Simply put, data mining is a continuous, iterative process that is the very core of business intelligence.
It involves the use of data mining software, sound methodology and human creativity to
achieve new insight through the exploration of data to uncover patterns,
relationships, anomalies and dependencies.
We have achieved our reputation as the data mining industry’s leading innovator
by developing powerful, user friendly and affordable data mining technology,
and by delivering comprehensive knowledge transfer to customers to enable them
to take advantage of the business benefits data mining technology makes possible.
For almost a decade we has taken the leadership role in broadening user
understanding and acceptance of this technology as a highly value decision
support system for a wide range of business applications in many different
industries.
Our data mining customers -- one of the largest installed base of active users of this
technology in the world -- have been increasing their revenues, lowering their
costs, and enhancing their competitive positions because they have openly
embraced and actively explored the possibilities data mining technology offers
to them.
Business Intelligence Benefits
Data mining technology delivers two key business intelligence benefits:
It enables enterprises, regardless of industry or size, in the context of
defined business objectives, to automatically explore, visualize and understand
their data, and to identify patterns, relationships and dependencies that impact
on business outcomes (such as revenue growth, profit improvement, cost containment,
and risk management) - a descriptive function.
It enables relationships uncovered and identified through the data mining process
to be expressed as business rules, or predictive models. These outputs can be
communicated in traditional reporting formats (presentations, briefs, electronic
information sharing) to guide business planning and strategy. Also these outputs,
expressed as programming code, can be deployed or "hard wired" into business
operating systems to generate predictions of future outcomes, based on newly
generated data, with higher accuracy and certainty - a predictive function.
For example, in the "CRM" arena a business can evaluate and develop a set of
business intelligence rules about all aspects of its customer interactions. A simple example
is modeling the likelihood of response to a specific solicitation of a new product
or service. Based on these business rules, the business can target its marketing
campaigns for maximum response to generate a desired level of response, revenue or
profitability. Other typical "CRM" business examples would include:
modelling customer acquisition (for targeted marketing and other CRM initiatives)
assessing customer defection (for customer service and reclamation purposes)
monitoring risk of loss (for customer scoring and credit approval decision making)
abuse intervention (to reduce losses through investigation of incidence of fraud)
However, the reach of data mining technology extends far beyond "CRM" to encompass
any process involving the acquisition, interpretation and acting on of data (internally
or externally sourced). In the business domain this would include areas as diverse
as internal audit and expense control through to research and development for new
products or services.
Using our data mining components, a wide range of solutions can be developed
directly and easily as applications integrated with our data mining engine
or as integrated components of mainstream "CRM/ERM" "sales force automation", "call
center" and other enterprise applications - by third party solution providers or in
the case of a large organization by their internal IT personnel working in
collaboration with Angoss and its partners.
Technology Issues
Data mining solutions are based on the implementation, through programming, of
interfaces to generally available and privately developed algorithms which
enable the efficient exploration and organization of data. These algorithms
support the identification of patterns, relationships and anomalies of potential
interest to business decision-makers.
In addition to implementing these algorithms in a user accessible method, data
mining technology also requires an understanding of various databases and
implementation of data mining solutions to take advantage of features of such
databases (if any) that make data mining tasks more efficient over large volumes
of data.
In addition to algorithm implementation issues, key considerations relative to
data mining solutions are data preparation issues and ensuring scalability and
performance over very large volumes of data.
We offer:
a complete range of data mining solutions targeted to both power analysts and
business users; and
a robust development environment and portfolio of reusable, customizable data
mining components ANGOSS technology partners can integrate with their decision
support systems and / or use to develop "intelligent" enterprise solutions; and
a comprehensive portfolio of value added applications developed by us using
our data mining components to craft business solutions to business problems.
We successfully differentiate ourself in the data mining industry by:
focussing on ease of use, performance over large data volumes, integration with
major database systems, and affordability as the key touchstones of its product
design and development process;
leveraging Microsoft technologies (applications, developer tools, database and
Internet platforms) with data mining solutions that span the Office and Back
Office product suites, enabling superior value added solutions for customers
with a Microsoft "solution of choice";
consistently leading the data mining industry in innovation; and
understanding the needs of and delivering value - in the form of technology,
knowledge transfer and exceptional, personalized service -- to our customers.
