Data Mining

Data mining is the discipline of analytics that exploits advanced methods for extracting insights from large volumes of data by combining methods of statistical modelling and machine learning algorithms.

Nowadays the volume of structured and unstructured data is continuously increasing, and the acquisition of knowledge constitutes a daily challenge in a variety of disciplines and industries. Data mining is the analytics technique which aims at exploring composite data, identifying patterns and obtaining meaningful insights from chaotic patterns of data. Data mining combines statistics and artificial intelligence (AI) with data management to analyse large datasets of business (banking, insurance, retail), science research and government security.

INESIS team has a long experience in the application of data mining process in various fields of business life. the complete data-mining process involves multiple steps, from understanding the goals of a project and the data that are available to implementing process changes based on the final analysis.



Describe historical events

Cluster Analysis

Group similar records together

Outlier detection

Identify unusual or abnormal observations

Mining associations

Detect relationships or casual structures among sets of items


Predict what might happen in future using probabilities

Regression Analysis

Statistical modelling techniques for predicting the future values of a metric based on the values of a specific set of variables that have an impact on the estimated predictions.

Neural networks

Computer programs which are fed with historical data and are gradually trained to learn and make predictions.

Decision trees

Machine learning method that creates models that learn to predict values based on decision rules, which are generated from the features of the data


Predict possible consequences based on different action scenarios and recommend the best possible decision for future actions.

Business Analysis, Data and Text mining are put into force for the application of effective decision making


  • Automated data analysis
  • Reveal hidden patterns
  • Predict feature events


  • Customer Analytics
  • Marketing Analytics
  • Predict customer behaviour
  • Targeted Product campaigns
  • Competitive pricing
  • Risk management
  • Portfolio analysis
  • Credit scoring
  • Fraud detection
  • Improve CRM
  • Sales forecasting
  • Accurate diagnosis/ patient treatment