Information Explosion

Reality: New Machine-Generated DataNon-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

“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!

Analytic Landscape – Data Centric Approach

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

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

Common Approaches Used Till Date for Achieving Database Performance

Co

lum

n

MPP Buffer ManagerDecompress

Optimizations for parallel processing and minimal data retrieval

Column store with compression Proprietary hardwareParallel Processing

RAMDisk

Data Warehouse Appliances

Acceptable performance has been achieved by using more hardwareor 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

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=856714

Gartner Reveals Five Business Intelligence Predictions for 2009 and Beyond

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

Data representations Information graphicsData representation techniques and toolsVisual representation – trends and best practices Interactivity in data representationTools and applicationsThe user perspective on information presentation

http://www.smashingmagazine.com/2007/08/02/data-visualization-modern-approaches/

Data Visualization

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