Solutions



  • Full Stack Development
  • Big Data Engineering
  • Big Data Application Development
  • Analytics Integration
  • Customer Analytics
    • Customer Behavior /Segmentation
    • Customer Life Time Value (CLTV) and Loyalty
  • Marketing Analytics
    • Segmentation and Targeting
    • Marketing Channel mix modeling and Optimization
    • Product-Mix Optimization and Profitability Analysis
    • Campaign Effectiveness
    • Price & Promotion Strategy
    • Cross-sell/Up-sell
  • Risk Analytics
    • Credit Scoring
    • Price Elasticity/Optimization
    • Fraud Detection
    • Claim Frequency/Severity Modeling
    • Collection and Recovery Analysis
  • Operational Analytics
    • Demand Sales Forecasting
    • Inventory Management
    • Stock Replenishment Analysis
  • Web Analytics
    • Traffic Analysis & Conversion Analysis
    • Visitor Segmentation
    • User Personalization
    • Multi-Channel Campaign Optimization

In a matured DW environments, the goal of "single version of truth" is lost due to spaghetti ETLs and multitude of Data Sources and sinks. Not only does it result in availability of the "right" data at the "right" time, but also Operational Data & Business Rule Standardization. Uncluttered semantic layer helps in lower debugging time and result in effective Data Audit and Control processes.

On the Infrastructure side, this service helps in lowering ETL windows by lower computing window for processing throughh elimination of redundant data. This will improved Scalability, Reliability and Performance of Information Management Architecture and helps iccorporate new requirements at lower cost.


Poor Report or BI Asset Phase out strategy will lead to sub-optimal IM Architecture. With the advent of Big Data and sophisticated Analytical techniques, it become imperative that the Organizations shall retire or remove or replace unused/under-utilized/under performing assets


Data mart consolidation extends along a spectrum of alternatives. The alternatives extend from

  • Simply locating the distinct data mart servers (infrastructure) in the same physical location — collocation,
  • To being on the same hardware within a different physical or logical partition,
  • To being within in a different database on the same server,
  • To being within the same database and on the same backup and recovery schedule, and finally
  • To being an integrated data design within the same data model and application implementation.


This range of interrelated options results in an interesting dynamic, especially in those instances where data marts have been implemented with a consistent design for intelligent information integration and interoperation. If the design of the data marts is consistent and unified, the result is a federated subsystem in which communication and meaningful joining of the marts across consistent customer, product, market, etc. dimensions is feasible. If the enterprise has actively exploited this feasibility (a result that is by no means always the case), then the benefits of data mart consolidation are reduced to data center consolidation as a special case, as indicated.