Our analytics practice started with a goal of providing analytical solutions to clients who may:

  • be in the early stages of analytics maturity
  • not have the required infrastructure to support such efforts
  • have budget limitations to spend on costly analytics talent

Algorithmic approaches to extract patterns, insights, and value from data in a business decision-making context is essential in today's data-driven world. By leveraging an end-to-end pipeline of data, modeling, decision-making, and deployment, businesses can unlock hidden insights and drive clear and measurable impact on revenue and cost. Our goal as data scientists is to deliver actionable and measurable business value by integrating and applying these approaches in specific business contexts.

Applications of DS:

  • Predictive Intelligence:
  • Predictive analytics is a data analytics method that utilizes structured and unstructured data to forecast future outcomes and behaviors, such as predicting trends, customer behavior, and fraud risk.

  • Forecasting:
  • This refers to using analytics to drive better decision-making in operations and planning, such as forecasting demand and shipment, planning costs, and analyzing sales data at a granular level.

  • Machine Learning:
  • New advanced algorithms are utilized to extract insights from complex and large data, including real-time bidding, IoT sensor data-based maintenance, and recommender systems.

  • NLP and Text:
  • To analyze unstructured text data and derive meaningful insights from it, such as conversation themes, topics, sentiment, sales leads, customer satisfaction, and more.

  • Optimization:
  • Help organizations make the best decisions in order to achieve the optimal revenue, margin, or cost in a given decision-making framework, such as maximizing marketing reach, resource optimization, or inventory management.

Solution Engineering:

Data science problems can typically be broken down into three stages: problem definition and data exploration, model development and validation, and the application of insights to business decisions. Our frameworks help approach these stages systematically to deliver measurable business value.

  • Problem Formulation
  • Data Cleaning
  • Data Exploration

  • Feature Creation
  • Algorithm Design
  • Performance Assessment

  • Information Extraction and Reporting
  • Executive Reporting and Visualization
  • Performance Monitoring and Maintenance