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5 Data Mining Examples

5.1 Bass Brewers

Bass Brewers is the leading beer producer in the UK and has a 23% of the market. The company has a reputation for great brands and good service but realised the importance of information in order to maintain a lead in the UK beer market.

We've been brewing beer since 1777, with increased competition comes a demand to make faster, better informed decisions.

Mike Fisher, IS director, Bass Brewers

Bass decided to gather the data into a data warehouse on a system so that the users i.e. the decision-makers could have consistent, reliable, online information. Prior to this users could expect a turn around of 24 hours but with the new system the answers should be returned interactively.

For the first time, people will be able to do data mining - ask questions we never dreamt we could get the answers to, look for patterns among the data we could never recognise before.

Nigel Rowley, Information Infrastructure manager

This commitment to data mining has given Bass a competitive edge when it comes to identifying market trends and taking advantage of this.

5.2 Northern Bank

A subsidiary of the National Australia Group, the Northern Bank has a major new application based upon Holos from Holistic Systems now being used in each of the 107 branches in the Province. The new system is designed to deliver financial and sales information such as volumes, margins, revenues, overheads and profits as well as quantities of product held, sold, closed etc.

The application consists of two loosely coupled systems;

The Northern is addressing the need to convert data into information as their products need to be measured outlet by outlet, and over a period of time.

The new system delivers management information in electronic form to the branch network. The information is now more accessible, paperless and timely. For the first time, all the various income streams are attributed to the branches which generate the business.

Malcolm Longridge, MIS development team leader, Northern Bank

5.3 TSB Group PLC

The TSB Group are also using Holos supplied by Holistic Systems because of

its flexibility and its excellent multidimensional functionality, which it provides without the need for a separate multidimensional database

Andrew Scott, End-User Computing Manager at TSB

The four major applications which have been developed are:

5.4 BeursBase, Amsterdam

BeursBase, a real-time on-line stock/exchange relational data base (RDB) fed with information from the Amsterdam Stock Exchange is now available for general access by institutions or individuals. It will be augmented by data from the European Option Exchange before January, 1996. All stock, option and futures prices and volumes are
being warehoused.

BeursBase has been in operation for about a year and contains approximately 1.8 million stock prices, over half a million quotes and about a million stock trade volumes. The AEX (Amsterdam EOE Index) or the Dutch Dow Jones, based upon the 25 most active securities traded (measured over a year) is refreshed via the database approximately every 30 seconds.

The RDB employs SQL/DS on a VM system and DB2/6000 on a AIX RS/6000 cluster. A parallel edition of DB2/6000 will soon be ready for data mining purposes, data quality measurement, plus a variety of other complex queries.

The project was founded by Martin P. Misseyer, assistant professor on the faculty of economics, business administration and econometrics at Vrije Universiteit. More information about BeursBase can be found on the World Wide Web at,

BeursBase unique in its kind is characterized by the following features: first BeursBase contains both real time and historical data. Secondly, all data retrieved from ASE are stored, rather than a subset, all broadcasted trade data are stored. Thirdly, the data, BeursBase itself and subsequent applications form the basis for many research, education and public relations activities. A similar data link with the Amsterdam European Option Exchange (EOE) will be established as well.

5.5 Delphic Universities

The Delphic universities are a group of 24 universities within the MAC initiative who have adopted Holos for their management information system, MIS, needs. Holos provides complex modelling for IT literate users in the planning departments while also giving the senior management a user-friendly EIS.

Real value is added to data by multidimensional manipulation (being able to easily compare many different views of the available information in one report) and by modelling. In both these areas spreadsheets and query-based tools are not able to compete with fully-fledged management information systems such as Holos. These two features turn raw data into useable information.

Michael O'Hara, chairman of the MIS Application Group at Delphic

5.6 Harvard - Holden

Harvard university has developed a centrally operated fund-raising system that allows university institutions to share fund-raising information for the first time.

The new Sybase system, called HOLDEN (Harvard Online Development Network), is
expected to maximize the funds generated by the Harvard Development Office from
the current donor pool by more precisely targeting existing resources and eliminating wasted efforts and redundancies across the university. Through this streamlining, HOLDEN will allow Harvard to pursue one of the most ambitious fund-raising goals ever set by an American institution to raise $2 billion in five years.

Harvard University has enjoyed the nation's premier university endowment since 1636. Sybase technology has allowed us to develop an information system that will preserve this legacy into the twenty-first century

Jim Conway, director of development computing services, Harvard University

5.7 J.P. Morgan

This leading financial company was one of the first to employ datamining/forecasting applications using Information Harvester software on the Convex Examplar and C series.

The promise of data mining tools like Information Harvester is that they are able to quickly wade through massive amounts of data to identify relationships or trending information that would not have been available without the tool

Charles Bonomo, vice president of advanced technology for J.P. Morgan

The flexibility of the Information Harvesting induction algorithm enables it to adapt to any system. The data can be in the form of numbers, dates, codes, categories, text or any combination thereof. Information Harvester is designed to handle faulty, missing and noisy data. Large variations in the values of an individual field do not hamper the analysis. Information Harvester has unique abilities to recognize and ignore irrelevant data fields when searching for patterns. In full-scale parallel-processing versions, Information Harvester can handle millions of rows and thousands of variables.

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