Ride the Next Wave of Flexible 'BI Workspaces'

Forrester Research says SaaS and in-memory options will let power users and analysts gain insight without IT bottlenecks. Embrace the trend, but beware the costs and risks.
Three Varieties of BI Workplaces

The beauty of BI Workspaces is that they let power users work with controlled production data without having to wait for IT and without having to port said data into a spreadsheet doomed to be disconnected if not downright dangerous. Here's how Forrester sizes up three leading approaches to providing BI Workspaces:

MOLAP. Newer than relational online analytical processing (ROLAP) and hybrid OLAP (HOLAP - combining MOLAP and ROLAP), MOLAP offers desktop-based slice-and-dice analytics. With leading examples including IBM Cognos PowerPlay and Oracle (Hyperion) Essbase, MOLAP lets analysts "create PC-based cubes using any data model that represents the best fit for the latest business requirements," writes Evelson. Thus, an analyst can use MOLAP to explore what-if scenarios such as deferring capital spending and halting discretionary spending in the face of an economic downturn and reduced sales.

BI SaaS and DW SaaS. The downside of MOLAP is that it can't always scale, so many companies are turning to SaaS-based BI and data warehousing to quickly gain access to flexible browser-based reporting and analytical tools. SaaS-based BI services such as InetSoft and Panorama analytics for Google Apps are a good fit for "smaller enterprises [or] departmental use cases where data sets are relatively small," according to Forrester. SaaS-based DW, in contrast, is geared to multigigabyte and terabyte data sets. The report cites the following example related to the current U.S. subprime credit crisis: "A number of financial services firms are loading large amounts of loan-level mortgage data into DW SaaS from providers such as 1010data [and Vertica] so they can analyze the patterns of historical prepayment, default, delinquency, and loss severity rates."

In-Memory Analytics. MOLAP and SaaS-based BI offer flexibility, but they still require data modeling steps. In contrast, in-memory technologies let you perform calculations and aggregations at RAM speeds with little data preparation. Thus, "in-memory models from vendors like InetSoft, QlikTech, and TIBCO Spotfire do not require a distinction between facts and dimensions — any element can be instantaneously used in either capacity," writes Evelson.


Based on the findings of the report, "BI Workspaces: BI Without Borders" offered four recommendations:

Include BI workspace functionality in overall BI requirements, but make sure end users have adequate training, use case examples and documentation.

Support BI workspace users by providing clean and timely data, and make sure users understand the implications of not using clean, secure or nonstandardized data sets.

Understand the implications of using a different technology/approach for BI workspaces. Some vendors provide both traditional BI and BI workspace applications using different technologies, so make sure you understand the risks and costs of supporting both environments.

Consider risks associated with SaaS-based BI/DW. "Due diligence is key," says Forrester. "For example, ask whether the SaaS vendor ensures data security by providing you with the results of its SAS 70 Type II Audit, and whether your hosted data is backed up in case of equipment failure or for disaster recovery purposes. "

The complete "BI Workspaces: BI Without Borders" report, which includes a matrix of Workspace approach definitions, strengths, cautions and vendors, as well as supporting case examples, is available as a free download through this link (registration required).