Kaggle: Crowd Sourcing for Data Analytics
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The document discusses Kaggle, a crowdsourcing platform that connects companies needing data solutions with data scientists seeking real-world data for project competitions. It outlines various aspects of Kaggle, including its value proposition for both data scientists and organizations, the structure of competitions, community engagement, and revenue streams. Additionally, it emphasizes the growing importance of data science in modern business and the diverse opportunities Kaggle provides across multiple industries.
Kaggle: Crowd Sourcing for Data Analytics
- 1. MT5016 –BUSINESS MODELS FOR HI-TECH PRODUCTS ASTUDYBY, Jeffray Jayaraj Michael (A0119246E) Niha Agarwalla (A0119230U) Nivethan Santhan (A0121887X) Sathishkumar Murugesan (A0133745E)
- 2. CONTENTS Introduction Scope of Activities Value Proposition Customer Selection & Market Value Capture Competitor Analysis Strategic Control 1 2 3 4 5 6 7
- 4. What is Data Science? The newly emerging field that is dedicated to analysing and manipulating unstructured/structured raw data to derive insights and build process, products and alter or develop new business model. Necessary skill-sets ranges from computer science, to mathematics, to knowledge in relevant field. INTRODUCTION Data science
- 5. How Kaggle addresses Data Science? It is almost never the case that any single organization has access to the advanced machine learning and statistical techniques that would allow them to extract maximum value from their data. Meanwhile, data scientists crave real-world data to develop and refine their techniques. Kaggle corrects this mismatch by offering companies a cost effective way to harness the ‘cognitive surplus’ of the world's best data scientists. What does Kaggle use to correct the mismatch? Crowdsourcing –It shares the real time data to specific group of users (data scientists) to come up with the predictive models to solve the problems. INTRODUCTION
- 6. WHY DATA SCIENCE AND ANALYTICS? Organization's are spending an average of 21% of their marketing budget on analytics http://blogs.osc-ib.com/2014/02/ib-student-blogs/data-is-the-new-oil/
- 7. DATA IS THE NEW OIL http://blogs.osc-ib.com/2014/02/ib-student-blogs/data-is-the-new-oil/
- 8. HOW KAGGLE WORKS? Thecompetitionhostpreparesthedataandadescriptionoftheproblem.Heannouncedtheprizepoolforapropersolutiontogetherwithadeadlineforthechallenge. Participantsexperimentwithdifferenttechniquesandcompeteagainsteachothertofindthebestmodels.Afterthedeadlinepasses, thecompetitionhostpaystheprizemoneytothewinner. KaggleConnectistheconsultingpartoftheplatform,whichconnectscompaniestotheeliteoftheKagglecommunity,whomservesolutionsfordifferentdatascienceproblems.
- 9. HOW THE COMPETITIONS WORK? 4. Understand (Data Scientist & Kaggle) 5. Collect (Data Scientist & Kaggle) 6. Data exploration (Data Scientist & Kaggle) 7. Plausibility check (Data Scientist & Kaggle) 8. Model (Data Scientist) 9. Validate (Kaggle –Ensemble approach) 1.Company (customer with problems) 2. Kaggle 3. Organize data (Kaggle) Data scientist Registration 10. Communicating Results Deploy Best solution
- 10. WHICH MODEL TO USE? Countless possible approaches to any data prediction problem. Which to choose?
- 11. HOW KAGGLE SELECTS THE BEST? Competitionsarejudgedbasedonpredictiveaccuracyandobjectivecriteriasetbythecompetitionhost/company. Kagglecomparetechniquesonauniformdatasetwithauniformevaluationalgorithmthatassignspointstoeachsolutionandtheresultsarecategorized. KaggleusesanEnsembleapproachwhichisproventobebettertoassesspredictivemodellingsolutions. Ensemble approach
- 12. HOW COMPETITIONS ARE CATEGORISED? Categories Recruiting Confused?
