We think you have liked this presentation. If you wish to download it, please recommend it to your friends in any social system. Share buttons are a little bit lower. Thank you!
Presentation is loading. Please wait.
Published byJayden Weber
Modified over 4 years ago
BayMiner EWS for Executives © Bayes Information Technology Oy 2007 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. 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.BayesITs 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 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: firstname.lastname@example.org@bayesit.com We are a Finnish HiTech company. Tekes (National Technology Agency) has supported development. Academy of Finland has supported research in Bayesian Networks. Thank you for your interest!
BayMiner QVM for Executives © Bayes Information Technology Oy 2007 How to profit from your investments in data collection systems Identify the root causes.
SOMA2 – Drug Design Environment. Drug design environment – SOMA2 The SOMA2 project Tekes (National Technology Agency of Finland) DRUG2000 program.
© ABR Innova Oy, 2004 Gulliver in the land of giants? Internationalisation of the r&d of a small enterprise Markku Rajala President, ABR Innova.
The Social Buddies Application Jalele Achour External Consultant in Social CRM Powered by.
With Folder HelpDesk for Outlook, support centres and other helpdesks can work efficiently with support cases inside Microsoft Outlook. The support tickets.
Data Mining with R/ORE Minming Duan. 2 iTech Solution Profile Agenda R/ORE Overview 1 XML output generation using SQL 4 Integration with IBP and BIEE.
Cutting-edge technology for the development of business software applications Takes advantage of the most recent international trends, combining Microsoft.NET.
IT Analytics for Symantec Endpoint Protection
Building an EMS Database on a Company Intranet By: Nicholas Bollons Sally Goodman.
Infor Integrated Business Planning
Bayesian Network and Influence Diagram A Guide to Construction And Analysis.
Machine Learning and Data Mining Course Summary. 2 Outline Data Mining and Society Discrimination, Privacy, and Security Hype Curve Future Directions.
EXPERT SYSTEMS apply rules to solve a problem. –The system uses IF statements and user answers to questions in order to reason just like a human does.
Coordinate implementation of customer service strategies Lecture 2 Payman Shafiee.
Report on Intrusion Detection and Data Fusion By Ganesh Godavari.
CS 589 Information Risk Management 23 January 2007.
© 2002 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin TURNING MARKETING INFORMATION INTO ACTION.
Quantum Technology Essential Question:
Review 4 Chapters 8, 9, 10.
Factors in B2B Buying Behavior Process Stages; see diagram below Players: roles in “Buying Center” gatekeepers, users, influencers, deciders, purchasers.
© 2018 SlidePlayer.com Inc. All rights reserved.