Data Driven Economy @CMU
•
4 likes•445 views
The document discusses the rise of big data and data science. It notes that as data has grown exponentially from various sources like social media and IoT devices, new tools like Hadoop and data lakes have been developed to manage large, complex data. It also discusses the roles of data scientists and data engineers on data science teams and provides examples of how companies in various industries like retail, transportation and manufacturing are using big data and analytics for applications like fraud detection, recommendation systems, and predictive maintenance. The document advocates for organizations to build cross-functional data teams and develop a data-driven culture focused on training employees to extract insights from data.
Recommended
Recommended
More Related Content
What's hot
What's hot (20)
Similar to Data Driven Economy @CMU
Similar to Data Driven Economy @CMU (20)
More from Komes Chandavimol
More from Komes Chandavimol (7)
Recently uploaded
Recently uploaded (20)
Data Driven Economy @CMU
- 1. The Data-Driven Economy By Komes Chandavimol May 12, 2019
- 2. Komes (Big) Data Career
- 3. https://www.youtube.com/watch?v=jHNetehfW9w How will you work in 2030? Copyright 2019 Komes Chandavimol. All Rights Reserved
- 4. Big Data Around us Copyright 2019 Komes Chandavimol. All Rights Reserved
- 5. Big Data around us Copyright 2019 Komes Chandavimol. All Rights Reserved
- 6. Big Data http://www.adweek.com/prnewser/how-many-times-do-the-worlds-social-media-users-click-every-minute/117427 https://www.domo.com/learn/data-never-sleeps-3-0 Copyright 2019 Komes Chandavimol. All Rights Reserved
- 7. Internet of Things http://topmanagement.com.mx/innovacion-social-y-empresarial-objetivo-de-hitachi/
- 10. Challenge in Big Data Size Speed Complexity Quality
- 12. http://thebigdatablog.weebly.com/blog/the-hadoop-ecosystem-overview Copyright 2019 Komes Chandavimol. All Rights Reserved The Big Data Tools
- 13. The Big Data Tools
- 14. 14 http://hortonworks.com/blog/optimize-your-data-architecture-with-hadoop/ Traditional Data Management Architecture
- 15. 15 http://hortonworks.com/blog/optimize-your-data-architecture-with-hadoop/ New Data Management Architecture
- 16. 16 https://www.digitalnewsasia.com/business/forget-data-warehousing-its-data-lakes-now Data Lake
- 17. How the Data Lake works? 17 http://www.clearpeaks.com/blog/category/tableau Traditional Enterprise Data warehouse Copyright 2019 Komes Chandavimol. All Rights Reserved
- 18. The Big Data Landscape
- 19. Big Data + Analytics = Value
- 20. Big Data Analytics 20 http://dataofthings.blogspot.com/2014/04/the-bbbt-sessions-hortonworks-big-data.html Copyright 2019 Komes Chandavimol. All Rights Reserved
- 22. Data New Analytic Insights (Information, knowledge, data story) Data Product + VisualizationMass Analytic Tools Data Mining/Machine Learning Recommender systems Complex Event Processing Data Science Team Data Scientist Picture:http://www.clipartpanda.com/categories/scientist-clip-art The Roles of Data Science Doing Data Science by O'Neil et al (2013) Datafication Copyright 2019 Komes Chandavimol. All Rights Reserved
- 23. The Rise of Data Scientist 23 http://flowingdata.com/2009/06/04/rise-of-the-data-scientist/ 2009 https://hbr.org/
- 24. Data Science Experience Sharing, Big Data Challenge #2,Bangkok Thailand http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram What is Data Science?
