Title: The Big BI Dilemma

Abstract: The heart of most current BI systems is formed by a traditional data warehouse architecture initially designed to support classic forms of reporting. For such systems, aspects such as improved governance, high-quality data, and stability play a key role. To offer such qualities, BI systems are accompanied by a rigid development, operation, and management process.

This traditional architecture has had a great run for twenty-five years and has served countless organizations well. But for many organizations it has passed its expiration date. Due to its rigid architecture, many new BI requirements are hard to implement with the existing BI systems. For example, BI systems have to support new forms of reporting and analytics, such as self-service BI; investigative analytics; data science; external users, such as online customers, partners, and suppliers; new storage technologies, such as Hadoop and NoSQL; external data sources, such as social media data and open data; and large quantities of data. In addition, reports must be developed faster.

Organizations know that their current BI system can’t be thrown away, because the existing reporting workload has to keep working. But how should they implement this new BI workload and integrate it somehow with the existing system? This is currently the big BI dilemma many organizations struggle with. In this keynote this dilemma is explained and solutions are presented.

Related Articles and Blogs:

 The Big BI Dilemma

 The Logical Data Warehouse Architecture is Tolerant to Change

 The Need for Flexible, Bi-Modal Data Warehouse Architectures

 The Big BI Dilemma

Related Whitepaper:

 Developing a Bi-Modal Logical Data Warehouse Architecture Using Data Virtualization