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CHAPTERS1. 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
1 - INTRODUCTION
1 - INTRODUCTION
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
CHAPTERS1. 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
2 – DEFINITIONS
• Big Data
• Small Data
• Data Discovery
2 – DEFINITIONS: BIG DATA
• Any offers?
2 – DEFINITIONS: BIG DATA – THE TECHNOLOGIST VIEW:
2 – DEFINITIONS: BIG DATA – THE STATISTICIAN VIEW:
N= ALL
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).
2 – DEFINITIONS: SMALL DATA
• Any offers?
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
2 – DEFINITIONS: DATA DISCOVERY
2 – DEFINITIONS: DATA DISCOVERY
Well
documented
Well
documented
2 – DEFINITIONS: DATA DISCOVERY - THEORY
Poorly
documented
Poorly
documented
2 – DEFINITIONS: DATA DISCOVERY - PRACTICE
CHAPTERS1. 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
• 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 ?
ONE LETTER THAT PUT A COMPANY OUT OF BUSINESS
3 (I) – CASE STUDIES – JUST HOW SMALL CAN SMALL BE ?
ONE LETTER THAT PUT A COMPANY OUT OF BUSINESS
3 (I) – CASE STUDIES – JUST HOW SMALL CAN SMALL BE ?
TWO DATA ITEMS THAT DROVE BUSINESS INTELLIGENCE ACROSS EUROPE
3 (I) – CASE STUDY – ARE BIGGEST CUSTOMERS PROFITABLE ?
TWO DATA ITEMS THAT DROVE BUSINESS INTELLIGENCE ACROSS EUROPE
3 (I) – CASE STUDIES – JUST HOW SMALL CAN SMALL BE ?
TWO DATA ITEMS THAT DROVE BUSINESS INTELLIGENCE ACROSS EUROPE
3 (I) – CASE STUDIES – JUST HOW SMALL CAN SMALL BE ?
TWO DATA ITEMS THAT DROVE BUSINESS INTELLIGENCE ACROSS EUROPE
3 (I) – CASE STUDIES – JUST HOW SMALL CAN SMALL BE ?
• BIG VOLUME = BIG REVENUE = BIG PROFIT ?
3 (II) – CASE STUDIES – ARE BIGGEST CUSTOMERS PROFITABLE?
BIG VOLUME = BIG REVENUE…
3 (II) – CASE STUDIES – ARE BIGGEST CUSTOMERS PROFITABLE?
BIG VOLUME = BIG REVENUE = BIG PROFIT ?
3 (II) – CASE STUDIES – ARE BIGGEST CUSTOMERS PROFITABLE?
BIG VOLUME = BIG REVENUE = BIG PROFIT ?......OR NOT!
3 (II) – CASE STUDIES – ARE BIGGEST CUSTOMERS PROFITABLE?
• GROWING, ACCORDING TO INTERNAL DATA. EXTERNAL DATA SHOWS?
3 (III) – CASE STUDIES – THE VALUE OF MASHUPS
GROWING……
3 (III) – CASE STUDIES – THE VALUE OF MASHUPS
Month
Un
its
Unit Sales per Month
Own
GROWING MARKET SHARE………
3 (III) – CASE STUDIES – THE VALUE OF MASHUPS
Month
Un
its
Unit Sales per Month
Own
Month
Un
its
Unit Sales per Month
Competitor
Own
GROWING MARKET SHARE IN A SHRINKING MARKET
3 (III) – CASE STUDIES – THE VALUE OF MASHUPS
Month
Un
its
Unit Sales per Month
Own
Month
Un
its
Unit Sales per Month
Competitor
Own
Month
Un
its
Unit Sales per Month
Competitor
MARKET
Own
• 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
IS THIS DATA RIGHT? ARE YOU SURE? REALLY SURE?
3 (IV) – CASE STUDIES – SHINING A LIGHT ON DARK PLACES
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
IF THIS SYSTEM WAS RIGHT WE’D BE GOING BUST!
3 (IV) – CASE STUDIES – SHINING A LIGHT ON DARK PLACES
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
ROUGHLY RIGHT VERSUS PRECISELY WRONG
3 (V) – CASE STUDIES – JUST HOW ACCURATELY DO YOU NEED TO BE WRONG?
• 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?
LOSING THE INFORMATION IN THE DATA – DASHBOARD DAZZLE
3 (V) – CASE STUDIES – JUST HOW ACCURATELY DO YOU NEED TO BE WRONG?
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
Pe
rfo
rma
ce
(U
nit
s)
Day
Performance - Expected vs Actual
EXPECTED
ACTUAL
Linear (EXPECTED)
Linear (ACTUAL)
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
• 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
4 - CONCLUSIONS
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
oEMAIL: [email protected]
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 ?
