1. Information Explosion

2. Reality: New Machine-Generated Data Non-relational and relational data outside of the EDW † Source: Analytics Platforms – Beyond the Traditional Data Warehouse, Survey of 223 companies. BeyeNetwork 2010 Data Types Outside of the Enterprise Data Warehouse 53% of Companies Struggle Analyzing Data Types Not in the Traditional Data Warehouse

3. “Data is widely available; what is scarce is the ability to extract wisdom from it.” Hal Varian, Chief Economist, Google Strategic Opportunity The Unmet Need!

4. Analytic Landscape – Data Centric Approach

5. The Challenges in Today’s Data Warehousing Environments Sources of data and the amount of data to analyze is growing exponentially Stale data exists because DW solutions cannot ingest the vast amounts of data fast enough Lack of performance for advanced analytics and complex queries The number of users and the concurrency of users is increasing rapidly

6. Considerations of DW Solution Easily scales to analyze the growing amounts of data Rapidly ingests large amounts of data from sources Provides high performance in database analytics Supports high user concurrency securely, reliably Handle multiple workloads

7. Common Approaches Used Till Date for Achieving Database Performance Column MPP Buffer Manager Decompress Optimizations for parallel processing and minimal data retrieval Column store with compressionProprietary hardware Parallel Processing RAM Disk Data Warehouse Appliances Acceptable performance has been achieved by using more hardware or by intelligently lowering the volume of data to be processed. However, none of these approaches leverages the performance features of today’s CPUs i.e. taking the most out of each modern commodity CPU

8.  Through 2012, more than 35 % of the top 5,000 global companies will regularly fail to make insightful decisions about significant changes in their business and markets  By 2012, business units will control at least 40% of the total budget for BI  By 2010, 20% of organizations will have an industry-specific analytic application delivered via software as a service (SaaS) as a standard component of their BI portfolio  In 2009, collaborative decision making will emerge as a new product category that combines social software with BI Platform capabilities  By 2012, one-third of analytic applications applied to business processes will be delivered through coarse-grained application mashups Gartner Research, Jan 2009, http://www.gartner.com/it/page.jsp?id=856714http://www.gartner.com/it/page.jsp?id=856714 Gartner Reveals Five Business Intelligence Predictions for 2009 and Beyond

9. Data mining: the extraction of predictive information from large databases. Data trend, connection and behavior pattern analysis Data quality Data mining tools Predictive and business analytics Descriptive and decision models Statistical techniques and algorithms Data Mining

10.  Data representations  Information graphics  Data representation techniques and tools  Visual representation – trends and best practices  Interactivity in data representation  Tools and applications  The user perspective on information presentation http://www.smashingmagazine.com/2007/08/02/data-visualization-modern-approaches/ Data Visualization

11. Questions?

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