Companies are facing a great effort to remain competitive in the current market complex. All organizations must equip themselves with intelligent tools to improve their performance and achieve business goals faster.

Intelligent Data Processing (IDP) refers to all solutions that exploit artificial intelligence algorithms on structured and unstructured data, with the purpose of extracting useful information and initiating strategic actions.

It is interesting to note that it is an important sector, just think that only in Italy in 2022 it reached the value of 500 million Euro, +32% compared to 2021.

But how does the IDP work?

Generally, this procedure is divided into several stages:

  • Pattern Discovery
  • Predictive Analysis
  • Fraud/Anomaly Detection
  • Monitoring&Control
  • Optimization System

1. Pattern Discovery

The activity of Pattern Discovery consists in the analysis and identification of rules within deconstructed data with the aim of tracing the classification.

You can apply this in different areas, from marketing to warehouse management, from software engineering to text analysis.

In addition, it is important to stress the importance of identifying the regularities and correlations in datasets being the basis for all Data Mining and Clustering tasks.

2. Predictive Analysis

The Predictive Analysis concerns the analysis of data with the aim of providing forecasts on the future trend of the phenomenon studied.

This type of activity is directly linked to the expansion of Big Data and Data Science, in fact they use techniques of data modeling, Machine Learning and Deep Leaning.

Setting the various steps optimally is crucial for the success of the operation; you must proceed as follows:

  • to highlight the problem to be solved;
  • to acquire, organize and process data strategically and effectively;
  • to select techniques and tools to develop corrective predictive models.

3. Fraud/Anomaly Detection

Fraud/Anomaly Detection is the recognition of elements, events or observations that do not conform to a predicted model.

This operation is proving increasingly important, especially in IT security contexts, analysis of customer behavior and control of business processes. Being able to detect anomalies plays a strategic role to avoid crises and/or problems within organizations.

Regardless of the technique used, Anomaly Detection algorithms detect deviations in two modes:

  • Assigning a score: a score is assigned to each data with respect to its degree of abnormality.
  • Binary classification: each data is assigned a binary label that classifies it as normal or anomalous.

4. Monitoring&Control

Monitoring&Control is about data analysis with the aim of monitoring the status of the system and making any changes in real time.

This activity can be described in terms of internal business strategies and procedures that must be chosen and approved by the entire work team.

Thanks to an updated verification plan of all the significant aspects of the process, it is possible to intervene in advance, solving critical issues before they become blocking. In addition, a healthy system is essential to ensure the safety and integrity of data.

5. Optimization System

Closely related to the activity described above, the Optimization System analyzes the data to determine possible future scenarios and to support in decisions and actions to be taken.

To optimize processes, it is necessary to use Artificial Intelligence tools that allow hypothetical situations to be realized, reducing uncertainty and uncertainty.

The advantages of Optimization are related to the speed of reaction, the flexibility and the decrease of the margins of error and inefficiency.


We at Pragma Etimos have repeatedly stressed the importance of making decisions based on real data by setting correct and effective strategies. Having a functional database has become a necessity and not an option in a dynamic market that requires more and more speed, flexibility and competitiveness.


Article written by Gaetano Lo Presti – Digital Philosopher

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