Brio Announces Multi-dimensional Analysis Architecture for Data Warehouses; Completes Three-Part Data Warehouse Strategy With New Products and Strategic Partnerships

Business Wire, July 10, 1995

MOUNTAIN VIEW, Calif.--(BUSINESS WIRE)--July 10, 1995--Brio Technology, Inc., today unveiled a three-part strategy to deliver front-end solutions to the expanding data warehouse market.

The strategy comprises a multi-dimensional analysis architecture for access to the data warehouse, a new version of the company's flagship product, BrioQuery, and key strategic partnerships. Additionally, Brio is showcasing two customers, Arizona State University (ASU) and Philip Morris, who have integrated Brio's technology into their own data warehouse solutions.

"Brio's goal is to enable organizations, individuals, and IS professionals to get more value out of their data warehouses through easy and effective desktop data access, multi-dimensional analysis, and reporting," said Katherine Glassey-Edholm, Brio's executive vice president of products and marketing. "Brio's multi-dimensional analysis module that is included in the query tool provides insight into information, not just simple access and reporting."

Today's announcement underscores the data warehouse market's rapid acceptance of Brio's leading multi-dimensional analysis technology across a broad range of industries including retail and consumer goods, higher education, bio technology, telecommunications, entertainment, transportation, and government.

Multi-dimensional Analysis Architecture

To remain competitive in today's business climate, corporations are turning to data warehouses to give them a strategic decision making advantage. A widely recognized definition of a warehouse is a subject-oriented, integrated, time variant, non-volatile collection of data in support of management's decision making process. Subject-oriented means the data warehouse focuses on the high-level entities of the business. Integrated means the data is stored in a consistent format. Time variant means the data associates with a point in time and lastly, non-volatile means the data doesn't change once it gets into the warehouse.

In addition to the data and metadata, the well-designed warehouse should contain a primary repository for standard queries and reports. In some cases, it is also useful to define user-acceptable logical models of the physical database schema and store them in the data warehouse. The most successful warehouses today are being designed for user readability, most typically using principles of Star schema design. Star schemas offer numerous benefits, in addition to business modeling and useability, of which the most noticeable is dramatically increased performance.

A data warehouse must also deliver the right data to the right people. People are always asking new questions, so predicting what they need is an art, not a science. Users often don't know what they need until they see it. Thus, end-user access tools must be capable of unlocking different kinds of information in different ways. The best access tool choice is one that serves the needs of the widest variety of users -- from those whose primary need is for pre-defined reports, to those who need access to any data and the liberty to surf through it at will. In the first case, simplicity is critical, in the latter case, flexibility is the main criteria. All users require a powerful and responsive tool. To minimize training, maintenance and support burdens on IS staff, the ideal tool will also run on common desktop platforms and fully utilize the organization's database system.

On the desktop, users need more than reporting, they need analysis in the form of a multi-dimensional engine to facilitate drill down, trend and comparative analysis. The engine needs to include: interactive manipulation of analysis against actual report data, dynamic memory paging to allow for high performance when dealing with large amounts of data, ability to deal with sparse matrix results, and an interface which makes a hypercube model approachable to users.

BrioQuery

BrioQuery(TM) is a complete ad hoc visual query and analysis tool with built-in multi-dimensional analysis and easy reporting designed especially for data warehouses. It was developed to leverage the performance capabilities inherent in Star schema-designed warehouses. It features an optional repository for central management of shared queries and Automatic Distributed Refresh (ADR) for automatic distribution and version control.

BrioQuery, available for Windows, Mac OS, and UNIX, is the first query product to focus on the analysis of data, not just the production of reports. To bring warehouse data analysis to the desktop, BrioQuery places a lightning-fast multi-dimensional analysis tool at the heart of an advanced ad-hoc SQL query system. This powerful analysis engine supports an intuitive, interactive "DataPivot(R)-style" interface that is unsurpassed in any desktop query tool. BrioQuery also includes a graphical query request builder, a flexible, one-step band-style reporter with built-in templates and style sheets, and high-level scripting for building desktop EIS systems. This combination of powerful features in a single, easy-to-use package makes BrioQuery an attractive alternative for both IS and end-users.