During the last decade, we have seen an explosive growth in our capabilities to both generate and collect data.

Advances in data collection, widespread use of bar codes for most commercial products, and the computerization of many business and government transactions have flooded us with information, and generated an urgent need for new techniques and tools that can intelligently and automatically assist us in transforming this data into useful knowledge.

This is the emerging field of knowledge discovery in databases (KDD) and data mining that derives from statistics, databases, machine learning, and artificial intelligence.

In the data is the knowledge. In the dissemination of that knowledge lies the power. We use knowledge-based techniques to find provable new value in our clients' data. Rather than replace current database technology, we extend its typical query-and-response approach to one which incorporates knowledge of the enterprise, its purposes, processes, and problems, opening the door to the discovery of valuable new business knowledge in existing data.

Amid a flood of data, there is a thirst for knowledge. Are your people drowning in data? The metaphor is ubiquitous. Relying on faster, larger, and cheaper storage media and using better database management systems, the world of business is awash in data. Few would disagree with Francis Bacon's claim of 400 years ago that "Knowledge itself is power". But, in this age of data (some would say too much data), knowledge can be elusive. Diffuse, fragmented, useful, it seems, for narrow purposes, the very data we rely on for better decision making; better marketing and operation, and better control of the enterprise overwhelm us. Looking beyond the bare facts, we look for the underlying meaning. We look for insight. We seek the knowledge hidden in the data.

Existing knowledge is the catalyst for finding new knowledge. Although we implement and use proprietary software, our focus is not on computer programs, but rather on the client's issues, concerns, unmet goals, or unsolved problems. And although we do extensive computation on client data, the focus is not on specific programs, but rather on customizing the process by capturing and encoding the particulars of each case.

The Overline's Knowledge Discovery process consists of five steps:

• Problem Specification: Starting with issues, concerns, and general objectives, a problem description evolves. Finally, a problem specification is arrived at. This includes quantifiable measures for later test and validation. Business context representation, background/contextual knowledge, prior practice, rules of operation, etc., are first recorded and then encoded as computable objects

• Data Preparation: Phases include encoding the data dictionary and data field semantics, sample selection, and data cleaning. Technical issues addressed include missing data fields, data uncertainty, and ordering of events in time

• Data Analysis: Selection, application, integration, and customization of data mining and data analysis methods

• Presentation of Results: Test, validate, evaluate, implement, and report. Results must be provably novel, useful, and understandable

• Systems Integration