Whitepaper : ER/Studio Data Architect

Today, you cannot pick up a magazine, read a blog, or hear a webcast without the mention of big data. It has had a profound effect on our traditional analytical architectures, our data management functions, and, of course, the analytical outputs. This paper describes an analytical architecture that expands on the existing enterprise data warehouse (EDW) to include additional data sources, storage mechanisms, and data handling techniques needed to support both conventional sources of data and those supplying big data.

The real value of a decision-making environment is not in creating reports or simple multi-dimensional analytics. It is in the development and use of the more sophisticated analytics like statistics, predictive algorithms, and data mining using all sources of data. And the way to support these critical capabilities is by extending the traditional data warehouse environment to include data sets in more fluid, less controlled components besides the EDW. Everyone in the enterprise (not just trained data scientists or statisticians) should be able to access all architectural components to include these valuable capabilities in their everyday decision-making.

The extended data warehouse (XDW) architecture supported all forms of data, analytics, and the business community, creating and using these. It comprises three analytical environments that must work together to give enterprises consistent, reliable and global views of customers, products, markets, etc.

In this paper, Claudia Imhoff, Ph.D., describes an analytical architecture that expands on the existing Enterprise Data Warehouse (EDW) to include new data sources, storage mechanisms, and data handling techniques needed to support both conventional sources of data and those supplying Big Data. She shares insights on the three analytical environments of an Extended Data Warehouse Architecture (XDW) including:
• Traditional EDW environment
• The Investigative Computing Platform
• Operational intelligence