The DATA RING - A canvas for DATA PROJECT
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The document presents an overview of the 'Data Ring', a collaborative initiative by Top-ix aimed at fostering innovation through big data projects and education. It highlights the evolution and challenges of big data, including data skills, infrastructure, and interpretability issues, while promoting the Big Dive training program designed to enhance technical skills in data science. The document also introduces the 'Data Ring' canvas, a framework for managing data projects, encouraging collaboration and refinement.
![A BABEL OF (CODING) LANGUAGES
PRODUCTION
JAVA, C, C++, …
DATA-DRIVEN
PROTOTYPE
PYTHON, R, D3.JS
REFACTORING
-
DATA
ENGINEERING
[ SCALA, …]](https://image.slidesharecdn.com/bdfrontiers2017public-170925064915/85/The-DATA-RING-A-canvas-for-DATA-PROJECT-19-320.jpg)
The DATA RING - A canvas for DATA PROJECT
- 1. THE DATA RING: A CANVAS FOR BIG DATA PROJECTS September 21th - Frontiers Conference 2017
- 3. AGENDA i. A few words about us (TOP-IX & BIG DIVE course). ii. BIG DATA opportunities, beyond the buzzword. iii. Open challenges in applied Data Science. iv. A canvas “Ring” to rule them all…
- 4. 80+ Members (15 in 2003) NON PROFIT CONSORTIUM PUBLIC & PRIVATE PARTICIPATION MISSION TO FOSTER INNOVATION BY LEVERAGING INFRASTRUCTURE ASSETS EDUCATION START-UP CORPORATE INNOVATION CIVIC TECH FUNDED PROJECTS IX NORTH-WEST ITALY DP 7 collaborators 16 employees 2 directors TOP-IX CONSORTIUM
- 5. OUR ACTIVITIES ABOUT DATA
- 6. 010100100110100100 0001010101001001 010101001001001 011101010101010 101010101010100 010101001010101 101010101010010 01010101010100 BIG DIVE HAS BEEN DESIGNED AS AN INTENSIVE TRAINING PROGRAM AIMED AT BOOSTING THE TECH SKILLS IN ORDER TO EXTRACT VALUE FROM DATA AND TO GENERATE IMPACT. 010100100110100100 0001010101001001 010101001001001 011101010101010 101010101010100 010101001010101 101010101010010 01010101010100 01010101010010 010101001010101 WHAT IS BIG DIVE ?
- 7. 4 weeks 20 divers 8 countries 2012 5 weeks 15 divers 5 countries 2013 5 weeks 15 divers 6 countries 2014 2015 5 weeks 19 divers 6 countries 2017 5 weeks 22 divers 5 countries 2016 5 weeks 20 divers 6 countries THE BIG DIVE HISTORY
- 9. marketing quantitative technology VS qualitative innovation process research business THE TERM AMBIGUITY YES, BIG DATA IS A “BUZZWORD” Say BIG DATA again!
- 10. WHAT’S “NEW” ABOUT DATA DATA SKILLS TO EXTRACT INFORMATION ARE NOW MORE ACCESSIBLE INFRASTRUCTURE AS A COMMODITY / Cloud / HPC & HPN / Frameworks CULTURE & APPROACH / Complexity science / Network thinking / Open Innovation DATA AVAILABILITY / Exponential growth / Machine VS human / Structured VS un-structured
- 11. BIG DATA + ML = The NEW STACK Big Data technologies are used to handle core data engineering challenges, and machine learning is used to extract value from the data.
- 12. COMMON OPEN CHALLENGES /THE DATA /THE SKILLS /FROM PROTOTYPE TO… /THE RESULTS INTERPRETATION AND THE EXPLAINABILITY ISSUE /“GREY ZONES” IN DATA EXPLOITATION /THE PURSUIT OF INNOVATION
- 14. DATA REMAINS THE STARTING POINT Metadata Features Selection Refers to the process of extracting useful information (or features) from existing data. “Data” that provides information about other data. {Descriptive, Structural, Administrative} Volume The effective amount of usable data. No a-priori objective parameters. On field validation is required.
- 15. ABOUT FEATURES… FROM SOURCE DATA TO RELEVANT DATA Noisy or redundant data makes it more difficult to discover meaningful patterns. High-dimensional dataset requires more complex models/algorithms and more computational power. Features “reduction” Data augmentation Enriching existing data with open data or through third-party data providers.
- 16. THE DATA TEAM CHALLENGE #2
- 17. HARD (TECH) SKILLS SOFT (HUMAN) SKILLS STATS, MATH SKILLS SECTOR, VERTICAL SKILLS Danger zone Re-arrangement of the THE DATA SCIENCE VENN DIAGRAM by Drew Conway CODING SKILLS STILL LOOKING FOR UNICORNS
- 19. A BABEL OF (CODING) LANGUAGES PRODUCTION JAVA, C, C++, … DATA-DRIVEN PROTOTYPE PYTHON, R, D3.JS REFACTORING - DATA ENGINEERING [ SCALA, …]
- 21. THE EXPLAINABILITY ISSUE LOW EXPLAINABILITY HIGH EXPLAINABILITY Inferential statistics Machine learning Deep learning
- 23. THE DARK SIDE OF BIG DATA /DATA DEMOCRACY VS BIG DATA OLIGARCHY /CORRELATION DOES NOT MEAN CAUSATION /BUBBLE FILTERS /BIAS /HUMAN RIGHTS
- 25. THE GRID VS THE COMPLEXITY
- 26. LEVERAGING (BIG) DATA OPPORTUNITIES REQUIRES A PROPER METHOD
- 27. THE DATA RING
- 28. THE CANVAS APPROACH The inspiring precursor The Data Ring
- 29. DATA RING CANVAS
- 30. DOWNLOAD, USE, COMMENT, REFINE IT (CC LICENSE)
- 31. Project name: Designed by: Date: Version: (D ata)output Data Infrastructure GOAL(S) SKI LLS PROCESSO VALORIZZAZI O NE TOO LS Data input Implementation T uning Interpretation VAL UE Execution Planning Data strategy Data skills O therskills Benchmark Metrics Budget & timing Outsourcing Data governance Exploration Hypothesis Datapreparation Dataprocessing Validation Iteration Accessibility Format Metadata Features DatalakeFramework Storage Computing Coding Dataengineering (Applied)Datascience Dataviz Business Legal Social science Sector expertise PRO C ESS