What is Big Data & Whats the Big Deal? What is Microsofts approach to Big Data? Customer Scenarios Summary

What is Big Data?

Data that you think is valuable, but processing and storing it may not have been traditionally practical or affordable Data that you need to ask questions to learn what questions to ask Data that is produced at fire hose rate

Cloud

Industry/VerticalScenarios Financial Services Modeling True Risk Threat Analysis Fraud Detection Trade Surveillance Credit scoring and analysis Web & E-Tailing Recommendation Engines Ad Targeting Search Quality Abuse and click fraud detection Retail Point of Sales Transaction Analysis Customer Churn Analysis Sentiment Analysis Telecommunications Customer Churn Prevention Network Performance optimization Call Detail Record (CDR) Analysis Analyzing Network to Predict Failure Government Fraud Detection and Cyber Security General (Cross Vertical) ETL & Processing Engine

The Microsoft Big Data Strategy

Microsoft and Big Data Volume Variety Velocity Relational Data Connecting to the Worlds Data with Azure Marketplace Insights for Everyone with Microsoft BI Enterprise Ready with Hadoop on Windows & Azure

Keep paces with the Formula One demands Faster split-second decisions Faster adjustment to new car conditions Lower TCO on operations 12 Terabyte of data used to simulate 90 race and test runs Loading data extremely fast into the PDW Comparing race track situations over the years in seconds Needed very fast adoption to new race track situations Required fastest data load capabilities Over 100 sensors produce 4 MB per sensor/second data McLaren Electronic analyses 400 billion records in less than 13 seconds with SQL Server Parallel Data Warehouse The SQL Server Parallel Data Warehouse technology allows for new kinds of historical and specific analysis of huge data sets in real time at affordable price. Peter van Manen, Managing Director, McLaren Electronic Systems

Big Data: How is it Different? Big Data Often Operates On Raw Sources To Deliver Value More Quickly Brian Hopkins and Boris Evelson. (September 2011)

The Hadoop Ecosystem (simplified)

NON RELATIONAL DATA MANAGEMENT SHARE AND GOVERN DISCOVER AND RECOMMEND TRANSFORM AND CLEAN ANALYTICS DATA ENRICHMENT OPERATIONAL SELF-SERVICEMOBILE PREDICTIVE REAL-TIME COLLABORATIVE MARKETPLACE External Data and Services RELATIONALMULTIDIMENSIONALSTREAMING

KEY FEATURESKEY FEATURES BENEFITS

BENEFITS

BENEFITS

INSIGHTS FOR ALL USERS THROUGH FAMILIAR TOOLS PB TB GB

KEY FEATURESKEY FEATURES BENEFITS

Big Data Customer Scenarios

24 Big Data Customer Scenario Hospital with Massive Streams of Monitoring Data Monitoring Radiology Constant monitoring of vital signs Testing to maintain wellness Communicable disease tracking Interdisciplinary team notes Financial Exponential amounts of data from different devices Delivery Transformation Public Health Health Back Office Care Management Health Information Electronic Medical Records Healthcare data processing Variety of data Clinical decision support systems enhance the efficiency and quality of operations Transparent medical data enhances customer value by sharing performance data Comparative effectiveness research helps determine the best treatment for specific patients Advanced analytics identifies patients who would benefit from proactive care or lifestyle changes Benefits to healthcare firms Doctors and nurses use different devices to update patients information in a database Monitoring Radiology Constant monitoring of vital signs Testing to maintain wellness Communicable disease tracking Interdisciplinary team notes Financial

25 Big Data Customer Scenario Credit Card Company Retains Customers by Understanding Social Relationships B C Credit card imprinting machines Credit card terminals Mobile devices Swipe hardware Virtual terminals Online payment processors Payment-enabled software Point-of-sale solutions Credit card transaction processing Issuing Bank/ Credit Card Association Merchant Account Provider Clearing Houses Merchant Bank A B C Better customer segmentation More targeted retention campaigns Better risk assessment with more granular data More accurate fraud detection Customer A uses her credit card in a variety of methods Benefits to financial services firm Credit card company retains customer A and targets her socially connected relations, B and C A Customer A is socially related to customer B and C Variable amount of data Credit card imprinting machines Credit card terminals Mobile devices Swipe hardware Virtual terminals Online payment processors Payment-enabled software Point-of-sale solutions

26 Big Data Customer Scenario Public Utility Performs Sophisticated Analysis on Smart Grid Data Customer uses electricity in a variety of ways, generating large amounts of smart grid data Large volume of high speed data Improved efficiency and power savings Smarter capacity planning Smarter pricing Better matching of power supply and demand Analyzes records of financial transactions to build patterns that can detect criminal activity or faulty equipment Customers using public utilityBenefits to public utility firms Billing Interval Detailed Consumption Data Aggregate Statistical Data Load Adjustment Decentralized Power Generation Price Signaling to Consumers Smart grid processing Customer uses electricity in a variety of ways, generating large amounts of smart grid data

Excess Data Logs ETL Some Data Data Warehouse

Raw Data Store it All Cluster Raw Data Store it All Cluster Data Warehouse Logs

LEVEAGING SOCIAL MEDIA

Submit changes back to Apache Foundation Just works on Windows Azure and Server Integration with Visual Studio, Javascript, Excel, etc. Performance, Scale, High Availability - Hadoop Connector for SQL Server and PDW Management, Ease of use Security, Data Governance Integration with AD and SC Integrate as part of our overall data platform

LEARN MORE Microsoft Big Data Solution: Windows Azure: TRY NOW Preview of the Hadoop-based service for Windows Azure: