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Looking for Patterns
in the Data
Ray Poynter
27 January 2022
Finding and Communicating the
Story in the Data
Ray Poynter
To be published Mid-2022
The Framework
1. Define the Problem
2. Assess the Wider Context
3. Find the Big Picture
4. Extract the Key Findings
5. Determine the Message
6. Create the Story
7. Communicate the Story
8. Follow Up
1 Define the Problem
If you don’t define the problem properly, you are unlikely to find the answer
The process includes:
• What is the business question?
• What are the research questions?
• What do we already know?
• What does success look like?
• What does the business plan to do after receiving the answers?
• What are the predictions?
Finding the
Patterns in the
Data
1. Using the question as a lens
2. Making the patterns easier to see
3. Look for Connections, Correlations,
Contradictions, Curiosities and
Surprises
4. Assemble the story
Making the Patterns Easier to See
• Making numbers more visual
• Sorting
• Comparing
• Benchmarks
• Derived variables
• Analytics
Per Capita Govt. Spend on Health - $
183 Countries, 1995 to 2010, source Gapminder
3 Significant Digits – Units of $10
Divide by 10, display no decimals (but don’t delete them)
Three digits good
Two digits medium
One digit poor
Sorting – Filters in Excel
Don’t look at data in alphabetical order or in questionnaire order
Pattern 1, Best = USA + Northern Europe
Pattern 2, Worst = Africa overrepresented
Surprise = Niue (Pacific island, pop < 2,000, it’s an outlier)
Looking at the Shape of the Data
Steps indicate
possible curiosities,
e.g. OECD, EU etc
Index, 1995 = 100
Pattern 1, the most improved tended to
have low incomes per person in 1995
Pattern 2, those who declined tended to
have low incomes per person in 1995
Diagonalizing
Brand Attribute Data
% Agreeing Sexy Cheap Stylish Friendly Strong Traditional
Brand 1 41.73% 63.40% 20.88% 71.20% 41.67% 31.66%
Brand 2 87.21% 22.16% 64.50% 40.02% 96.24% 61.31%
Brand 3 71.52% 48.80% 53.86% 30.08% 27.88% 28.12%
Brand 4 97.23% 9.70% 93.97% 19.89% 96.99% 98.19%
Brand 5 92.70% 12.73% 88.36% 34.63% 30.03% 26.98%
Brand 6 30.58% 60.82% 20.61% 55.34% 75.00% 64.70%
Brand 7 11.47% 72.02% 1.93% 83.76% 59.86% 28.94%
Hide the Decimal Places
% Agreeing Sexy Cheap Stylish Friendly Strong Traditional
Brand 1 42% 63% 21% 71% 42% 32%
Brand 2 87% 22% 65% 40% 96% 61%
Brand 3 72% 49% 54% 30% 28% 28%
Brand 4 97% 10% 94% 20% 97% 98%
Brand 5 93% 13% 88% 35% 30% 27%
Brand 6 31% 61% 21% 55% 75% 65%
Brand 7 11% 72% 2% 84% 60% 29%
Note, don’t change the numbers, just the way they are displayed
Conditional Formatting
% Agreeing Sexy Cheap Stylish Friendly Strong Traditional
Brand 1 42% 63% 21% 71% 42% 32%
Brand 2 87% 22% 65% 40% 96% 61%
Brand 3 72% 49% 54% 30% 28% 28%
Brand 4 97% 10% 94% 20% 97% 98%
Brand 5 93% 13% 88% 35% 30% 27%
Brand 6 31% 61% 21% 55% 75% 65%
Brand 7 11% 72% 2% 84% 60% 29%
We can see there are differences, but the patterns are not clear yet
