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Information and Service Management (ISM) 57E99902 Master’s thesis seminar

Information and Service Management (ISM) 57E99902 Master’s thesis seminar. Merja Halme Timo Kuosmanen Pekka Malo . Statisticians ’ proverb. " Too much information kills the information and leads to misinformation ". Examples of Career Paths.

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Information and Service Management (ISM) 57E99902 Master’s thesis seminar

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  1. Information and Service Management (ISM) 57E99902 Master’sthesisseminar Merja Halme Timo Kuosmanen Pekka Malo

  2. Statisticians’ proverb "Too much information kills the information and leads to misinformation"

  3. Examples of Career Paths MattiKoivu, Executive at Financial Supervisory Authority MikkoSyrjänen, Executive at Wärtsilä Mari Aalto, Analyst at Customer Experience Unit, Itella MikkoSarkkinen, Consultant PirjaHeiskanen, Vice President, Trading and Industrial Intelligence, Fortum Veli-Pekka Heikkinen, Head of Portfolio Management, eQ Asset Management

  4. Celebrities Terhi Majasalmi, appearing in tv series on personal budget management Sari Multala, world champion in sailing

  5. Familiargraduates Jussi Pajunen, city mayor Sirpa Pietikäinen, MEP

  6. Thesis titles from the past few years Media analytics: predicting returns with market sentiment Employing Data Mining in the Customer Relationship Management of a catalogue company Efficiency of retailers of Aro Corporation employing Data Envelopment Analysis

  7. Thesis titles from the past few years Determining the ROI of a comprehensive CRM service - Case ItellaAsiakkuusmarkkinointi Menu planning of restaurants – integer optimization approach Use of self-organized maps in customer segmentation

  8. Thesis titles from the past few years Fair allocation of costs in a joint project - Case HUS Subjective vs. objective methods in product cost forecasting Segmentation of Finnishgamblers Identification of fields of improvement in an internet-based gift store employing conjoint analysis

  9. Getting started with your thesis … Data collection • Survey data • Company data (e.g. customer data) • Proprietary data banks • Streaming online data, etc. Analysis software • SPSS, SAS, R, RapidMiner, etc. Guidance for statistical inference

  10. Questionnaires • Translate the info needed into questions • Mustmake the respondentinvolved and finish the interview • Minimize the responseerror Questionnaires

  11. Questionnairemustbebased on previousresearch • Donot design a questionnairebeforecarefullycheckingwhathasbeenwritten on the topic, definingyourtheoreticalframework, studyinghowyoucanutilizeprevioussurveys (standardquestionsets, e.g. in marketing, psychology… ) • And getacquainted with questionnairetechniques Questionnaires

  12. Importantpoints in questionnaireplanning 1/2 • What is being studied and why • What is the information needed • What is the inverviewing method • Content of individual questions • Question structure Questionnaires

  13. Importantpoints in questionnaireplanning 2/2 • Questionwording • How to order the questions • Form and layout • Eliminateproblemsbypilot-testing • How to overcomeinability and unwillingness to answer Questionnaires

  14. Whatkind of scales? The following is called the Likert scale. Which SCALE in our categorization? For me it is more important to aim at a job with a substantial salary than a job which is interesting. -3 Completely disagree • -2 Strongly disagree • -1 Somewhat disagree •  0 Neither disagree nor agree •  1 Somewhat agree •  2 Strongly agree •  3 Completely agree Questionnaires

  15. Seeexamplesurvey • http://www.mmstudy.net/openg/openglogin.html • It is a good idea to useverydifferenttype of questions – REMEMBER TO ASK THE SAME THING REPEATEDLY FOR THE SAKE OF INCREASING RELIABILITY Questionnaires

  16. Electronic questionnaire software • The studyused SSI Web • The schoolhasWebropol Questionnaires

  17. Statistical software • Such as SAS EG or SAS Jmpor SPSS is the convenientway to do data analyses Database Marketing coursepagesinclude an introduction to SAS basicfunctions Questionnaires

  18. One wayfrequencies • E.g. • How many responders have had more than one year of work experience? • Which percentage of responders is men? Questionnaires

  19. Crosstabling E.g. • Create a cross table of gender and work experience? Does it seem that either males or females tend to have more working experience? • TESTING if the gender and working experience are independent/dependent Questionnaires

  20. Comparingmeans • Calculate the means of WAGE for men and women and compare the Likert scale variable averages TESTING the equality of means?! Questionnaires

