CIAFS 2015 - The Importance of Small Data - FINAL
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The document outlines chapters from a presentation on data analytics, including introductions, definitions of key terms, and case study themes. Some of the case study themes explored include how impactful small data can be, whether largest customers are the most profitable, the value of combining multiple data sources, identifying issues with poor quality or inaccurate data, and how precise data needs to be. The conclusions recommend starting small with projects and data, ensuring high quality small data, and that rough estimates are better than precise but wrong answers. Contact information is provided for any questions.
CIAFS 2015 - The Importance of Small Data - FINAL
- 2. CHAPTERS 1. INTRODUCTION 2. DEFINITIONS 3. CASE STUDY THEMES: I. JUST HOW SMALL CAN SMALL BE? II.ARE BIGGEST CUSTOMERS PROFITABLE? III.THE VALUE OF MASHUPS IV.SHINING A LIGHT ON DARK PLACES V. JUST HOW ACCURATELY DO YOU NEED TO BE WRONG? 4. CONCLUSIONS 5. Q&A
- 5. DISCLAIMER ALL VIEWS ARE MY OWN BASED ON 25 YEARS EXPERIENCE VENDORS MAY NOT LIKE WHAT I SAY! MENTION OF PRODUCTS, TOOLS, SERVICES & COMPANIES SHOULD NOT BE TREATED AS AN ENDORSEMENT (OR A CRITICISM) NAMES HAVE BEEN CHANGED TO PROTECT THE GUILTY! IF YOUD LIKE A COPY OF THE PRESENTATION THEN GET IN TOUCH
- 6. CHAPTERS 1. INTRODUCTION 2. DEFINITIONS 3. CASE STUDY THEMES: I. JUST HOW SMALL CAN SMALL BE? II.ARE BIGGEST CUSTOMERS PROFITABLE? III.THE VALUE OF MASHUPS IV.SHINING A LIGHT ON DARK PLACES V. JUST HOW ACCURATELY DO YOU NEED TO BE WRONG? 4. CONCLUSIONS 5. Q&A
- 7. 2 DEFINITIONS Big Data Small Data Data Discovery
- 8. 2 DEFINITIONS: BIG DATA Any offers?
- 9. 2 DEFINITIONS: BIG DATA THE TECHNOLOGIST VIEW:
- 10. 2 DEFINITIONS: BIG DATA THE STATISTICIAN VIEW: N= ALL
- 11. 2 DEFINITIONS: BIG DATA THE PRACTITIONER VIEW: "Big Data refers to things we can do at a large scale that cannot be done at a smaller one, to extract new insights or create new forms of value, in ways that change markets, organisations, the relationship between citizens and governments, and more" (Big Data: A revolution that will transform how we live, work and think". Viktor Mayer-Schonberger and Kenneth Cukier, John Murray, London, 2013. ISBN: 9781848547933).
- 12. 2 DEFINITIONS: SMALL DATA Any offers?
- 13. 2 DEFINITIONS: SMALL DATA ANY data generated prior to mid 1990s Anything which requires N < ALL When causation > Correlation When a single datapoint matters Anything you dont want to label as Big Data
- 14. 2 DEFINITIONS: DATA DISCOVERY
- 15. 2 DEFINITIONS: DATA DISCOVERY
- 16. Well documented Well documented 2 DEFINITIONS: DATA DISCOVERY - THEORY
- 17. Poorly documented Poorly documented 2 DEFINITIONS: DATA DISCOVERY - PRACTICE
- 18. CHAPTERS 1. INTRODUCTION 2. DEFINITIONS 3. CASE STUDY THEMES: I. JUST HOW SMALL CAN SMALL BE? II.ARE BIGGEST CUSTOMERS PROFITABLE? III.THE VALUE OF MASHUPS IV.SHINING A LIGHT ON DARK PLACES V. JUST HOW ACCURATELY DO YOU NEED TO BE WRONG? 4. CONCLUSIONS 5. Q&A
- 19. ONE LETTER THAT PUT A COMPANY OUT OF BUSINESS TWO DATA ITEMS THAT DROVE BUSINESS INTELLIGENCE ACROSS EUROPE 3 (I) CASE STUDIES JUST HOW SMALL CAN SMALL BE ?
- 20. ONE LETTER THAT PUT A COMPANY OUT OF BUSINESS 3 (I) CASE STUDIES JUST HOW SMALL CAN SMALL BE ?
- 21. ONE LETTER THAT PUT A COMPANY OUT OF BUSINESS 3 (I) CASE STUDIES JUST HOW SMALL CAN SMALL BE ?
- 22. TWO DATA ITEMS THAT DROVE BUSINESS INTELLIGENCE ACROSS EUROPE 3 (I) CASE STUDY ARE BIGGEST CUSTOMERS PROFITABLE ?
- 23. TWO DATA ITEMS THAT DROVE BUSINESS INTELLIGENCE ACROSS EUROPE 3 (I) CASE STUDIES JUST HOW SMALL CAN SMALL BE ?
- 24. TWO DATA ITEMS THAT DROVE BUSINESS INTELLIGENCE ACROSS EUROPE 3 (I) CASE STUDIES JUST HOW SMALL CAN SMALL BE ?
- 25. TWO DATA ITEMS THAT DROVE BUSINESS INTELLIGENCE ACROSS EUROPE 3 (I) CASE STUDIES JUST HOW SMALL CAN SMALL BE ?
- 26. BIG VOLUME = BIG REVENUE = BIG PROFIT ? 3 (II) CASE STUDIES ARE BIGGEST CUSTOMERS PROFITABLE?