Sum the Rows and Columns
% Agreeing Sexy Cheap Stylish Friendly Strong Traditional Sum
Brand 1 42% 63% 21% 71% 42% 32% 271%
Brand 2 87% 22% 65% 40% 96% 61% 371%
Brand 3 72% 49% 54% 30% 28% 28% 260%
Brand 4 97% 10% 94% 20% 97% 98% 416%
Brand 5 93% 13% 88% 35% 30% 27% 285%
Brand 6 31% 61% 21% 55% 75% 65% 307%
Brand 7 11% 72% 2% 84% 60% 29% 258%
Sum 432% 290% 344% 335% 428% 340%
Sort the Rows
% Agreeing Sexy Cheap Stylish Friendly Strong Traditional Sum
Brand 4 97% 10% 94% 20% 97% 98% 416%
Brand 2 87% 22% 65% 40% 96% 61% 371%
Brand 6 31% 61% 21% 55% 75% 65% 307%
Brand 5 93% 13% 88% 35% 30% 27% 285%
Brand 1 42% 63% 21% 71% 42% 32% 271%
Brand 3 72% 49% 54% 30% 28% 28% 260%
Brand 7 11% 72% 2% 84% 60% 29% 258%
Sum 432% 290% 344% 335% 428% 340%
Largest number to the top
Sort the Columns
% Agreeing Sexy Strong Stylish Traditional Friendly Cheap Sum
Brand 4 97% 97% 94% 98% 20% 10% 416%
Brand 2 87% 96% 65% 61% 40% 22% 371%
Brand 6 31% 75% 21% 65% 55% 61% 307%
Brand 5 93% 30% 88% 27% 35% 13% 285%
Brand 1 42% 42% 21% 32% 71% 63% 271%
Brand 3 72% 28% 54% 28% 30% 49% 260%
Brand 7 11% 60% 2% 29% 84% 72% 258%
Sum 432% 428% 344% 340% 335% 290%
In Excel, select Options in the Sort dialogue to access left-to-right sorting
Calculate Distances from
top row & left column
% Agreeing Sexy Strong Stylish Traditional Friendly Cheap Distance
Brand 4 97% 97% 94% 98% 20% 10% 0%
Brand 2 87% 96% 65% 61% 40% 22% 54%
Brand 6 31% 75% 21% 65% 55% 61% 124%
Brand 5 93% 30% 88% 27% 35% 13% 99%
Brand 1 42% 42% 21% 32% 71% 63% 146%
Brand 3 72% 28% 54% 28% 30% 49% 117%
Brand 7 11% 60% 2% 29% 84% 72% 173%
Distance 0% 101% 38% 92% 141% 154%
To calculate the distances, use the formula =SQRT(SUMXMY2(range1,range2))
This calculates the distance using Pythagoras.
Sort the Rows
% Agreeing Sexy Strong Stylish Traditional Friendly Cheap Distance
Brand 4 97% 97% 94% 98% 20% 10% 0%
Brand 2 87% 96% 65% 61% 40% 22% 54%
Brand 5 93% 30% 88% 27% 35% 13% 99%
Brand 3 72% 28% 54% 28% 30% 49% 117%
Brand 6 31% 75% 21% 65% 55% 61% 124%
Brand 1 42% 42% 21% 32% 71% 63% 146%
Brand 7 11% 60% 2% 29% 84% 72% 173%
Distance 0% 101% 38% 92% 141% 154%
Sort the rows by distance, smallest number from the top
Sort the Columns
% Agreeing Sexy Stylish Traditional Strong Friendly Cheap Distance
Brand 4 97% 94% 98% 97% 20% 10% 0%
Brand 2 87% 65% 61% 96% 40% 22% 54%
Brand 5 93% 88% 27% 30% 35% 13% 99%
Brand 3 72% 54% 28% 28% 30% 49% 117%
Brand 6 31% 21% 65% 75% 55% 61% 124%
Brand 1 42% 21% 32% 42% 71% 63% 146%
Brand 7 11% 2% 29% 60% 84% 72% 173%
Distance 0% 38% 92% 101% 141% 154%
Sort the columns by distance, smallest number on the left
Tidy up the Data
% Agreeing Sexy Stylish Traditional Strong Friendly Cheap
Brand 4 97 94 98 97 20 10
Brand 2 87 65 61 96 40 22
Brand 5 93 88 27 30 35 13
Brand 3 72 54 28 28 30 49
Brand 6 31 21 65 75 55 61
Brand 1 42 21 32 42 71 63
Brand 7 11 2 29 60 84 72
Remove the extra columns, multiply by 100 to remove the % signs.
Pattern 1, Brands 4, 2 & 5 are Sexy and Stylish
Pattern 2, Brands 6, 1 & 7 are Friendly and Cheap
Do Not (Normally) use the Questionnaire Sequence
0
5000
10000
15000
20000
25000
0
100
200
300
400
500
600
700
Brazil China France Germany India Italy Japan Russia South
Africa
Sweden UK USA World
Cases
Per
Million
Deaths
Per
Million
Deaths and Cases Per Million of Population
(until 27 September 2020)
Deaths / 1Million Case / 1Million
https://www.worldometers.info/coronavirus/ - downloaded 27 September 2020
Sort the data by something meaningful
0
5000
10000
15000
20000
25000
0
100
200
300
400
500
600
700
Brazil USA UK Italy Sweden France South
Africa
Russia World Germany India Japan China
Cases
Per
Million
Deaths
Per
Million
Deaths and Cases Per Million of Population
(until 27 September 2020)
Deaths / 1Million Case / 1Million
https://www.worldometers.info/coronavirus/ - downloaded 27 September 2020
World = benchmark
Countries with high death rates
include LatAm, USA, Europe and SA
Lower rates include Europe and Asia
Sort the data by something meaningful
0
5000
10000
15000
20000
25000
0
100
200
300
400
500
600
700
Brazil USA UK Italy Sweden France South
Africa
Russia World Germany India Japan China
Cases
Per
Million
Deaths
Per
Million
Deaths and Cases Per Million of Population
(until 27 September 2020)
Deaths / 1Million Case / 1Million
https://www.worldometers.info/coronavirus/ - downloaded 27 September 2020
Some countries break the link
between Cases and Deaths
Idea to test = Were UK, Italy, Sweden
& France testing fewer people?
Derived Variables
• Younger people
• People in London
• Men
If a concept is
preferred
(slightly) by
• The differences may be much larger
Create a
variable ‘Young
Men in London’
Downloaded from Worldmeters.info
3 March 2021
https://www.worldometers.info/coronavirus/country/uk/
Downloaded from Worldmeters.info
3 March 2021
https://www.worldometers.info/coronavirus/country/uk/
Downloaded from Worldmeters.info
3 March 2021
https://www.worldometers.info/coronavirus/country/uk/
Simplify and Compare
Downloaded from The Guardian 22 Jan 2022
• Pattern = Cases, Hospitalisations, then Deaths
• Alpha had the deaths and hospitalisations
• Delta had the vaccinations and lockdowns
• Omicron had boosters and light restrictions
• The link between cases and deaths changed
Try Alternative Perspectives
Add comparators
Deaths, raw numbers, new, linear scale
Derived Scale – per 100K
Deaths, per 100K, new, linear scale
Cumulative
Deaths, per 100K, cumulative, linear scale
Cumulative – longer time span
Deaths, per 100K, cumulative, linear scale
Consider other comparators
Deaths, per 100K, cumulative, linear scale
Consider other comparators
Deaths, per 100K, cumulative, linear scale
The Absence of a Strong Pattern Can be Interesting
Source: Statista, 2020
Older people cook
from scratch
more often – but
the differences
between age
groups are not
large.
