Big Data (Generic)

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.