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Porter introduction-a

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Porter introduction-a

  1. 1. Porter P. C. Lin
  2. 2. Outline <ul><li>Expertise & Major Achievements </li></ul><ul><li>P-S-P Challenges </li></ul><ul><li>Executing Action Plan </li></ul><ul><li>Expecting Contributions </li></ul><ul><li>Q&A </li></ul>
  3. 3. Expertise & Major Achievements page
  4. 4. Expertise: <ul><li>Background includes: IE, Logistics, IT and MBA </li></ul><ul><ul><li>MBA  M.S. of International MBA Program of NCCU </li></ul></ul><ul><ul><li>Logistics  M.S. of NCTU </li></ul></ul><ul><ul><li>IE  B.S. of Fen Chia University </li></ul></ul><ul><ul><li>IT  Oracle ( OCP DBA) & Microsoft (MCSE) Certified </li></ul></ul><ul><li>Comprehensively and Systematically mind set. </li></ul><ul><ul><li>Milestone  JCA successfully from IT to Operation team. </li></ul></ul><ul><ul><ul><li>Operations and People management. </li></ul></ul></ul><ul><ul><ul><li>CT/ IPD Project Management experiences. </li></ul></ul></ul><ul><li>Good practicing with people management theory. </li></ul><ul><ul><li>Effective communication skill. </li></ul></ul><ul><ul><li>Enthusiastic, with Passions for people management. </li></ul></ul>
  5. 5. Major Achievements <ul><li>FY08 APAC VP Purple Promise Award </li></ul><ul><li>People management </li></ul><ul><ul><li>Average SFA is over 4.0 </li></ul></ul><ul><ul><li>GOLD candidate coaching </li></ul></ul><ul><ul><li>Sr. Courier Candidates set </li></ul></ul><ul><ul><li>Part time employees management. </li></ul></ul><ul><li>Breaking the rules by making impossible to be “ I’m possible ” : </li></ul><ul><ul><li>Planning & Executing Early PUP Model in FY10. </li></ul></ul><ul><ul><li>CLH enhancement after CANH launching . </li></ul></ul><ul><ul><li>ASIA - One delay with PM service Level 100%. </li></ul></ul><ul><ul><li>Early push shuttle, during CNY rush hour. </li></ul></ul>
  6. 6. P-P-P Personality Proactive Perseverance Passionate Integrator
  7. 7. P - S - P Challenges
  8. 8. <ul><li>People  </li></ul><ul><ul><li>Efficiency vs. Safety </li></ul></ul><ul><ul><li>Safety Mind Set </li></ul></ul><ul><ul><li>  Common Job Grading </li></ul></ul><ul><ul><li>  Team members turn over   </li></ul></ul><ul><li>Service </li></ul><ul><ul><li>  CANH + CLH Impact+ Flight late arrival </li></ul></ul><ul><ul><li>  Model Ops Re-Enforcing  </li></ul></ul><ul><ul><li>Customer feedbacks </li></ul></ul><ul><ul><li>Compliances and Rules </li></ul></ul>Challenges - Internal & External
  9. 9. <ul><li>Profit </li></ul><ul><ul><li>  Global Economy recovery uncertainly  </li></ul></ul><ul><ul><li>Customization evaluation with Cost surging  </li></ul></ul><ul><ul><li>  Market share varying with Cargo direct flight </li></ul></ul><ul><ul><li>Cross Strait Opportunities </li></ul></ul><ul><li>Others </li></ul><ul><ul><li>People , Service & Profit Equilibrium </li></ul></ul><ul><ul><li>Change management </li></ul></ul><ul><ul><li>Synergy Generating and enduring </li></ul></ul><ul><ul><li>Moral sustain </li></ul></ul>Challenges - Internal & External
  10. 10. Executing Action Plan & Strategy
  11. 11. Executing Action Plan & Strategy : <ul><li>STAR </li></ul><ul><ul><li>S teering T eam by A ctions R einforcing </li></ul></ul><ul><li>Leadership Strategy </li></ul><ul><ul><li>Double E </li></ul></ul><ul><ul><li>B – A – C & U – A – C </li></ul></ul>
  12. 12. - <ul><ul><li>Right person, Right position, Right people </li></ul></ul><ul><ul><li>management </li></ul></ul><ul><ul><li>Disciplines is to keep Ops on Track </li></ul></ul><ul><ul><li>Performance Orienting to hold manager accountable </li></ul></ul>STAR S teering T eam by A ctions R einforcing People Management Cost Management Operation Management Report Customer Relationship Execution
