Case Studies

Case-Studies: we develop and deploy our solution blueprints around five focal areas that drive near term results and establish longer term viable roadmaps

Data Discovery and Harvesting:
Explore, Assess and Leverage . . .

Key challenges:

  • Scattered data across silo systems
  • Lack of correlation of ‘raw’ from N sources
  • Multiple complex data ingestion pipelines

Capabilities needed:

  • Catalog your most critical datasets
  • Navigate via Metadata & Search
  • Collaborate for data transformations
  • Deliver high value datasets to Analytics

Customer 360 View:
Understand your Customer

Key challenges:

  • Correlate internal & external sources
  • Buying: what, when, why, how, where?
  • Point-of-Impact: pro-active cross selling

Capabilities needed:

  • Unified view of customer & segments
  • Micro-targeting based on buy profiles
  • Competitive pricing & timely logistics
  • Interactive & Contextual visualization

Operational Analytics:
Visibility via Machine Learning

Key challenges:

  • Large volume of machine data is discarded
  • Complexity in accessing & searching data
  • Difficulty in Root-Cause Analysis & alerting

Capabilities needed:

  • Reliable & high speed capture of data flow
  • Isolation of anomalies from data flows
  • Classifications into predictive models
  • Feedback into Operational Systems
Call To Action

Transform legacy batch Data Warehouse to self-help Analytic Data Stores for business decision makers:

Key challenges:

  • Difficulty in moving ALL your data into central DW repository
  • Large volume of ‘cold’ or ‘low-touch’ data with low usage
  • Long time-to-deliver cycles and high costs from central DW

Capabilities needed:

  • Establish Hadoop as a Landing Zone for pre-processing &
  • Combining datasets based to deliver queryable datastores
  • Mixed latency (batch, near-real time & real-time) Analytics
  • Processed data distribution selectively to downstream Apps.

Architecting and Operating BigData Platform as a foundation for compelling Business Applications:

Key challenges:

  • Establishing proven architectures as BigData Systems
  • Lack of skilled Design, Deployment & Operational resources
  • High cost of Commercial Vendor solutions vs. Open Source

Capabilities needed:

  • Continual ability to innovate via rapid Proof-of-Concepts
  • Best practices for Data Quality, Metadata, Lineage Mgmt.
  • Information Security, Access & Audit for Fraud / Risk mgmt.
  • Selective adoption of Open-Source innovations
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