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Big Data (Generic)

adjusted   The Big Data solution consists of an Azure Hadoop port and/or Microsoft SQL Server PDW. Big Data adoption is usually triggered by:
  • Very large volumes of data. These usually originate in either financial data (transaction level), device data (IoT) or Social media data (twitter, facebook etc).
  • Diverse content formats. Most of the organization's knowledge base is unstructured data like videos, word docs etc. Big Data technology is required to unlock these.
  • High velocity data. Conventional BI, especially in the Microsoft stack, relied on offline batch processed to crunch numbers. With rapidly changing data, the need for real-time analytics through big data has become apparent.
Key Features:
  • Ability to process a massive amount of data
  • Ability to process unstructured data
  • Ability to high velocity (frequently changing data
  • Cross reference data with existing operational and data warehouse data.
  • Rich, self-service BI for customers
  • On demand server usage. If you analyze once a day for 30 minutes, you pay for 30 minutes.
  Common usage scenarios
  • Social media sentiment analysis: Analyze and engage customers in social media when they have something negative to say about your product
  • Predictive analytics: Discovering un-obvious patterns from data for e.g. Monitoring device data can tell you it is about to break down before it does.
  • Deeper customer insights: Retailers can spot purchasing patterns based on demographic information, purchasing history, environmental factors etc.
  • Government and Politics: Deep insight into constituency issues, demographic, political inclinations etc.
  • Healthcare: Enhancing and automating disease diagnosis. Infectious disease propogation. Monitoring patients at home, doctor performance measurement.

  • TECHNOLOGIES

  • RELATED LINKS

  • ON BLOG

  • Questions?

Explore Other Examples

Explore how IT can help your business by

BUSINESS BENEFITS

  • Sales and Marketing improvements through better customer insight
  • Better product quality for manufacturing concerns
  • Fewer machine outage
  • Fine grained observation.

CASE STUDIES

  • Foribus
  • BMW