  13. 13. - - <ul><ul><li>Plan annual budget </li></ul></ul><ul><ul><li>with monitoring and </li></ul></ul><ul><ul><li>managing Cost. </li></ul></ul><ul><ul><li>Ensuring all the contract meet Corp compliances. </li></ul></ul><ul><ul><li>Rational cost down </li></ul></ul><ul><ul><li>with performance </li></ul></ul><ul><ul><li>driving. </li></ul></ul>STAR S teering T eam by A ctions R einforcing People Management Cost Management Operation Management Report Customer Relationship Execution
  14. 14. - STAR S teering T eam by A ctions R einforcing <ul><li>Model Ops , Model our way forward! </li></ul><ul><ul><li>Model our way / Model our mindset / Model </li></ul></ul><ul><ul><li>our language </li></ul></ul><ul><ul><li>Planning, Executing, & Fine Tuning </li></ul></ul><ul><ul><li>Cooperate with Dispatch team </li></ul></ul><ul><ul><li>FAMIS insight ! </li></ul></ul><ul><ul><li>Discipline with correct method </li></ul></ul><ul><li>Safety above all </li></ul><ul><ul><li>Efficiency vs. Safety </li></ul></ul><ul><ul><li>Safety culture evolving and enduring </li></ul></ul>People Management Cost Management Operation Management Report Customer Relationship Execution
  15. 15. STAR S teering T eam by A ctions R einforcing <ul><li>Customer Feedback </li></ul><ul><ul><li>Be at cause </li></ul></ul><ul><ul><li>Take it as Opportunity </li></ul></ul><ul><ul><li>Hold Manager accountable </li></ul></ul><ul><li>Align with sales to meet customers’ </li></ul><ul><li>need, & review the necessity </li></ul><ul><li>Review with manager for ad hoc </li></ul><ul><li>customer exceptions </li></ul>People Management Cost Management Operation Management Report Customer Relationship Execution
  16. 16. <ul><li>Value will be created by Synergy with Effective </li></ul><ul><li>Executions </li></ul><ul><li>Keep the balance in mind and in place </li></ul><ul><li>Only focus on what we can change, seeking opportunity of what we can influence </li></ul><ul><li>Safety, Security, Regulatory & Compliance </li></ul><ul><ul><li>Compliance keeps us on track </li></ul></ul><ul><ul><li>Commitments lead us ahead ! </li></ul></ul>STAR S teering T eam by A ctions R einforcing People Management Cost Management Operation Management Report Customer Relationship Execution
  17. 17. Cost Management Effective Executions People Management Customer Relationship Operation Management Balancing Balancing Balancing Balancing
  18. 18. <ul><li>Stretching out “Two Hands” : </li></ul><ul><ul><li>Internally : People management </li></ul></ul><ul><ul><li>Externally : Customer relationship </li></ul></ul><ul><li>Standing firmly with “Two Legs ” </li></ul><ul><ul><li>Operation management </li></ul></ul><ul><ul><li>Cost management </li></ul></ul><ul><li>Effective Executions in “Mind ” </li></ul><ul><ul><li>Effective executions make it works. </li></ul></ul><ul><ul><li>Safety, Security, Regulatory & Compliance </li></ul></ul>STAR S teering T eam by A ctions R einforcing Cost Management Safety Security Regulatory Compliance People Management Operation Management Cost Management Effective Executions People Management Customer Relationship Operation Management Balancing Balancing Balancing Balancing
  19. 19. Leadership Strategy Double E : E xecution & E ngagement <ul><li>Executing by limiting the Knowing - Doing Gap </li></ul><ul><ul><li>The Key element of Execution is to follow up </li></ul></ul><ul><ul><li>Harnessing the power of passion and simplicity to get result </li></ul></ul><ul><ul><li>To distill the most complex issues into simple </li></ul></ul><ul><li>“ E = MC 2 “ </li></ul><ul><ul><li>Engagement = M otivation * C ommunication * C ommitments </li></ul></ul><ul><ul><ul><li>Aligning the common goal </li></ul></ul></ul><ul><ul><ul><li>Motivating by the Value contribution mindset </li></ul></ul></ul><ul><ul><ul><li>Effective communications by managing commitments </li></ul></ul></ul>
