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Data mining (also known as predictive analysis or knowledge discovery from databases) can almost literally be seen as digging up information from data. Without data mining, analysis is restricted to a small number of aspects that are expected to be important. Data mining vastly expands that small analysis scope. Users are able to analyze very large data sets to find all hidden patterns and complex relations by using artificial intelligence, statistical techniques and visualization methods.

data mining

Applying data mining to large data sets, such as customers, transactions and events, enables the user to quickly reach deep insight and build models that recognize characteristics and predict individual behavior. This optimized information position results in better decision-making and more productivity on strategic, tactical and operational levels.

Some examples:

  • Predicting customer interest in certain products, in order to guide product launches and advertizing campaigns.
  • Recognizing possible fraud in financial transactions.
  • Discovering place/time combinations that show a higher risk of criminal behavior, in order to plan more effective preventive actions.
  • Finding explanations for differences in sales, not based on one or two parameters but on the entire set of available data.
  • Finding interesting niches within a target group, in order to develop personalized customer approaches.

Sentient data mining

Sentient makes data mining accessible for everyone with DataDetective, by embedding this complex technology in one single user-friendly analysis environment. Within this environment, it is possible to explore the data in a task-oriented way and to build machine learning models. With Sentient’s unique technology, models can use all available data instead of just a few aspects, for example, the model can include everything that is known about a person (including their personal history), instead of just age and gender.