Building toward BPM

IN THE SCRAMBLE to bring accountability and profitability back to the corporate world, BPM (business performance management) is gaining momentum in the BI (business intelligence) world. The basic idea is to tie operational data from enterprisewide systems to specific business goals, and to provide managers with integrated visibility into performance against those goals -- a tall order.

To deliver on this promise, vendors such as SAS, Cognos, Hyperion, and Business Objects are rolling out BI platforms, frameworks, and architectures that aim to enable more integrated data gathering, analysis, and metrics-based management throughout the enterprise. The promise here is to go beyond querying and reporting to facilitate integration of multiple analytic applications across departments, and provide a seamless delivery experience for large numbers of users. "Having a BI architecture that lets you go from ETL [Extraction, Translation, and Loading] and data cleansing to analysis to information delivery is where the value proposition is," explains Keith Collins, chief technology officer of SAS in Cary, N.C.

But behind these frameworks are a host of thorny technological issues that need to be worked out. And to complicate matters, everybody's jumping into the analytics game, including the major ERP, CRM, and SCM ISVs, and of course none of the players wants to give up its current piece of the action. Here's a rundown of some of the key issues involved in making the analytics ugly duckling turn into a business performance management swan.

Data acquisition, metadata, and real-time cleansing

Although recent investment in ERP and CRM systems has resulted in better source data for performance management systems, data integration is still high on the list of pain points that the new BI frameworks are attempting to address. Unlike operational connectors, BI vendors' tools are geared toward extraction of detailed historical data from core systems (front end and back end) through bulk extraction and so-called "drip-feed" techniques. This data must also be tied into both data warehouses and spreadsheet-based data.

Vendors such as Ascential Software and Informatica have developed platforms for data integration, ETL and staging for analytic applications, leveraging prepackaged connectors plus XML to perform key functions, such as data cleansing, transformation, profiling, and source system analysis (to cut down on labor costs in the discovery phase). The resulting data can then be tapped by other BI vendors.

One key issue is data consistency or normalizing the data -- for example, getting data that relates to specific buckets of time for trending analysis. "You have to do predictive modeling or forecasting in a known state," says SAS's Collins. "You've got to have a consistent source of data to work from."