BlogDo you use R lattice? ggplot2? Or both?

Event RecapLive coverage of PAW strategies and business-minded discussions in analytics and predictive models, including insight this year from leaders at Google, Orbitz, the City of Chicago and others

BlogCorrelation isn’t enough for fruitful analytic advancement
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NewsBig Blue unveils IBM Watson Engagement Advisor as a customer-facing search and answer tool with a marketing wrinkle and mobile components

BlogLessons for BI in Northeastern labor research

BlogDavenport and Kim take on quants, pump brakes on big data "revolution"
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Like few other careers, the roles of the information manager are on perpetual course of disruption. Certain aspects of that change, however, cry out for more attention than others across business and even throughout IT departments.

ColumnIt is not just big data, but rather the big value that can be created by converting data into meaningful and relevant information for insight, foresight, decisions and actions
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Industry EventsDimensional data is a core component of modern BI/DW implementations. Dimensionally organized data offers a more effective and adaptable solution to business analytics needs than can be achieved with relational data structures. Virtually anyone involved in business intelligence and data warehousing projects needs to have fundamental knowledge of the pathway from business questions to business analytics. In addition, real-world data warehouse designs rarely resemble the simple star schemas found in product demos or introductory courses (with only a single fact table, fully additive facts, and several standard dimension tables). The TDWI Washington, DC Seminar will teach the core fundamental principles as well as provide an extended set of techniques to address real-world complexity.

summaryA recap of our bi-weekly Twitter chat, featuring a number of the most insightful tweets

BlogSalford Systems series is a solid primer, with promising specialized models
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Michael J. Hernandez’s best-selling Database Design for Mere Mortals has earned worldwide respect as the clearest, simplest way to learn relational database design. Now, he’s made this hands-on, software-independent tutorial even easier, while ensuring that his design methodology is still relevant to the latest databases, applications, and best practices. Step by step, Database Design for Mere Mortals, Third Edition, shows you how to design databases that are soundly structured, reliable, and flexible, even in modern web applications. Hernandez guides you through everything from database planning to defining tables, fields, keys, table relationships, business rules, and views. You’ll learn practical ways to improve data integrity, how to avoid common mistakes, and when to break the rules.

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 ask for 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.

Data Discovery and Profiling is a relatively new data management technique that uses tools to identify all data sources throughout the organization and unearth the meaning of the information itself. As with any new paradigm, today’s budget holders and decision makers must first be educated on data discovery and profiling and then shown the value it can bring to the organization through a clear return on investment (ROI) analysis.

Read the usability comparative product review by Craig Boyd as he ranks CA ERwin against PowerDesigner and ER/Studio. All three tests involved modeling for a Microsoft SQL Server 2008 environment.

Data modeling is a time-proven method for understanding data, its interrelationships and its rules. Data management professionals have long understood the value of data modeling. However, business professionals often don’t understand the value of data modeling. So how can you demonstrate to business management the return on investment (ROI) of data modeling? Read this white paper and find out.

In today’s information-driven economy, more and more organizations rely on business intelligence (BI) applications to make strategic business decisions. A BI report is only as good as the database on which it is built, however, and a data model is key to understanding both the business and technical requirements behind the construction of a BI solution. CA ERwin and SAP BusinessObjects BI solutions provide a powerful combination to produce best-of-breed reporting and analytics, along with with robust data modeling and database design capabilities.