  21. WE WILL HELP YOU • To makeyourownquestionnaires,interviews and analyses. The software is available and the websurveysareeasy to send and collect the data. Wewillassistyou with the analyses. • However, useyourtime to considerwhat to measure, how to ask, whatscales to use, how to takemissingvalues and how to achieve a goodsample. Questionnaires

  22. Analysis of Public &Company Data • Typically the data usedearlierhasoriginatedfromcustomerdatabasesorithasbeenweb data (registeredusersorcookies) • Alsootherkinds of company data arepossible, e.g. in the numerousthesis made to assessefficiency of e.g. differentdepartments in a company • Data alsofrompublicsources, e.g. Statistics Finland

  23. Company and Public Data – exampletoolsused • Econometricmodels • Forecasting • Efficiencyanalysis (e.g. DEA) • GameTheory • Optimization and Simulation • Data Mining (predictivemodeling, segmentation, association analysis etc.)

  24. What is Multivariate Data Analysis? Multivariatestatistics ~ statisticalmethodsthatsimultaneouslyanalyseseveralmeasurements on eachdata-case (e.g. individual) underinvestigation. Analysingsituationswhenyouhave • Manyindependent (i.e. explanatory) variablesand/or • Manydependent (i.e. response/explained) variables • Varyingdegrees of correlationsbetweenvariables

  25. WhyMultivariateStatistics? Difficulty of addressingcomplicatedresearchquestionswithunivariatetools Severaldrivers for increasingpopularity, e.g. • Availability of nicelypackaged software • Greatercomplexity of contemporaryresearch • Largeamounts of data • Emergence of data miningperspective (findingunforeseenpatterns and associations)

  26. Quantitative Qualitative Confirmatory Qualitative & QuantitativeResearch Explorative

  27. Hypothesis Population Data (Random Sample) Statistical Analysis Verification New Hypothesis / Theory Confirmatory Analysis Theory / Pre-Knowledge

  28. Data Analysis Statistical Analysis New Theory / Invention Population New Data (Random) Statistical Analysis Verification New Hypothesis / Theory Exploratory Analysis Data (Sample)

  29. Broad Topics Business Analytics Data mining Optimization Multi-criteria decision making Productivity and efficiency analysis

  30. What is Data Mining? Discovery of knowledge from data in human-understandable form. Automatedextraction of predictiveinformation “Non-trivial extraction of implicit, previously unknown, and potentially useful information from data.” - Tan, Steinbach & Kumar : Introduction to Data Mining

  31. From Data to Intelligent Business Decisions Source:Tan, Steinbach & Kumar: Introduction to data mining

  32. Challenge of Big Data • DB • systems Artificial Intelligence Data Mining is a rapidlygrowingfieldwithin Business Intelligence Challenges: • highdimensional data • massivedatasets • variety of data types (numbers, images, text, etc.) • heterogenous and distributed data Data Mining is a marriage of manydisciplines

  33. Common Data Mining Tasks Clustering Association Data (in different forms) Classification Regression Sequence analysis Outlier analysis

  34. Applications and currentresearch Applications: marketanalysis, productrecommendation, shoppingbasketanalysis, economicforecasting and trendrecognition Currentresearchprojects: Financial sentimentanalysis Textmining for economictendencies Decisionmaking and performance in business simulations

  35. Data Mining Tools at Aalto BIZ

  36. Optimization

  37. Multi-criteria Decision Making

  38. Productivity & efficiencyanalysis Typicalresearchquestions: How organizationscouldutilizetheirresourcesmoreefficiently? Whichunitsperformwell? How muchinefficientunitscouldsaveresourcesorincrease output? How performancehasevolvedovertime? Applications: education, health care, energy, finance, agriculture, transport, utilities etc. Related fields: economics, management science, operations research, public administration.

  39. Productivity & efficiencyanalysis Currentresearch at ourgroup: Stochasticnonparametricenvelopment of data (StoNED) • Modelingrisk and heteroscedasticity • Jointproduction of multipleoutputs • Non-convexfrontiers • Computationalalgorithms Currentapplications: Regulation of electricitydistribution Performance of a bankbranchnetwork Green growth in agriculture (OECD project)

  40. Quantitative Finance Related Topics Evaluation of investment projects; e.g., investments in IT, services, etc. How valuable is a firm? Mergers and acquisitions Accounting for uncertainty in strategy decisions: major risk analysis Use of options and other instruments for risk management; real options ALM (asset-liability management) in insurance and banking Optimization of an investment portfolio (real or financial) Behavioral decision making; e.g., consumer choice behavior, private investment behavior Emissions trade and environmental protection And many more …

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