Any Organization attempting to implement high-speed Business Intelligence (BI) solutions for the delivery of timely and accurate information inevitably realizes the limitations of conventional database technologies (e.g. Oracle, SQL Server). Current technologies are hampered by the sophistication, overhead and expense inherent to processing complex or large volumes of data and simply cannot provide the performance or scalability so imperative to the effective support of critical business decisions.

Speed, Flexibility, Volume - Trends in Business Intelligence

A recent publication by IDC (source: IDC insight 2005) identified three significant market trends: growth in the number of end users, growth in data volumes, and growth in demand for the flexibility perquisite to shorter decision cycles. In an earlier study IDC quoted that "Only 15% of managers agree with the statement that most reports developed in their organizations deliver the right data to the right people at the right time" (source: IDC Business Intelligence Survey, May 2005). More subtly, IDC further states that "40% of organizations indicate that their business intelligence solutions can be down for more than a few hours without causing significant disruption to ongoing operations."

Organizations are finding that they require an entirely new breed of dedicated Business Intelligence Technologies—Decision Support Technologies specifically—which do not merely attempt to optimize the old traditional strategies of massive parallel CPU processing and heavy indexing. Such traditional solutions are either based on restrictive architectures or require the use of non-standard proprietary hardware.

And beyond these BI infrastructure issues lie implementation and operational issues. Companies invest millions of dollars in data warehouses, only to discover that a mere three to four per cent of data warehouse capability can be used effectively. Even worse, exercising a specific analysis may be impossible, as essential data is either entirely missing from the data warehouses or is only available in a summary format that precludes detailed analysis.

To address these requirements and shortcomings, organizations must find alternatives to traditional business analytics platforms—alternatives that can measurably speed set-up and run-time, that are flexible and adaptable to ever-changing business intelligence requirements, and are scalable—to a size essential to processing the volume of data available to business today, and into the future.

Speed Translates Into Profit

Speed is one critical factor in this equation—speed in set-up and at run-time will determine the measure of success that enterprises achieve in translating their data into intelligence and thus in translating speed into profit.

 

• Embedding in operational business processes - Easy setup
  Hilbert BI can be embedded within operational business processes to improve inventory, risk, and customer relationship management etc.
• Implementing - Fast setup
  Hilbert BI system implementation is simple and thus inherently quick, from data extraction through analysis
• Performance - Fast runtime
  Hilbert Analyses are performed in a mix of number and text, structured and un-structured data or across multiple data repositories (databases) with no degradation in per-formance
• Adapt to business changes - Fast setup and Fast runtime
  Hilbert BI systems adapts immediately to changing business needs in the form of new data sources, ad-hoc information requests, drill downs, etc.

A New Generation of BI Technology – Hilbert Engine

The Hilbert Engine from Hilbert Technology Inc. represents a radically new approach to analytical database technology. The combination of its simple yet elegant processes provides the most robust and adaptive set of tools available to create and manage previously impractical solutions.

The Advantage of Re-Solving a Problem

Technology or the applied sciences have a way of building on themselves. This process has produced some very productive results. Unfortunately, "progress" in such new application domains like Computer Science is not accomplished by a balanced and concerted conscious effort but through the serendipity of many opportunistic and pragmatic acts. That statement may appear somewhat jaded, but it is important to keep in mind when analyzing the "how and why" of current database technology.

Over the last 50 years the IT industry has moved from tubes, wires and Hollerith card counting machines to massive semiconductor computers, disk arrays and relational databases. A historical trace through commercially applied IT developments shows a progression of doing much the same thing faster and cheaper in synch with Moore’s Law. Most of the need in user communities, which drew attention and sourced the funding for improvements, was to expand on the benefits of transactional processing.

The database tools developed to support these systems incorporate vast complexities to maintain transactional and rollback integrity. Also, they have increasing capabilities for handling BLOBs (Binary Large Objects as a collection of binary data stored as a single entity in a database management system) such as pictures and sound files. Given the current and future relevance of Internet like platforms these improvements are very good – and profitable for the database companies.

Unfortunately, this path and its improvements have lead technological developments away from a very basic but growing need – the need for speed in queries, reporting and analysis. The simple reason for the "miss" in the market is that the database industry is trying to solve this problem on top of the technologies already in place. In particular the major offerings continue to extend and expand "indexing" features to their already burdened database engines.

A new and radically approach to analytical technology was needed which breaks with the traditional exhausted database approach.

Please read our Whitepaper "Perspective on Performance in Business Intelligence - The Hilbert Engine –" to get more information how Hilbert transforms speed into profit.