- 25. 25http://hrb.org http://www.anlytcs.com/2014/01/data-science-venn-diagram-v20.html 2014 The Rise of Data Scientist
- 26. Data Science Experience Sharing, Big Data Challenge #2,Bangkok Thailand http://www.anlytcs.com/2014/01/data-science-venn-diagram-v20.html 2014 The Data Science
- 27. The Team of Data
- 28. 28 Data (Science) Team Doing Data Science by O'Neil et al (2013)
- 29. 29 Doing Data Science by O'Neil et al (2013)
- 30. 30 Data Science Team Data Scientist & Data Engineer http://www.kdnuggets.com/2015/11/different-data-science-roles-industry.html
- 31. 31 Data Science Team Data Scientist & Data Engineer http://www.kdnuggets.com/2015/11/different-data-science-roles-industry.html https://www.facebook.com/DataScienceTh/posts/931828353527079:0
- 33. 33DataInquest – Predictive Analytics and Data Science Bootcamp
- 34. Data Science is a Team Sport http://www.ibmbigdatahub.com/blog/why-data-science-team-sport
- 35. Big Data Use Cases By Data-Driven Team
- 36. HR Analytics – Google Hiring Data sources: Historical hiring attributes Data products: Predictive model – recruiting high performer Behavioral Test Situational Test GPA Brain Teaser Good School http://www.kornferryinstitute.com/briefings-magazine/summer-2014/big-data-predictive-analytics-and-hiring Credit: Jarun Ngamvirojcharoen
- 37. Fraud Detection Data sources: historical pattern of transaction data Data products: predictive models – fraud/non-fraudhttps://bluefishway.com/2013/09/13/panic-oh-no-not-again/ http://blogs.wsj.com/cio/2015/08/25/paypal-fights-fraud-with-machine-learning-and-human-detectives/ Credit: Jarun Ngamvirojcharoen
- 38. Recommendation System Data sources: Click Streams, Customer Behaviors Data products: Prescriptive analytics – Personalized Marketing, Recommended Pages Personalized Shopping Experience Data sources: Click Steam, Customer History, Call Center ID Data products: Omni- Channel Prediction, Customer Journey
- 39. Rolls-Royce Data Labs Data Scientist, Visualization Specialist, Data Engineer, Business Analytics https://www.youtube.com/watch?v=AOdH9aZVdaE
- 40. Monitoring and Maintenance Data sources: IoT Sensors in factory Data products: predictive maintenance models http://www.electrex.it/en/news/600-automated-energy-management-system-a-enms-for-cement-production-plants.ht http://www.digitalistmag.com/digital-economy/2015/12/01/iot-digitization-reinforce-cement-industry-03814141 Credit: Jarun Ngamvirojcharoen
- 41. Fuel Saving Data sources: Telematics (sensor), GPS Data products: Prescriptive analytics – route optimization, predictive maintenance (parts/malfunction) http://www.cnet.com/news/ups-turns-data-analysis-into-big-savings/ http://www.cnet.com/news/ups-turns-data-analysis-into-big-savings/ Credit: Jarun Ngamvirojcharoen
- 42. Customer Engagement Data products: Customer Segmentation, Marketing Effectiveness
- 43. More Data-Driven Use Cases
- 44. Customer Engagement + Supplier Chain + Location Data sources: Mobile App, Loyalty Program, GIS Data products: Buying behavior analysis, coupon-response model , location optimizationhttp://www.fastcompany.com/3020859/most-creative-people/how-chinas-one-child-policy-forced-starbucks-to-rethink-its-beijing-sto https://digit.hbs.org/submission/starbucks-driving-customer-engagement-via-digital-innovation/ Credit: Jarun Ngamvirojcharoen
- 45. Menu Design and Optimization Data sources: Customer Activities, Local consumption Data products: Personalized Coffee and customized products
- 46. Location Analytics Data sources: Customer Sales Activities, Loyalty Program, GIS Data products: Real-Estate Decision, Location expansion/closure https://qz.com/334269/what-starbucks-has-done-to-american-home-values/
- 47. Personalized Attentions Data sources: Customer Sales Activities, Loyalty Program, GIS Data products: Customer Experience, Customer Network
- 48. More Data-Driven Use Cases
- 49. Build a Data Team1. Fix the basics Hybrid Analytics Team De-centralized Analytics TeamCentralized Analytics Team DA BU DA BU IT DS BA BU IT IT DA BA DT DM DM DS BADT DSDM Recommended option BADT Adapt a new ways of working 2.
- 50. How Data Team works? BABA DS A data-driven Sprint A Sprint takes 3-4 weeks to deliver product to customers and continue to improve both new feature and data Insights A New way of working Starting with problems, identify the possible solutions and develop the products
- 51. The Next Step is to build Data Culture 3 – 24 months DA DSDM BA DA DSDM BADT Expand A team2.Build A team1. 12 – 18 months Build a culture3.
- 52. The Next Step is to build Data Culture Focusing on training everyone to be a Data Champions
- 53. Workshops Analytics Training Data Team Big Data Tools Training Progress Vendor SupportIT Support Try and Error AI The Next Step is to build Data Culture Focusing on training everyone to be a Data Champion (2 months)
- 54. Data Team Everyone Can do Analytics! Champion #1 1 2 3 Champion #2 Transform to Data-Driven Culture
- 55. How can we start?