Fast Challenges Data Warehouses By Combining Search And Real-Time BIFast Challenges Data Warehouses By Combining Search And Real-Time BI

Fast Search & Transfer unveiled details Monday of its Adaptive Information Warehouse, its bid to overcome the limitations of data warehouses by converging search engine and business intelligence functions in a single platform.

The ambitious effort essentially places relational online processing on the front end of reports generation before the search function kicks in. "Users can directly search and navigate business intelligence data in an ad hoc manner, then display relevant, usable information to users without the need for predefined report creation," said Davor Sutiga, Fast's VP of strategic market development.

In an interview, Sutiga said AIW is driven largely by two key elements -- a BI portal and the Fast Radar and the Fast Data Cleansing system.

"In the past, the key has been how you store the data," he said. "And it has taken time to built a data warehouse. But now with AIW you can act on data in real time."

Fast Radar is Web-based and can present statistical analysis to a range of users throughout enterprises via a familiar search and navigation interface. Sutiga said users often can build in days reports that previously took weeks to create from traditional data warehouse and BI technologies.

The data cleansing feature uses a combination of linguistic data cleaning, ad hoc query, and other functions to process structured and unstructured information. A single, reliable master index can be generated from data repositories, regardless of format and location.

"Search-based business intelligence eliminates the hours spent each day sorting through thousands of static records," Sutiga said. "It puts aggregate and real-time information at decision-makers' fingertips, enabling them to quickly create ad hoc exception reports and interactively investigate and identify trends."

AIW is aimed primarily at new users, he said, although some current Fast customers already use the technology. He cited the Reuters news and financial reporting service as an example of a company that has replaced a database-centric service with AIW features for trading platforms and premium content. Sutija said Reuters can analyze millions of financial derivatives in real time by essentially taking the data out of data bases and moving them into AIW.

Energy bar maker Clif Bar implemented the radar function in less than a month, Sutija said. By analyzing historical sales figures, sales forecasts and other items, he said, the company was able to cut its inventory from 77 to 35 days.