Data Mining

Data Mining is the process of discovery and interpretation that uncovers meaningful patterns in raw data, patterns that can be used to address challenging business questions requiring prediction and inference. Data mining helps to reveal previously unknown valuable customer information that can strengthen a company’s decision-making process and empower it to identify new business opportunities.

Bell.One™ provides a comprehensive and robust portfolio of data mining services that can help companies across a wide variety of industries such as finance, banking, marketing, retail manufacturing and tax governing bodies gather critical information from reliable sources, get up-to-date customer data and business trends as well as streamline operations, gain valuable insights into customer and business trends, and utilize the information to target the right set of customers and boost the company’s performance.

Key Features

  • Data mining methods are suitable for large data sets and can be more readily automated.
  • The data mining methods, used for extracting hidden patterns from data, are classified into the following two categories: description methods and prediction methods.
    • Description methods are oriented towards data interpretation, which focuses on understanding (by visualization for example) the way the underlying data relates to its parts.
    • Prediction-oriented methods aim to automatically build a behavioral model, which obtains new and unseen samples and is able to predict values of one or more variables related to the sample.
  • Data mining analyzes the data by applying a wide variety of techniques, developed for the efficient handling of large volumes of raw data.

How it works


  • In finance and banking, data mining is used to create accurate risk models for loans and mortgages. They are also very helpful when detecting fraudulent transactions.
  • In marketing, data mining techniques are used to improve conversions, identify target groups, increase customer satisfaction, build customer profiles and create targeted advertising campaigns.
  • Retail stores use customer shopping habits to optimize the layout of their stores in order to improve customer experience and increase profits.
  • Tax governing bodies use data mining techniques to detect fraudulent transactions and single out suspicious tax returns or other business documents.
  • In manufacturing, data discovery is used to improve product safety, usability and comfort.