Validate your Big Data Business use case before you implement

One of the key best practice for successful implementation of Big Data Analytics solution is to validate the business use case for Big Data. It will help organization with 2 important aspects for successful implementation:

1. Keeping the scope limited to help work with limited data set within Big data context

2. Help measure the success of solution that address key business problem

In case the same data set addresses multiple use cases, organization may need to prioritize their use case and apply iterative and phased approach. It’s the theory of getting biggest bang for the buck, tactical and strategic.

While there are extensive industry specific use cases, here are some of the use cases for handy reference.

Retail/Consumer Use Cases

  • Merchandizing and market basket analysis.
  • Campaign management and customer loyalty programs.
  • Supply-chain management and analytics.
  • Event- and behavior-based targeting.
  • Market and consumer segmentations

Financial Services Use Cases

  • Compliance and regulatory reporting
  • Risk analysis and management
  • Fraud detection and security analytics
  • CRM and customer loyalty programs
  • Credit risk, scoring and analysis
  • High speed Arbitrage trading
  • Trade surveillance
  • Abnormal trading pattern analysis

Web & Digital Media Services Use Cases

  • Large-scale clickstream analytics
  • Ad targeting, analysis, forecasting and optimization
  • Abuse and click-fraud prevention
  • Social graph analysis and profile segmentation
  • Campaign management and loyalty programs

Health & Life Sciences Use Cases

  • Clinical Trials Data Analysis
  • Disease Pattern Analysis
  • Campaign and sales program optimization
  • Patient care quality and program analysis
  • Medical Device and Pharma Supply-chain management
  • Drug discovery and development analysis

Telecommunications Use Cases

  • Revenue assurance and price optimization
  • Customer churn prevention
  • Campaign management and customer loyalty
  • Call Detail Record (CDR) analysis
  • Network performance and optimization
  • Mobile User Location analysis

Government Use Cases

  • Fraud detection
  • Threat detection
  • Cybersecurity
  • Compliance and regulatory analysis

New Application Use Cases

  • Online Dating
  • Social Gaming

Fraud Use-Cases

  • Credit and debit payment card fraud;
  • Deposit account fraud;
  • Technical fraud and bad debt;
  • Healthcare fraud;
  • Medicaid and Medicare fraud;
  • Property and Casualty (P&C) insurance fraud,
  • Workers’ compensation fraud.

E-Commerce Use-Cases

  • Cross-Channel Analytics
  • Event Analytics
  • Recommendation Engines using Predictive Analytics
  • Right Offer at the Right time
  • Next Best Offer or Next Best Action (Ebay, Netflix, Amazon and Others)

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