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12 December 2025 | Posted by angela.tuduri

Data to decision: how does a predictive model work?

Did you know that historical data can be transformed into predictions using machine learning models?

Data by itself is not very useful (if at all). Its true value comes when that data is analyzed and converted into predictions that guide real decisions. This process—called predictive analytics—allows us to anticipate behaviors, mitigate risks, optimize resources, and plan based on probable scenarios.  

What is a predictive analytics? 

Predictive analytics involves studying current and historical data to forecast possible future scenarios. Its goal is not only to describe what happened or why it happened, but to anticipate what is likely to happen, based on patterns and relationships present in the data.  

Unlike descriptive analysis (what happened) or diagnostic analysis (why it happened), predictive analysis goes one step further: it seeks to answer “what could happen.”  

How a real prediction model works  

The cycle: data to decision  

Transforming data into decisions involves several key stages:  

1. Collect and prepare the data  

Data usually comes from multiple sources: databases, ERP systems, spreadsheets, transactional records, etc. It must be cleaned, unified, and structured in a common repository (data warehouse/data lake).  

2. Train the model  

Using statistical techniques, machine learning, or even deep learning, a model is trained on historical data. This model learns patterns that allow it to estimate the probability of future outcomes.  

3. Validate and deploy  

Once the model has been trained, it must be validated with test data to check its accuracy. If the results are good, it is integrated into business systems to manage predictions that aid decision-making.  

4. Monitor and update  

A predictive model is not static: as new data is generated or the environment changes, it is advisable to recycle the model, retrain it, and adjust it to maintain its relevance and accuracy.  

Common predictive modeling techniques  

Among the most widely used techniques in predictive analytics are:  

  • Decision trees: these allow decisions or outcomes to be predicted based on a sequence of binary (yes/no) questions.  

  • Regression (linear or logistic): used to model relationships between independent and dependent variables, or to classify events according to probabilities.  

  • Time series analysis: when data is sequential in nature, techniques such as ARIMA are used or, in more advanced contexts, recurrent neural networks or memory models. 

  • Neural networks/deep learning: when data is complex or high-dimensional (images, voice, multiple variables), these techniques can capture non-obvious relationships and offer more accurate predictions.  

Real-world applications: from finance to maintenance  

Predictive analytics is used in multiple sectors:  

  • In finance, to assess credit risks before approving loans, detect fraud, or anticipate market behavior.  

  • In retail, to forecast demand, optimize inventory, and personalize marketing campaigns.  

  • In manufacturing, to plan purchases, optimize logistics, predict machinery failures, and schedule preventive maintenance.  

  • In healthcare, to anticipate disease risks, plan resources, optimize treatments, or detect anomalies in patient monitoring.  

Key benefits  

  • Reduced risk in decision-making, supported by data and probabilities.  

  • Improved operational efficiency and forecasting of needs (stock, personnel, maintenance, etc.).  

  • Personalization of services/products and greater customer satisfaction thanks to behavior predictions.  

Constraints and challenges  

  • The quality of the model depends directly on the quality and quantity of available data. Biased, incomplete, or unrepresentative data can distort predictions.  

  • Models must be reviewed and updated as business or environmental conditions change, which requires technical and organizational effort.  

  • There are no absolute guarantees: a predictive model offers probable scenarios, not certainties. It should be used to support decisions, not as a substitute for human judgment.  

A real prediction model is much more than statistics or graphs: it is a strategic tool that transforms data into decisions. If you want to learn more and train in a rapidly growing sector, discover La Salle Campus Barcelona. 

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