- 13. HOW COMPETITIONS ARE CATEGORISED? GettingStarted:Publiccompetitionsforbeginnerstoparticipateandinvolvesnocashprize. Customersaremostlynon-profitorganizations. Playground:Publiccompetitionsset-uptobemorefun,quirkyandidea-driven,ratherthantosolveanybusinessorresearchproblems. KaggleProspect:Publiccompetitionsthatdoesn’tusetheleaderboardtodeterminethewinner, andwherethegoalisnotapredictivemodel.ThegoalsofProspectcompetitionsincludedataexploration,analyses,anddatavisualizations. Research:Publiccompetitionswherethecompetitiongoalsareresearch/scientificinnatureorserveapublicgood.Thesecompetitionstendtofocusonambitiousmachinelearningproblemsattheforefrontoftechnology,orproblemswithasignificantsocial-goodaspect.
- 14. Recruiting:Publiccompetitionswherethesponsorsarelookingtohiredatascientistsandusethecompetitiontofindandtestpotentialtalent.Therearenoteams,andeachusermustshowcasetheirindividualwork. Masters:CompetitionsopentoonlyaselecttierofeliteKagglers,orasubsetofthesebyinvitation- onlyorspecialeligibilitycriteria.Thesecompetitionshavesignificantcommercialvalueorsensitivedata. Featured:Publiccompetitionswithsignificantprizemoneymeanttosolvecommercialproblems. Prizewinnersgrantthesponsoranon-exclusivelicensetotheirwork,andwillpresenttheirresultsviaadetailedwrite-up HOW COMPETITIONS ARE CATEGORISED?
- 15. SAMPLE COMPETITION Intel gathered the data of previous NCAA tournament results and fixtures match-up, players data and home and away wins over a period of two decades. First stage is to generate a predictive model to and compare it with the previous tournaments. Target is to use the model to predict the winners of the 2014 NCAA tournaments. Prize money : $15,000 id pred1 pred2 name.x name.y S_507_509 0.245309234288291 0.708999299530187 ALBANY NY AMERICAN UNIV S_507_511 0.015245408147597 0.083965574256572 ALBANY NY ARIZONA S_509_511 0.044761732923018 0.041779131840498 AMERICAN UNIV ARIZONA S_509_512 0.282281213282214 0.185690215492044 AMERICAN UNIV ARIZONA ST S_507_512 0.114997411223728 0.324048786686369 ALBANY NY ARIZONA ST S_511_521 0.846952788682282 0.835060080083856 ARIZONA BAYLOR S_507_521 0.077615865041407 0.28593300082739 ALBANY NY BAYLOR S_509_536 0.304576324006342 0.187324294026667 AMERICAN UNIV BYU S_507_536 0.126407140118714 0.326412371166609 ALBANY NY BYU Predictions :
- 16. TOP COMPANIES INVOLVED In kagglethousand of competition are hosted Competition varieties range from Biology to Finance. Various companies such as Nasa, Microsoft etcand medium sized enterprise host competition. Universities such as Stanford and Harvard even host the competition.
