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

3. 1 - INTRODUCTION

4. 1 - INTRODUCTION

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 YOU’D 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 1990’s
• Anything which requires N < ALL
• When causation > Correlation
• When a single datapoint matters
• Anything you don’t 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 WE’D 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 WE’D BE GOING BUST!
3 (IV) – CASE STUDIES – SHINING A LIGHT ON DARK PLACES

38. IF THIS SYSTEM WAS RIGHT WE’D 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 ?