Our Data Science Approach
We leverage data mining, machine learning, big data, computational statistics, and analytics to extract meaningful and relevant insights from noisy, structured, and unstructured data
Capture
The required or relevant data is usually distributed across the enterprise. This phase focuses on the different aspects of capturing data from various sources
- Data Acquisition
- Data Entry
- Data Extraction
Maintain
Once the data is captured, the maintain phase focuses on storing it for easy access, interactions, and analysis
- Data Warehousing
- Data Cleansing
- Data Staging
- Data Processing
- Data Architecture
Process
In this phase, the data is processed to identify trends and patterns and techniques are applied to find a suitable abstraction of the dataset
- Data Mining
- Clustering
- Classification
- Data Modelling
- Data Summarization
Analyze
Once the data has been classified, and we have modelled it, it needs to be analyzed by leveraging multiple techniques so that an explanation for a specific experience can be derived
- Exploratory/Confirmatory
- Predictive Analysis
- Regression
- Text Mining
- Qualitative Analysis
Report
The final phase deals with the most efficient ways of communicating the summary of the research and analysis using data visualization tools
- Data Reporting
- Data Visualization
- Business Intelligence
- Decision Making
Data Science Applications
Recommendation Engine
Price Optimization
Inventory Management
Targeted Promotions
Analytics
Customer Lifetime Value Prediction
Intelligent Cross-selling & Upselling
Fraud Detection
Labor Optimization
Tools and Technologies
Retail
Insurance
Healthcare
Life Sciences
Investment Banking
Subject Matter Experts
360 degree perspective
Experienced team
Consulting mindset
