Data Analytics

Data Analytics

Data Analytics

As data becomes the currency of the digital world, constraints like confidentiality, ownership, volume and quality of data are preventing companies from turning data into actionable insights. The paradigm of Edge analytics, real-time processing capabilities and advancements in AI/ML is helping companies leverage analytics for maximum business impact.  Analytics is delivering company growth through key insights that leads to new product creation and enhanced customer experiences.

Key Imperatives For Analytics Enablement

We’re helping deliver:

Technology Platform: The right platform for data ingestion, data integration, data transformation and data storage considering cost constraints, data volume, and data growth considerations

Accurate Analytics Models: Measured insights into Machine Learning (Supervised, Unsupervised or Deep Learning), Vision-based analytics or Natural Language Processing (NLP) by considering the data characteristics in terms of volume and quality and ensuring the required amount of accuracy

Prioritization For Analytics Engagement: Identification and prioritization of business problems by working with function heads/business users of the customer while spotting trends and new business growth opportunities

Actionable Data Insights: Actionable insights/visualization from a data set by leveraging licensed data visualization/data discovery tools. We consider data volume, quality and regulatory compliance during each analytical engagement including platform selection, analytical model design, data visualization and business problem selection

Business Impact


  • Information Management
    • Information Architecture Strategy and Key value drivers
    • Data Quality, Data Integration, ETL, Data Aggregation
    • Data Warehousing, Data modeling, Database capacity planning, Performance tuning
  • Big Data Services
    • Evaluation of existing enterprise system & gather data requirements
    • Interfacing with business and IT and understand functional, technology requirements
    • Evaluation & selection of big data distributions (Cloudera, Hortonworks, MapR)
    • Select Architecture Paradigm (Cloud-Native vs. Cloud Agnostic, Lambda vs Kappa)
    • Scope determination for a pilot, full project
    • Create reference architecture
  • Big Data Platform Development
    • Proof Of Concept for Data Lake/Big Data Platform
    • Define success criteria for PoC
    • Testing of POC in real/simulated environments
    • Batch migration of data from existing data sources to data lake/big data platforms
    • Real-time/Stream Processing of data
    • Data Lineage , archival and business knowledge enrichment services on Data lake
  • Data Discovery
    • Exploratory Data Analysis
    • Deriving Trends and Patterns from Raw data
    • Visualization and analysis of data
    • Decision story board
    • Pre-created reports & self-service portals
  • AI & ML
    • Data preparation, Feature engineering, Model training & Validation, Model maintenance, Hypothesis testing
    • Advanced analytics models for optimization, anomaly detection, predictive maintenance, ,
    • Deep Learning (CNN, LSTM, Boltzmann machines); Supervised (Random forests, Gradient Boosted Trees); Unsupervised (k-means, PCA, SVD)
    • Natural language processing/ Text mining; Vision/ Image analytics

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