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Ralf Ekholm CEO Bayes Information Technology Ltd.

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. What is it all about?. The data analysis market is changing:

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Ralf Ekholm CEO Bayes Information Technology Ltd.

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  1. 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.

  2. What is it all about? • The data analysis market is changing: • Data mining is not sufficient anymore. • Classic reporting is replaced by predictive analytics. • 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.

  3. 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.

  4. 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.

  5. 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.

  6. Predicting risk using BayMiner EWS,the steps during the development phase • Collect in a table the essential data about realized projects. • BayesIT’s experts process it and produce a model of the risks. • Projects are grouped according to how well they have materialized, using true multi-dimensional modelling. • All variables (up to tens) and their values are considered simultaneously. • The resulting risk model is used to steer traffic lights for clear communication to the end user. • These traffic lights combined with a questionnaire on your intranet functions as an on-line risk screening application.

  7. Predicting risk using BayMiner EWS,steps during the use. • Key in known data about a new project (approx 15 questions). • Observe how the traffic lights light up. • Green=ok, yellow=more data required, red=forbidden to tender. • During development phase you may do off-line analysis: • Observe how a new project positions in relation to the other projects. • If the new project locates itself among the weak ones, it is very likely that the new project will not succeed either. • Alternatively predict unknown values using the Profile in BayMiner Pro: • Select a number of similar cases (near the one under study). • You get the prediction for variables whose values are not known.

  8. 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.

  9. 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: ralf.ekholm@bayesit.com • We are a Finnish HiTech company. • Tekes (National Technology Agency) has supported development. • Academy of Finland has supported research in Bayesian Networks.

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