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.
Much has been written in recent years about the need to spread BI to the masses — embedding intelligence within applications, simplifying interfaces, improving access to reports and so on. A new report from Forrester Research suggests that the next wave of productivity lies in providing flexible "BI Workspaces" to the oranization's few analysts and power users. Overcoming the classic tech struggle between autonomy and centralized control, BI Workspaces are said to offer a best-of-both-worlds environment in which experts can analyze and model vetted data without facing IT constraints.
Just what are "BI Workspaces"? The Forrester Report, "BI Workspaces: BI Without Borders," defines them as "a data exploration environment where a power user can analyze production, clean data with near complete freedom to modify data models, enrich data sets, and run the analysis whenever necessary, without much dependency on IT and production environment restrictions." The primary examples of these environments are multidimensional OLAP (MOLAP) slice-and-dice analytics, BI and data warehousing delivered Software as a Service (SaaS) style, and in-memory analytics platforms. This article explains why report author Boris Evelson writes that one or more of these capabilities must now be on the list for any leading-edge BI environment.
Why Your Arms Are Tied
The quest for the proverbial "single version of the truth" and various compliance requirements have in many ways restricted the flexibility of reporting and analysis, according to Forrester. As an example, corporate security, data privacy and regulatory requirements are often an obstacle to open and timely access to information. What's more, despite ever-faster-and-more-powerful processing capacities, analyst and power-user demands still take a back seat to day-to-day transactional loads. Perhaps most restrictive is the architecture of many BI environments, which creates a "cascading effect of interdependent components" (see chart at right).
Cascading Effects of Independent BI Components (click image for larger view)
"Most BI environment architectures use several components (sometimes more than 30!), such as data discovery, transformation and integration, data modeling, online analytical processing (OLAP), reporting, dashboards, and alerts, and involve as many steps for raw data to reach its final destination in a report or a dashboard," writes Evelson. "One simple change to even a single source data element may result in a few changes to extract, transform, load (ETL) and data cleansing jobs, which may turn into several data model changes in operational data store (ODS), data warehouse (DW), and data marts; this in turn affects dozens of metrics and measures that could be referenced in hundreds of queries, reports, and dashboards."
All too often, power users, analysts and business users alike have attempted to work around these restrictions by resorting to spreadsheets, but this approach no longer works. "Aside from the lack of controls and huge operational risk, spreadsheets are just not powerful enough to analyze terabytes of data involved in any modern, competitive decision-making," Evelson writes.
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