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Thursday, August 23, 2012

A BI Architectures Approach to Modeling and Evolving with Analytic Databases

Major shifts converging in today's BI environment bring the opportunity to discover new answers to old questions about what BI architectures are about and how they are designed.

August 21, 2012

By John O'Brien, Principal, Radiant Advisors

Whether you have been a BI architect managing a production data warehouse for many years or are embarking on building a new data warehouse, the new analytic technologies coming out today have never been so powerful and complex to understand. In fact, with so many analytic technologies available on the market, they are somewhat overwhelming as we struggle to make sense of what to do with them and which ones to use with our existing environments.

This is good for BI architects because it brings us back to BI architecture fundamentals, key data management principles, pattern recognition, and agile processes, along with BI capabilities that challenge classic BI architecture best practices to design what clearly makes sense to meet the demands of business today.

There are major shifts converging in today's BI environment, and these changes bring with them the opportunity to discover new answers to old questions about what a BI architecture is all about and how an architecture is designed. As I explore these questions in this article, I will focus on three main themes: BI architectures are strategic platforms that evolve to their full potential; good architectures are based on recognizing data management principles (patterns and so-called best practices are discovered later); and BI architecture design is purposeful at every stage of development and technology decisions follow this purpose.

Architecture Maturity and Information Capabilities

We have all seen the research that says BI architectures evolve into robust information platforms and value over time. BI teams have focused on increasing value through maturing their data warehouses from operational reporting to data marts to data warehouses and finally to enterprise data warehouses. This evolutionary approach is typical when balancing pressing tactical needs for information delivery with strategic development, and is found in many companies where business demands for information drive towards a data warehouse platform.

The classic data warehouse is the last thing to be built, if ever, because the emphasis remains on quicker delivery first and information consistency later. Unfortunately, this leads to data warehouses that reflect current information needs and doesn't foster the evolution of a mature analytics culture.

Instead, an architecture based on BI capabilities focuses on nurturing the analytic culture of the business community by first educating user communities about the BI capabilities available and then on business subject data that is delivered via BI capabilities. This approach centers the data warehouse architecture on BI capabilities such as information delivery; reporting and parameterized reporting; dimensional analytics for goals achievement; and advanced analytics for gaining insights, to name a few. These discussions recognize that the same consistent data has many usage patterns, behaviors, and roles in the decision process. A BI architecture that is designed in this way ensures that data models and chosen analytic technologies are best suited to their intended purpose.

However, this BI-capabilities approach is contrary to some BI architects' belief that there should be an all-in-one data warehouse platform in the enterprise.

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