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TDWI White Paper Library

TDWI is proud to maintain this library as a valuable resource for business intelligence and data warehousing professionals seeking to augment their knowledge of tools, solutions, and industry best practices. The content in this repository is created by our many partners in the BI/DW hardware, software, and consulting markets.

See the most recent white papers below.

Find out more about how your organization can post a paper.


Business Analysis and Design - White Papers


Building Blocks for Segmentation: Advancements in Market Segmentation

Building Blocks for Segmentation: Advancements in Market Segmentation

The essence of any segmentation strategy is to allow a company to craft value propositions for specific products and services. There are many ways to develop a segmentation scheme. A widely used approach is to execute a primary research study among a representative sample of customers and prospects.


Three Keys to Better Data-Driven Decisions: What You Should Know...Right Now

Three Keys to Better Data-Driven Decisions: What You Should Know...Right Now

Small and midsize companies everywhere are inundated with waves of data -- about products, competitors, suppliers, and customers. This information explosion is not the only problem facing companies when it comes to data management. There’s also a tendency for employees to create and maintain files on local drives rather than shared databases, effectively creating “micro-silos” of data.


Best Practices for Implementing a Data Warehouse on the Oracle Exadata Database Machine

Best Practices for Implementing a Data Warehouse on the Oracle Exadata Database Machine

Increasingly, companies are recognizing the value of an enterprise data warehouse (EDW). A true EDW provides a single 360-degree view of the business and a powerful platform for a wide spectrum of business intelligence tasks, ranging from predictive analysis to near-real-time strategic and tactical decision support throughout the organization.


CA ERwin Data Modeler’s Role in the Relational Cloud

CA ERwin Data Modeler’s Role in the Relational Cloud

The Database-as-a-Service (DaaS) model is growing its presence across the virtual globe. Enterprises are asking whether DaaS is mature enough to meet their low-cost and high-security data management requirements using a cloud paradigm. Companies are deploying virtualized applications and, as a consequence, they are asking for relational cloud as well. Dynamic scalability, privacy, performance, and heterogeneous environments or interfaces to business intelligence products need a class of solutions to satisfy the cloud paradigm as a whole. Data modeling supports DaaS in that it provides a clear mapping of the deployed data structures, storage, and data topology as well as the core business concepts. In this way, CA ERwin Data Modeler enables organizations to collect and serve data models from and to any web data source and data management system in the cloud.


Advancing the Data Agenda: Roles and Responsibilities in the Data Quality Trenches

Advancing the Data Agenda: Roles and Responsibilities in the Data Quality Trenches

It is high-minded to observe that any data program (data governance, master data management, data analytics, data modeling, etc.) requires a full partnership between business and IT. The real world is more mundane and far more demanding than that. Indeed, success with any data program depends on getting the right groups involved and assigning the right people the right roles and responsibilities. This white paper addresses the question, "who, specifically, are those right groups and right people?" on two levels. It also highlights underlying principles that, properly applied, lead to solid answers to the "who" questions for a wide variety of scenarios.


The Practitioners Guide to Data Profiling

The Practitioner's Guide to Data Profiling

Data profiling has become the de facto starting point for practically any data-oriented project because it uncovers potential issues and provides valuable insight into your data. Its ability to summarize details about large data sets from different angles makes it a powerful tool for analysts -– yet without specific direction, there is a risk of “analysis paralysis” that prevents business objectives from being realized. To fully achieve the power and benefits of data profiling, it’s important to clarify analysis processes and techniques that use the technology. This white paper addresses data profiling techniques that can help the analyst understand the empirical truth associated with enterprise data.


Master Reference Data: Extract Value from Your Most Common Data

Master Reference Data: Extract Value from Your Most Common Data

Creating master reference data can be very simple. Typically, an organization will start small and then grow the master as acceptance spreads throughout the organizations. This white paper presents some key considerations for creating a reference data master.


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