- 27. BIG VOLUME = BIG REVENUE 3 (II) CASE STUDIES ARE BIGGEST CUSTOMERS PROFITABLE?
- 28. BIG VOLUME = BIG REVENUE = BIG PROFIT ? 3 (II) CASE STUDIES ARE BIGGEST CUSTOMERS PROFITABLE?
- 29. BIG VOLUME = BIG REVENUE = BIG PROFIT ?......OR NOT! 3 (II) CASE STUDIES ARE BIGGEST CUSTOMERS PROFITABLE?
- 30. GROWING, ACCORDING TO INTERNAL DATA. EXTERNAL DATA SHOWS? 3 (III) CASE STUDIES THE VALUE OF MASHUPS
- 31. GROWING 3 (III) CASE STUDIES THE VALUE OF MASHUPS Month Units Unit Sales per Month Own
- 32. GROWING MARKET SHARE 3 (III) CASE STUDIES THE VALUE OF MASHUPS Month Units Unit Sales per Month Own Month Units Unit Sales per Month Competitor Own
- 33. GROWING MARKET SHARE IN A SHRINKING MARKET 3 (III) CASE STUDIES THE VALUE OF MASHUPS Month Units Unit Sales per Month Own Month Units Unit Sales per Month Competitor Own Month Units Unit Sales per Month Competitor MARKET Own
- 34. IS THIS DATA RIGHT? ARE YOU SURE? REALLY SURE? IF THIS SYSTEM WAS RIGHT WED BE GOING BUST! 3 (IV) CASE STUDIES SHINING A LIGHT ON DARK PLACES
- 35. IS THIS DATA RIGHT? ARE YOU SURE? REALLY SURE? 3 (IV) CASE STUDIES SHINING A LIGHT ON DARK PLACES
- 36. IS THIS DATA RIGHT? ARE YOU SURE? REALLY SURE? 3 (IV) CASE STUDIES SHINING A LIGHT ON DARK PLACES 0 1 2 3 4 5 6 7 8 09:00:00 10:00:00 11:00:00 12:00:00 13:00:00 14:00:00 15:00:00 16:00:00 17:00:00 (blank) Contacts Customer Contacts
- 37. IF THIS SYSTEM WAS RIGHT WED BE GOING BUST! 3 (IV) CASE STUDIES SHINING A LIGHT ON DARK PLACES
- 38. IF THIS SYSTEM WAS RIGHT WED BE GOING BUST! 3 (IV) CASE STUDIES SHINING A LIGHT ON DARK PLACES -10000 -5000 0 5000 10000 15000 20000 25000 30000 Distributor Profitability (Revenue - Rebate) Net Rev Rebate
- 39. ROUGHLY RIGHT VERSUS PRECISELY WRONG 3 (V) CASE STUDIES JUST HOW ACCURATELY DO YOU NEED TO BE WRONG?
- 40. LOSING THE INFORMATION IN THE DATA DASHBOARD DAZZLE ROUGHLY RIGHT VERSUS PRECISELY WRONG 3 (V) CASE STUDIES JUST HOW ACCURATELY DO YOU NEED TO BE WRONG?
- 41. LOSING THE INFORMATION IN THE DATA DASHBOARD DAZZLE 3 (V) CASE STUDIES JUST HOW ACCURATELY DO YOU NEED TO BE WRONG?
- 42. ROUGHLY RIGHT VERSUS PRECISELY WRONG 3 (V) CASE STUDIES JUST HOW ACCURATELY DO YOU NEED TO BE WRONG? -1000 0 1000 2000 3000 4000 5000 6000 7000 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99 Performace(Units) Day Performance - Expectedvs Actual EXPECTED ACTUAL Linear (EXPECTED) Linear (ACTUAL)
- 43. 1. INTRODUCTION 2. DEFINITIONS 3. CASE STUDY THEMES: I. JUST HOW SMALL CAN SMALL BE? II.ARE BIGGEST CUSTOMERS PROFITABLE? III.THE VALUE OF MASHUPS IV.SHINING A LIGHT ON DARK PLACES V. JUST HOW ACCURATELY DO YOU NEED TO BE WRONG? 4. CONCLUSIONS 5. Q&A
- 44. START SMALL SMALL PROJECT, SMALL DATA THE SMALLER THE DATA, THE BIGGER THE IMPORTANCE OF DATA QUALITY ROUGHLY RIGHT IS QUICKER AND BETTER THAN PRECISELY WRONG THE REAL POWER OF ANALYTICS IS WHEN YOU MASH TOGETHER DATA 4 - CONCLUSIONS
- 45. 4 - CONCLUSIONS
- 46. 1. INTRODUCTION 2. DEFINITIONS 3. CASE STUDY THEMES: I. JUST HOW SMALL CAN SMALL BE? II.ARE BIGGEST CUSTOMERS PROFITABLE? III.THE VALUE OF MASHUPS IV.SHINING A LIGHT ON DARK PLACES V. JUST HOW ACCURATELY DO YOU NEED TO BE WRONG? 4. CONCLUSIONS 5. Q&A
- 47. oEMAIL: NUTTALL_GARY@HOTMAIL.COM oTWITTER: @GPN01 oLINKEDIN: HTTP://WWW.LINKEDIN.COM/IN/GARYNUTTALL oMEETUP: MEETUP MASHUP LONDON: HTTP://WWW.MEETUP.COM/MEETUP-MASHUP- LONDON/ oBLOGGER: HTTP://MEETUPMASHUP.BLOGSPOT.CO.UK/ 5 QUESTIONS ?