  20. 20. Leadership Strategy B - A - C & U-A-C <ul><li>B e A t C ause </li></ul><ul><ul><li>Learning & reading the messages from employees’ interactions. </li></ul></ul><ul><ul><li>Help employees to see what they can see but not see. </li></ul></ul><ul><ul><li>It’s often not the mountain ahead, but the grain of sand in shoes bothering. </li></ul></ul><ul><li>U nderstanding A lignment and C ommitment </li></ul><ul><ul><li>Effective Coaching by Listening ! </li></ul></ul><ul><ul><li>Seek first to understand, then to be understood . </li></ul></ul><ul><ul><li>What you talk does not matter, But What they really listen does matter. </li></ul></ul>
  21. 21. Expecting Contributions
  22. 22. <ul><li>Emerge Internal Synergy by Culture Evolving </li></ul><ul><ul><li>Right People asset Management </li></ul></ul><ul><ul><li>Purple DNA, Purple Mindset </li></ul></ul><ul><li>Create External Synergy by Well aligning </li></ul><ul><ul><li>Effective Communications </li></ul></ul><ul><ul><li>Common Goal Setting </li></ul></ul><ul><li>Get Overall Operations Rationalizing & Balancing </li></ul><ul><ul><li>Effectiveness, Efficiency and Safety </li></ul></ul><ul><ul><li>Model Ops Enforcing </li></ul></ul>Expecting Contribution :
  23. 23. Vision : L - E - D Leveraging , Engaging , & Delivering Synergy Engagement Competence Organization capability Front Line Managers Team’s Value Resources: Revised from HRM Lecture IMBA NCCU SH Lee 2005 Synergy Generating Driving the Force Competitiveness & Value L everaging the Professional E ngaging the People D elivering the Value
  24. 24. Those Robust Bricks Build Up “ Great Wall ” Customer Relationship STAR S teering T eam by A ctions R einforcing B-A-C Execution U-A-C Integrator Engagement Vision L - E - D
  25. 25. page The Porter Promise <ul><li>Leading Team to be a “ AAA ” Diamond Station : </li></ul><ul><ul><li>A ligned the team  A ctively, & Running team to be the most A spiring team </li></ul></ul>
  26. 26. <ul><li>DAMA-NCR </li></ul><ul><li>Tuesday, November 13, 2001 </li></ul><ul><li>Laura Squier </li></ul><ul><li>Technical Consultant </li></ul><ul><li>[email_address] </li></ul>What is Data Mining?
  27. 27. Agenda <ul><li>What Data Mining IS and IS NOT </li></ul><ul><li>Steps in the Data Mining Process </li></ul><ul><ul><li>CRISP-DM </li></ul></ul><ul><ul><li>Explanation of Models </li></ul></ul><ul><ul><li>Examples of Data Mining Applications </li></ul></ul><ul><li>Questions </li></ul>
  28. 28. The Evolution of Data Analysis
  29. 29. Results of Data Mining Include: <ul><li>Forecasting what may happen in the future </li></ul><ul><li>Classifying people or things into groups by recognizing patterns </li></ul><ul><li>Clustering people or things into groups based on their attributes </li></ul><ul><li>Associating what events are likely to occur together </li></ul><ul><li>Sequencing what events are likely to lead to later events </li></ul>
  30. 30. Data mining is not <ul><li>Brute-force crunching of bulk data </li></ul><ul><li>“ Blind” application of algorithms </li></ul><ul><li>Going to find relationships where none exist </li></ul><ul><li>Presenting data in different ways </li></ul><ul><li>A database intensive task </li></ul><ul><li>A difficult to understand technology requiring an advanced degree in computer science </li></ul>
  31. 31. Data Mining Is <ul><li>A hot buzzword for a class of techniques that find patterns in data </li></ul><ul><li>A user-centric, interactive process which leverages analysis technologies and computing power </li></ul><ul><li>A group of techniques that find relationships that have not previously been discovered </li></ul><ul><li>Not reliant on an existing database </li></ul><ul><li>A relatively easy task that requires knowledge of the business problem/subject matter expertise </li></ul>
  32. 32. Data Mining versus OLAP <ul><li>OLAP - On-line Analytical Processing </li></ul><ul><ul><li>Provides you with a very good view of what is happening, but can not predict what will happen in the future or why it is happening </li></ul></ul>