- 17. KAGGLE COMMUNITY Kagglecommunityistheplacewherevariousdatascientistsandexpertsstandonasingleplatformtosharethoughts. Kagglerunsablog“nofreehunch”whereeveryactivityhappeninginkaggle,bestpractices,conferencesandupdatesonrecentdevelopmentsareconstantlyposted. Thecommunityalsohasthetopdatascientistsintheworld,withwhomthecompaniescoulddiscussonthecurrentmodelandtheeffectsofthepredictivemodelsdeveloped. TheJobsBoardisthenewfeaturewherecompany/customerinneedofDataScientistcouldpostanadwiththeirrequirements
- 18. CONTENTS Introduction Scope of Activities Value Proposition Customer Selection & Market Value Capture Competitor Analysis Strategic Control 1 2 3 4 5 6 7
- 19. SCOPE OF ACTIVITIES Kaggle Open source Investors & support Companies Data Scientist Competitionhosts x Data providers x Content development x Software x x Algorithm x x x Evaluation x DataStorage x Marketing x x Licensing x x Readingmaterial x x x Search x Terms x x x
- 20. CONTENTS Introduction Scope of Activities Value Proposition Customer Selection & Market Value Capture Competitor Analysis Strategic Control 1 2 3 4 5 6 7
- 21. VALUE PROPOSITION –KAGGLE KAGGLE has two types of Customer: 1.Data Scientist (who works for the problem) 2.Company/Organizations.(who gives the problem)
- 22. Participation by worlds leading data scientist Many data scientist participate Different minds gives different solutions Kaggleplatform<<< data scientist Ensemble approach Signing of NDA, Background check, Exclusive sets of data scientists VALUE PROPOSITION-COMPANIES
- 23. VALUE PROPOSITION FOR DATASCIENTIST To Big companies such as NASA, Facebook, Microsoft Highly paid jobs in big organizations. Signature track : Data Scientist in Kaggleleader board which gives them recognition in the field of predictive modelling.
- 24. CONTENTS Introduction Scope of Activities Value Proposition Customer Selection & Market Value Capture Competitor Analysis Strategic Control 1 2 3 4 5 6 7
- 25. CUSTOMER SELECTION -MARKET SEGMENTS Companies & Research Organization Data Scientists
- 26. END USERS Corporations and Research Organizations People Kaggle Trend Analytics on Stock Prices Users Subscribe to services based on Kaggle Solutions Direct Indirect
- 27. TARGETED INDUSTRIES Companies & Research Organization Life Sciences Energy Financial Services IT Retail
- 29. TARGETED USERS 100,000 Data Scientists Job Seekers Freelancers
- 30. DATA SCIENTISTS
- 31. https://gigaom.com/2013/07/11/kaggle-now-has-100k-data-scientists-but-whats-a-data-scientist/ KAGGLE : NUMBER OF DATA SCIENTIST 100,000 as of 2013
- 32. KAGGLE’S MARKET Sales Forecasting Stock Forecasting Risk Modelling & Pricing Logistic optimisation Best Process Prediction Inventory Management Traffic Forecasting Energy demand Crime Prediction Tax Social fraud detection Hospital Casualty Demand Private Sectors Public Sectors
- 33. MARKET DRIVERS IT offers a definitive source of competitive advantage across all industries and will offer significant future value. Data is being considered to be the future commodity. Individuals create 70% of data, Enterprises store 80% of the data
- 34. MARKET OPPORTUNITY http://marketing555.wordpress.com/2012/10/02/the-big-and-small-of-data/ Overall $107 Billion Outsourced $43 Billion in 2017
- 35. CONTENTS Introduction Scope of Activities Value Proposition Customer Selection & Market Value Capture Competitor Analysis Strategic Control 1 2 3 4 5 6 7
- 36. KaggleCompetition Community Access % from prize money Company-Open Data Data Scientists Solution Prize Money CURRENT REVENUE STREAM -BUSINESS
- 37. KaggleConnect Top Data Scientist Access Connect Fee Company-Sensitive Data Top 0.5% Data Scientists Money Solution CURRENT REVENUE STREAM -BUSINESS
- 38. CURRENT REVENUE STREAM -EDUCATION Kaggle corp Assignments % Revenue Results in order of marks obtained Student enrolled in the university Question & Data Data model Top universities
- 39. PROPOSED REVENUE STREAM –EDUCATION Contract with online courseware websites like Coursera, edxcould be signed and provide data for students enrolled in specific courses. Singapore government has proposed to introduce data science in high schools as a part of co-curriculum. Kaggle could enter the market to provide a tool for schools.
