict continuous values (e.g., expected sales).
Analytical techniques
- Regression analysis estimates relationships between variables and is ideal for continuous data such as sales forecasts.
- Decision trees are simple, intuitive models for classification and visualize decision paths as “branches” and possible outcomes as “leaves.”
- Neural networks are particularly suitable for recognizing complex patterns in large and non-linear datasets and are useful for analyzing difficult-to-predict data patterns.
How can predictive analytics models perform optimally?
To use predictive analytics models efficiently, several factors should be considered that can contribute to greater success:
Use cases: where is predictive analytics used?
Predictive analytics is used in many industries and areas, including:
Where would you like to use Predictive analytics?
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Bissantz and predictive analytics
Bissantz does not use predictive analytics as an isolated technology, but as an integral part of an analytically grounded decision-making process – always with the goal of making predictions understandable and translating them into operational management.
With the business intelligence software DeltaMaster, companies can seamlessly integrate predictive analytics models into existing reporting and analysis processes. The focus is not on algorithms for their own sake, but on the interpretation and practical use of results by business users – without requiring prior data science knowledge.
Specific predictive analytics functions in DeltaMaster:
| Function | Benefit |
|---|---|
| Decision intelligence | Automatic suggestions for effective measures |
| AI-powered planning proposals | Suggested values for planning and forecasting based on patterns in historical data |
| Visualization of forecasts | Typographically scaled numbers, sparklines, and Bissantz Numbers make forecasts immediately understandable |
| Integration into reporting | Forecasts are displayed directly within the reporting context |
„With DeltaMaster, our predictions are presented in a way that is understandable for the business departments, without us needing data science skills. This means forecasts actually feed into our management decisions.“BI Manager
Retail company
Frequently asked questions
What is predictive analytics in simple terms?
It means learning from past data to predict future developments. Similar to an experienced controller who recognizes from years of sales data that december is traditionally strong and february is weak – only predictive analytics identifies these patterns automatically, across much larger datasets and with statistical precision, and translates them into concrete forecasts.
What is the difference between predictive analytics and forecasting?
Forecasting is a specific method within predictive analytics – typically the forecasting of time series such as sales or demand. Predictive analytics is the broader term and includes forecasting as well as classification, segmentation, risk modeling, and other prediction tasks.
What is the difference between predictive and prescriptive analytics?
Predictive analytics forecasts what will happen. Prescriptive analytics goes one step further and recommends what should be done – deriving concrete courses of action from predictions. In practice, prescriptive analytics systems build on predictive analytics models and supplement them with optimization logic and decision rules.
How does Bissantz support predictive analytics in controlling?
Bissantz supports predictive analytics in controlling with AI-powered forecasts, driver-based predictions, and scenario analyses. This allows you to identify future developments at an early stage, evaluate courses of action and planning assumptions, and make informed decisions based on reliable forecasts.
Summary
Predictive analytics is a key tool for modern business management: it enables data-driven foresight instead of reactive action – creating the basis for informed decisions. Whether using conventional regression models or ai-powered neural networks, the value of predictive analytics does not come from the complexity of the models, but from the quality of the data, the comprehensibility of the results, and their consistent integration into operational management processes. Bissantz implements this principle in a user-friendly way – with forecasts that are not only calculated, but also understood and put to use.