  33. 33. Data Mining Versus Statistical Analysis <ul><li>Data Analysis </li></ul><ul><ul><li>Tests for statistical correctness of models </li></ul></ul><ul><ul><ul><li>Are statistical assumptions of models correct? </li></ul></ul></ul><ul><ul><ul><ul><li>Eg Is the R-Square good? </li></ul></ul></ul></ul><ul><ul><li>Hypothesis testing </li></ul></ul><ul><ul><ul><li>Is the relationship significant? </li></ul></ul></ul><ul><ul><ul><ul><li>Use a t-test to validate significance </li></ul></ul></ul></ul><ul><ul><li>Tends to rely on sampling </li></ul></ul><ul><ul><li>Techniques are not optimised for large amounts of data </li></ul></ul><ul><ul><li>Requires strong statistical skills </li></ul></ul><ul><li>Data Mining </li></ul><ul><ul><li>Originally developed to act as expert systems to solve problems </li></ul></ul><ul><ul><li>Less interested in the mechanics of the technique </li></ul></ul><ul><ul><li>If it makes sense then let’s use it </li></ul></ul><ul><ul><li>Does not require assumptions to be made about data </li></ul></ul><ul><ul><li>Can find patterns in very large amounts of data </li></ul></ul><ul><ul><li>Requires understanding of data and business problem </li></ul></ul>
  34. 34. Examples of What People are Doing with Data Mining: <ul><li>Fraud/Non-Compliance Anomaly detection </li></ul><ul><ul><li>Isolate the factors that lead to fraud, waste and abuse </li></ul></ul><ul><ul><li>Target auditing and investigative efforts more effectively </li></ul></ul><ul><li>Credit/Risk Scoring </li></ul><ul><li>Intrusion detection </li></ul><ul><li>Parts failure prediction </li></ul><ul><li>Recruiting/Attracting customers </li></ul><ul><li>Maximizing profitability (cross selling, identifying profitable customers) </li></ul><ul><li>Service Delivery and Customer Retention </li></ul><ul><ul><li>Build profiles of customers likely to use which services </li></ul></ul><ul><li>Web Mining </li></ul>
  35. 35. How Can We Do Data Mining? <ul><li>By Utilizing the CRISP-DM Methodology </li></ul><ul><ul><li>a standard process </li></ul></ul><ul><ul><li>existing data </li></ul></ul><ul><ul><li>software technologies </li></ul></ul><ul><ul><li>situational expertise </li></ul></ul>
  36. 36. Why Should There be a Standard Process? <ul><li>Framework for recording experience </li></ul><ul><ul><li>Allows projects to be replicated </li></ul></ul><ul><li>Aid to project planning and management </li></ul><ul><li>“ Comfort factor” for new adopters </li></ul><ul><ul><li>Demonstrates maturity of Data Mining </li></ul></ul><ul><ul><li>Reduces dependency on “stars” </li></ul></ul>The data mining process must be reliable and repeatable by people with little data mining background.
  37. 37. Process Standardization <ul><li>CRISP-DM: </li></ul><ul><li>CRoss Industry Standard Process for Data Mining </li></ul><ul><li>Initiative launched Sept.1996 </li></ul><ul><li>SPSS/ISL, NCR, Daimler-Benz, OHRA </li></ul><ul><li>Funding from European commission </li></ul><ul><li>Over 200 members of the CRISP-DM SIG worldwide </li></ul><ul><ul><li>DM Vendors - SPSS, NCR, IBM, SAS, SGI, Data Distilleries, Syllogic, Magnify, .. </li></ul></ul><ul><ul><li>System Suppliers / consultants - Cap Gemini, ICL Retail, Deloitte & Touche, … </li></ul></ul><ul><ul><li>End Users - BT, ABB, Lloyds Bank, AirTouch, Experian, ... </li></ul></ul>
  38. 38. CRISP-DM <ul><li>Non-proprietary </li></ul><ul><li>Application/Industry neutral </li></ul><ul><li>Tool neutral </li></ul><ul><li>Focus on business issues </li></ul><ul><ul><li>As well as technical analysis </li></ul></ul><ul><li>Framework for guidance </li></ul><ul><li>Experience base </li></ul><ul><ul><li>Templates for Analysis </li></ul></ul>
  39. 39. The CRISP-DM Process Model
  40. 40. Why CRISP-DM? <ul><li>The data mining process must be reliable and repeatable by people with little data mining skills </li></ul><ul><li>CRISP-DM provides a uniform framework for </li></ul><ul><ul><li>guidelines </li></ul></ul><ul><ul><li>experience documentation </li></ul></ul><ul><li>CRISP-DM is flexible to account for differences </li></ul><ul><ul><li>Different business/agency problems </li></ul></ul><ul><ul><li>Different data </li></ul></ul>
