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How to profit from your investments
in data collection systems
“Avoid unprofitable projects through a better use of your
data with BayMiner EWS” (Early Warning System)
Ralf Ekholm
CEO
Bayes Information Technology Ltd.
BayMiner EWS for Executives
© Bayes Information Technology Oy 2007
What is it all about?

The data analysis market is changing:
1. Data mining is not sufficient anymore.
2. Classic reporting is replaced by predictive analytics.
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
For managers:

A method to identify risk factors.

A method to get realistic forecasts.
For users:


A new method to know better before deciding.
BayMiner EWS is NOT:

An administrative tool.

A project scheduling tool.
BayMiner EWS for Executives
© Bayes Information Technology Oy 2007
Advantages and Benefits
 You get project risks under control already in the
tendering phase:
 Steer sales away from risky product & market combinations.
 Recognize the co-influence of several risk factors.
 Avoid 75 % of unprofitable projects..
 You can utilize company knowledge effectively:
 Share knowledge over organizational borders.
 Avoid the use of scarce resources for unprofitable tasks.
 Costs only 20 % of experts’ manual screening.
BayMiner EWS for Executives
© Bayes Information Technology Oy 2007
Familiar problems?
 Inappropriate order intake causes surprise costs.
 Networking has brought new risks.
 Your statistics are not trustworthy.
 Your information system for reuse of past experience is
restricted to document sharing.
BayMiner EWS for Executives
© Bayes Information Technology Oy 2007
These problems can be solved:
 With the BayMiner EWS method that:
 Elicits knowledge from sparse data.
 Presents information in an easily understood way.
 BayMiner PRO is a decision support development tool that:
 Learns from data about operations in the past.
 Visualizes problem clusters.
 Indicates the probable causes and their co-influences.
 BayMiner EWS is a special version for on-line risk recognition.
 Easy to integrate - operates via the company's intranet.
 Highly visual - indicates results with simple traffic lights.
BayMiner EWS for Executives
© Bayes Information Technology Oy 2007
Predicting risk using BayMiner EWS,
the steps during the development phase
1. Collect in a table the essential data about realized projects.
2. BayesIT’s experts process it and produce a model of the risks.
3. Projects are grouped according to how well they have materialized,
using true multi-dimensional modelling.
4. All variables (up to tens) and their values are considered
simultaneously.
5. The resulting risk model is used to steer traffic lights for clear
communication to the end user.
6. These traffic lights combined with a questionnaire on your intranet
functions as an on-line risk screening application.
BayMiner EWS for Executives
© Bayes Information Technology Oy 2007
Predicting risk using BayMiner EWS,
steps during the use.
1. Key in known data about a new project (approx 15 questions).
2. Observe how the traffic lights light up.
• Green=ok, yellow=more data required, red=forbidden to tender.
3. During development phase you may do off-line analysis:
1. Observe how a new project positions in relation to the other projects.
2. If the new project locates itself among the weak ones, it is very likely
that the new project will not succeed either.
3. Alternatively predict unknown values using the Profile in BayMiner Pro:
1. Select a number of similar cases (near the one under study).
2. You get the prediction for variables whose values are not known.
BayMiner EWS for Executives
© Bayes Information Technology Oy 2007
Useful links
 http://www.bayminer.com/
 http://cosco.hiit.fi/
 the research group behind it.
 http://www.bayminer.com/files/papersetc/bnets.pdf
 theory, pretty heavy.
 http://www.kdnuggets.com/
 the most comprehensive Data Mining and Knowledge Discovery
site.
BayMiner EWS for Executives
© Bayes Information Technology Oy 2007
Thank you for your interest!
Bayes Information Technology Ltd.
Porttikuja 3 C
FIN-00940 Helsinki
tel. +358-9-72892680
www.Bayminer.com
CEO Ralf Ekholm
tel. +358-50-5497109
e-mail: [email protected]
We are a Finnish HiTech company.
Tekes (National Technology Agency) has supported development.
Academy of Finland has supported research in Bayesian Networks.
BayMiner EWS for Executives
© Bayes Information Technology Oy 2007