An organization using business analytics tools and solutions can answer questions about past and current events and make better decisions for its future. Its capabilities and insights may still be limited, however, and it may be missing critical insights into its data. Advanced analytical tools and data mining techniques can help dive deeper into an organization’s data.

Business analytics implementations within an organization can greatly increase value as the solutions mature and more sophisticated tools are applied. A recent study by Nucleus Research found that the average return on investment (ROI) ranged from 188% for improvements in the traditional business analytics capabilities to 1,209% for more advanced use of predictive analytics and data mining technologies.


Neubrain’s solution provides high value advanced analytics capabilities for your specific needs and challenges. Our solution drives productivity through flexible, efficient, and scalable algorithms. It is designed to integrate with heterogeneous data sources and handle various data types to support many data mining methods and tasks:

  • Description – showing patterns or trends in data and offering possible explanations
  • Classification – finding a model or function that describes and distinguishes data classes or concepts to predict group membership for data instances; popular classification techniques include decision trees and neural networks
  • Estimation – building models from complex and accurate data sets; similar to classification, except target variables are numeric (See also: Driver Based Planning and Cost Modeling)
  • Prediction – devising models or functions to classify and estimate future results
  • Clustering – grouping data into similar segments or objects based on locality and multi-variant relationships; most commonly used for segmentation, where similarity of records in clusters is maximized, and similarity to records outside clusters is minimized
  • Association Rules – determining which attributes “go together” and setting rules based on relationships between characteristics; a common use is the market basket analysis to determine which items in a store are likely to be purchased together

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