  41. 41. Phases and Tasks Business Understanding Data Understanding Evaluation Data Preparation Modeling Determine Business Objectives Background Business Objectives Business Success Criteria Situation Assessment Inventory of Resources Requirements, Assumptions, and Constraints Risks and Contingencies Terminology Costs and Benefits Determine Data Mining Goal Data Mining Goals Data Mining Success Criteria Produce Project Plan Project Plan Initial Asessment of Tools and Techniques Collect Initial Data Initial Data Collection Report Describe Data Data Description Report Explore Data Data Exploration Report Verify Data Quality Data Quality Report Data Set Data Set Description Select Data Rationale for Inclusion / Exclusion Clean Data Data Cleaning Report Construct Data Derived Attributes Generated Records Integrate Data Merged Data Format Data Reformatted Data Select Modeling Technique Modeling Technique Modeling Assumptions Generate Test Design Test Design Build Model Parameter Settings Models Model Description Assess Model Model Assessment Revised Parameter Settings Evaluate Results Assessment of Data Mining Results w.r.t. Business Success Criteria Approved Models Review Process Review of Process Determine Next Steps List of Possible Actions Decision Plan Deployment Deployment Plan Plan Monitoring and Maintenance Monitoring and Maintenance Plan Produce Final Report Final Report Final Presentation Review Project Experience Documentation Deployment
  42. 42. Phases in the DM Process: CRISP-DM
  43. 43. Phases in the DM Process (1 & 2) <ul><li>Business Understanding: </li></ul><ul><ul><li>Statement of Business Objective </li></ul></ul><ul><ul><li>Statement of Data Mining objective </li></ul></ul><ul><ul><li>Statement of Success Criteria </li></ul></ul><ul><li>Data Understanding </li></ul><ul><ul><li>Explore the data and verify the quality </li></ul></ul><ul><ul><li>Find outliers </li></ul></ul>
  44. 44. Phases in the DM Process (3) <ul><li>Data preparation: </li></ul><ul><ul><li>Takes usually over 90% of our time </li></ul></ul><ul><ul><ul><li>Collection </li></ul></ul></ul><ul><ul><ul><li>Assessment </li></ul></ul></ul><ul><ul><ul><li>Consolidation and Cleaning </li></ul></ul></ul><ul><ul><ul><ul><li>table links, aggregation level, missing values, etc </li></ul></ul></ul></ul><ul><ul><ul><li>Data selection </li></ul></ul></ul><ul><ul><ul><ul><li>active role in ignoring non-contributory data? </li></ul></ul></ul></ul><ul><ul><ul><ul><li>outliers? </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Use of samples </li></ul></ul></ul></ul><ul><ul><ul><ul><li>visualization tools </li></ul></ul></ul></ul><ul><ul><ul><li>Transformations - create new variables </li></ul></ul></ul>
  45. 45. Phases in the DM Process (4) <ul><li>Model building </li></ul><ul><ul><li>Selection of the modeling techniques is based upon the data mining objective </li></ul></ul><ul><ul><li>Modeling is an iterative process - different for supervised and unsupervised learning </li></ul></ul><ul><ul><ul><li>May model for either description or prediction </li></ul></ul></ul>
  46. 46. Types of Models <ul><li>Prediction Models for Predicting and Classifying </li></ul><ul><ul><li>Regression algorithms (predict numeric outcome): neural networks , rule induction, CART (OLS regression, GLM) </li></ul></ul><ul><ul><li>Classification algorithm predict symbolic outcome): CHAID, C5.0 (discriminant analysis, logistic regression) </li></ul></ul><ul><li>Descriptive Models for Grouping and Finding Associations </li></ul><ul><ul><li>Clustering/Grouping algorithms: K-means, Kohonen </li></ul></ul><ul><ul><li>Association algorithms: apriori , GRI </li></ul></ul>
  47. 47. Neural Network Output Hidden layer Input layer
