In the Forrester blog For Information and Knowledge Workers analyst James Kobielus has an interesting post on the change of guard in the BI scenario looking at the eventual demise of OLAP.
I think the points raised by James are very relevant and does get us thinking on how we should look at the whole analytical framework / architecture that is defined in organizations.
Excerpts from the post:
No one expects the OLAP cube to vanish completely from the BI landscape, but its role in many decision-support environments has been declining over the past several years. Increasingly, vendors are emphasizing new approaches that, when examined in a broader context, appear to be loosening OLAP's lockhold on mainstream BI and data warehousing. The emerging paradigm for ad-hoc, flexible, multi-dimensional, user-driven decision support includes the following important approaches:
- Automated discovery and normalization of dispersed, heterogeneous data sets through a pervasive metadata layer
- Semantic virtualization middleware, which supports on-demand, logically integrated viewing and query of data from heterogeneous, distributed data sources without need for a data warehouse or any other centralized persistence node
- On-the-fly report, query, and dashboard creation, which relies on dynamic aggregation of data, organization of that data within relevant hierarchies, and presentation of metrics that have been customized to the user or session context
- Interactive data visualization tools, which enable user-driven exploration of the full native dimensionality of heterogeneous data sets, thereby eliminating the need for manual modeling and transformation of data to a common schema
- Guided analytics tools, which support user-driven, ad-hoc creation of sharable, extensible models containing data, visualization, and navigation models for customizable decision-support scenarios
- Inverted indexing storage engines, which support more flexible, on-the-fly assembly of structured data in response to ad-hoc queries than is possible with traditional row-based or column-based data warehousing persistence layers
- Distributed in-memory processing, which enables continuous delivery of intelligence being extracted in real-time from millions of rows of data that originates in myriad, distributed data sources