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Data Warehouse Technology and Platforms - White Papers

See the most recent data warehouse technology and platforms whitepapers below.

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Data-Driven Marketing

Data-Driven Marketing

03/10/10

This report examines how organizations are using data to impact marketing performance and marketing effectiveness. For example, VITAS has been using IBM Cognos TM1 for over 10 years for everything from complex expansion planning to health care regulatory compliance, realizing numerous benefits. Read how in this case study.


Comprehensive Business Intelligence Market Study: Summary Results from The BI Survey 7, Analytic Service Providers (ASP) for Data Warehousing

03/03/10

The BI Survey 7 offers a valuable guide to the product capabilities and support users can expect from leading BI vendors, including Business Objects, Cognos, Hyperion, SAP, Oracle, and MicroStrategy. Download the Summary Report for more.


An Architecture for Software-as-a-Service (SaaS) Business Intelligence

An Architecture for Software-as-a-Service (SaaS) Business Intelligence

03/03/10

This white paper discusses how companies are exploring and successfully implementing SaaS to support on-demand business intelligence across all levels of their enterprise


Aster Data nCluster: A New Architecture for Data Analytics

Aster Data nCluster: A New Architecture for Data Analytics

03/02/10

Aster Data's nCluster brings a forward-looking, exciting new design for analytic data requirements. With its massively parallel architecture; its use of commodity hardware; its focus on scalability, availability and manageability; and its rapid innovation via the integration of MapReduce and other features, Aster Data's nCluster offers users a distinctive set of capabilities in a promising new design.


In-Database Analytics: The Heart of the Predictive Enterprise

In-Database Analytics: The Heart of the Predictive Enterprise

03/02/10

Visionary organizations are adopting an emerging practice known as “in-database analytics,” which supports more pervasive embedding of predictive models in business processes and mission-critical applications. With in-database analytics, enterprises migrate their predictive analysis (PA), data mining (DM), and other compute-intensive analytic functions from separate, standalone applications to execute in the enterprise data warehouse (EDW). Doing so allows IT professionals to leverage the EDW’s full parallel-processing, scalability, and optimization features. In-database analytics can help enterprises cut costs, speed development, and tighten governance on advanced analytics initiatives. Business process and applications (BP&A) professionals should implement in-database analytics in conjunction with ongoing efforts to consolidate and scale their EDW.


Business Drivers and Enabling Technologies for Clickstream Data Warehouse Initiatives

Business Drivers and Enabling Technologies for Clickstream Data Warehouse Initiatives

02/04/10

Dramatically reducing the time, cost and effort required for integrating large amounts of Web data can radically simplify an organization’s ability to analyze online visitor behavior via a clickstream data warehouse (CDW). Learn how to optimize your CDW and: • Gain greater insight into online customer behavior • Make more strategic decisions based on actionable data • Increase margins, lower costs and improve bottom line • Increase staff productivity


A Guide to the Value of Reliable Data in Insurance

A Guide to the Value of Reliable Data in Insurance

01/26/10

How critical is reliable data to the insurance industry? Insurance companies must attract the right customers, price correctly, write the right business, decline high-risk business, mitigate risk and reserve correctly. They must also maintain positive cash flow, manage outstanding claims and get the best re-insurance deals – all the while minimizing operating expenses. If unreliable data is flowing through the organization, the impact on the core insurance operational and analytical processes can be catastrophic.


Data Privacy Best Practices for Data Protection in Nonproduction Environments

Data Privacy Best Practices for Data Protection in Nonproduction Environments

01/25/10

Learn best practices for creating data privacy procedures in nonproduction environments including creating a set of policies to classify data types, integrating these policies into business processes, and providing ongoing compliance reviews.