  48. 48. Neural Networks <ul><li>Description </li></ul><ul><ul><li>Difficult interpretation </li></ul></ul><ul><ul><li>Tends to ‘overfit’ the data </li></ul></ul><ul><ul><li>Extensive amount of training time </li></ul></ul><ul><ul><li>A lot of data preparation </li></ul></ul><ul><ul><li>Works with all data types </li></ul></ul>
  49. 49. Rule Induction <ul><li>Description </li></ul><ul><ul><li>Produces decision trees: </li></ul></ul><ul><ul><ul><li>income < $40K </li></ul></ul></ul><ul><ul><ul><ul><li>job > 5 yrs then good risk </li></ul></ul></ul></ul><ul><ul><ul><ul><li>job < 5 yrs then bad risk </li></ul></ul></ul></ul><ul><ul><ul><li>income > $40K </li></ul></ul></ul><ul><ul><ul><ul><li>high debt then bad risk </li></ul></ul></ul></ul><ul><ul><ul><ul><li>low debt then good risk </li></ul></ul></ul></ul><ul><ul><li>Or Rule Sets: </li></ul></ul><ul><ul><ul><li>Rule #1 for good risk: </li></ul></ul></ul><ul><ul><ul><ul><li>if income > $40K </li></ul></ul></ul></ul><ul><ul><ul><ul><li>if low debt </li></ul></ul></ul></ul><ul><ul><ul><li>Rule #2 for good risk: </li></ul></ul></ul><ul><ul><ul><ul><li>if income < $40K </li></ul></ul></ul></ul><ul><ul><ul><ul><li>if job > 5 years </li></ul></ul></ul></ul>
  50. 50. Rule Induction <ul><li>Description </li></ul><ul><li>Intuitive output </li></ul><ul><li>Handles all forms of numeric data, as well as non-numeric (symbolic) data </li></ul><ul><li>C5 Algorithm a special case of rule induction </li></ul><ul><li>Target variable must be symbolic </li></ul>
  51. 51. Apriori <ul><li>Description </li></ul><ul><li>Seeks association rules in dataset </li></ul><ul><li>‘ Market basket’ analysis </li></ul><ul><li>Sequence discovery </li></ul>
  52. 52. Kohonen Network <ul><li>Description </li></ul><ul><li>unsupervised </li></ul><ul><li>seeks to describe dataset in terms of natural clusters of cases </li></ul>
  53. 53. Phases in the DM Process (5) <ul><li>Model Evaluation </li></ul><ul><ul><li>Evaluation of model: how well it performed on test data </li></ul></ul><ul><ul><li>Methods and criteria depend on model type: </li></ul></ul><ul><ul><ul><li>e.g., coincidence matrix with classification models, mean error rate with regression models </li></ul></ul></ul><ul><ul><li>Interpretation of model: important or not, easy or hard depends on algorithm </li></ul></ul>
  54. 54. Phases in the DM Process (6) <ul><li>Deployment </li></ul><ul><ul><li>Determine how the results need to be utilized </li></ul></ul><ul><ul><li>Who needs to use them? </li></ul></ul><ul><ul><li>How often do they need to be used </li></ul></ul><ul><li>Deploy Data Mining results by: </li></ul><ul><ul><li>Scoring a database </li></ul></ul><ul><ul><li>Utilizing results as business rules </li></ul></ul><ul><ul><li>interactive scoring on-line </li></ul></ul>
  55. 55. Specific Data Mining Applications:
  56. 56. What data mining has done for... Scheduled its workforce to provide faster, more accurate answers to questions. The US Internal Revenue Service needed to improve customer service and...
  57. 57. What data mining has done for... analyzed suspects’ cell phone usage to focus investigations. The US Drug Enforcement Agency needed to be more effective in their drug “busts” and
  58. 58. What data mining has done for... Reduced direct mail costs by 30% while garnering 95% of the campaign’s revenue. HSBC need to cross-sell more effectively by identifying profiles that would be interested in higher yielding investments and...
  59. 59. Final Comments <ul><li>Data Mining can be utilized in any organization that needs to find patterns or relationships in their data. </li></ul><ul><li>By using the CRISP-DM methodology, analysts can have a reasonable level of assurance that their Data Mining efforts will render useful, repeatable, and valid results. </li></ul>
  60. 60. Questions?
  61. 61. Ignatius Hospital

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