- 40. PROPOSED REVENUE STREAM –GOVERNMENT ALIASES Kaggle corp Kaggle competition Kaggle connect Government/ Customer Local Data scientist Data available online Job offer Brand value gained as a government recognised platform/organisation for Analytics Prize money % of Prize money Job Data model Has knowledge about the local market Data model + Trust/Privacy Human Resource
- 41. PROPOSED REVENUE STREAM –KAGGLE CONSULTANCY Kaggle corp Kaggle connect Oil & Gas industries/ Customer Raw Data + Challenge Fee for consultancy Top 0.5% of Data Scientist in relevant field Work Kaggle consultancy Job offer Structured data Ownership Data model With good Brand value, trust and adequate human resource availability, Kaggle could enter the field of analytics as a consulting firm. The major field of interest could be Oil & Gas as the data is large, unstructured and sensitive.
- 42. VALUE CAPTURE -KAGGLE PRODUCTS Kaggle Public Competitions Competitions allow organizations to post their data and a specific prediction problem to be answered competitively by the world's best Kaggle Masters Competitions Kaggle provides the same platform as with its public competitions, except that access is limited only to an elite group of Kaggle players Kaggle-in-Class Kaggle-in-Class allows instructors to host data prediction competitions for their students.
- 43. KAGGLE IN LONG TAIL
- 44. CONTENTS Introduction Scope of Activities Value Proposition Customer Selection & Market Value Capture Competitor Analysis Strategic Control 1 2 3 4 5 6 7
- 45. Kaggle Innocentive For users Career Choice with enough competitions Rewarding hobby platform Crowdsourcing, Open innovation,Predictive modelling Open innovation,Research and Development Scope Problems involving Data analytics R&D in various industries Registered Members 100,000 in 3 years 300,000 in 12 years Max Prize money 3million 1 million Numberof Competitions 311(107/year) 1650( 138/year) https://www.kaggle.com/competitions KAGGLEVSINNOCENTIVE Kaggle focuses on problems that are related to data analytics. Kaggle’s data scientist use machine leaning as a methodology to solve these problems. Problems posted in Innocentive are related to R&D, product development generic issues. Ususallycoding stands as the major part of the development. These 2 are different organizations with a different value proposition.
- 46. CONTENTS Introduction Scope of Activities Value Proposition Competitor Analysis Customer Selection Value Capture Strategic Control 1 2 3 4 5 6 7
- 47. More Data scientists attracts more Clients NETWORK EFFECT First mover advantages of internet platforms Clients Data ScientistMore Clients attracts more data scientists
- 48. STRATEGIC PARTNERSHIP & COLLABORATION Strong collaboration with big data companies And Institutions –GE, Google, Facebook, Amazon, WalmartSecure PlatformSecure Platform
- 49. BARRIER FOR ENTRY StrengthenandestablishexclusiverelationshipswithBigdatacompaniesandWorldclassInstitutionswillcreateabarrierforothercompetitorstoenterinthebusiness Patent/tradesecretofbusinessmodelshallbemade
- 50. IP MANAGEMENT Kaggle has a strong IP management IP protected ranking software which is used to choose the best model Ranking software is the key for Appropriability Between the parties, Kaggle is the owner of all Intellectual Property Rights in and to the Website Winner entry will be governed by a separate contract between the winner and the Competition Host All text, graphics, user interfaces, photographs, trademarks, logos and artwork, including the design, structure… licensed by or to Kaggle and is protected by applicable copyright, patent and trademark laws and various other intellectual property rights and unfair competition laws.
- 51. COMPLEMENTARY ASSETS Job Opportunities Data analysis courses and online support Certificate/Credit System: Kaggle can establish a credit system as like the leader board that can leverage a Student to join in a school Complementary Products like T-Shirts for Non-Profit competitions
- 52. Transforming the inefficient market for technical talent into the world’s largest meritocracy.
- 53. 1. INTRODUCTION “I keep saying the sexy job in the next ten years will be statisticians.” Hal Varian Google Chief Economist 2009 “Aim to make Data Science a Sport.” Anthony Goldbloom Kaggle Founder 2012
- 54. THANK YOU