35-64 almost
identical, 18-24 a
bit lower, 65+ a
bit higher
Analytics
We use analytics when the patterns are not visible without
analytics
Key tools include
• Correspondence Analysis – shows relationships on a map
• Factor analysis – great for simplifying the data
• Cluster analysis – shows groupings that exist in the data
• Regression analysis – shows the scale of relationships
• Latent Class – allows techniques to be combined, e.g. regression and
cluster analysis
The Building Blocks of Stories
Fact 1 Fact 2 Fact 3 Fact 4 Fact 5 Fact 6 Fact 7 Fact 8 Fact 9
Finding
1
Finding
2
Finding
3
Finding
4
Finding
5
Finding
6
Finding
7
Finding
8
Insight
Categorizing the Facts
Fact a Fact d
Fact c
Nice to Know
Fact b
Fact f
Fact g Fact l
Fact e
Fact h
Fact i
Fact j
Fact k
Extract the Findings
Fact a Fact d
Fact c
Nice to Know
Fact b
Fact f
Fact g Fact l
Fact e
Fact h
Fact i
Fact j
Fact k
Finding
a
Finding
b
Finding
c
Finding
d
Finding
e
Secondary Findings
Fact a Fact d
Fact c
Nice to Know
Fact b
Fact f
Fact g Fact l
Fact e
Fact h
Fact i
Fact j
Fact k
Finding
a
Finding
b
Finding
c
Finding
d
Finding
e
Finding
f
Extract the Insight
Fact a Fact d
Fact c
Nice to Know
Fact b
Fact f
Fact g Fact l
Fact e
Fact h
Fact i
Fact j
Fact k
Finding
a
Finding
b
Finding
c
Finding
d
Finding
e
Finding
f
Insight
Finding the
Patterns in the
Data
1. Using the question as a lens
2. Making the patterns easier
to see
3. Look for Connections,
Correlations, Contradictions
and Surprises
4. Assemble the story
Questions?
Sponsors
Communication

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Looking for patterns in the data

  • 1. Looking for Patterns in the Data Ray Poynter 27 January 2022
  • 2. Finding and Communicating the Story in the Data Ray Poynter To be published Mid-2022
  • 3. The Framework 1. Define the Problem 2. Assess the Wider Context 3. Find the Big Picture 4. Extract the Key Findings 5. Determine the Message 6. Create the Story 7. Communicate the Story 8. Follow Up
  • 4. 1 Define the Problem If you don’t define the problem properly, you are unlikely to find the answer The process includes: • What is the business question? • What are the research questions? • What do we already know? • What does success look like? • What does the business plan to do after receiving the answers? • What are the predictions?
  • 5. Finding the Patterns in the Data 1. Using the question as a lens 2. Making the patterns easier to see 3. Look for Connections, Correlations, Contradictions, Curiosities and Surprises 4. Assemble the story
  • 6. Making the Patterns Easier to See • Making numbers more visual • Sorting • Comparing • Benchmarks • Derived variables • Analytics
  • 7. Per Capita Govt. Spend on Health - $ 183 Countries, 1995 to 2010, source Gapminder
  • 8. 3 Significant Digits – Units of $10 Divide by 10, display no decimals (but don’t delete them) Three digits good Two digits medium One digit poor
  • 9. Sorting – Filters in Excel Don’t look at data in alphabetical order or in questionnaire order Pattern 1, Best = USA + Northern Europe Pattern 2, Worst = Africa overrepresented Surprise = Niue (Pacific island, pop < 2,000, it’s an outlier)
  • 10. Looking at the Shape of the Data Steps indicate possible curiosities, e.g. OECD, EU etc
  • 11. Index, 1995 = 100 Pattern 1, the most improved tended to have low incomes per person in 1995 Pattern 2, those who declined tended to have low incomes per person in 1995
  • 12. Diagonalizing Brand Attribute Data % Agreeing Sexy Cheap Stylish Friendly Strong Traditional Brand 1 41.73% 63.40% 20.88% 71.20% 41.67% 31.66% Brand 2 87.21% 22.16% 64.50% 40.02% 96.24% 61.31% Brand 3 71.52% 48.80% 53.86% 30.08% 27.88% 28.12% Brand 4 97.23% 9.70% 93.97% 19.89% 96.99% 98.19% Brand 5 92.70% 12.73% 88.36% 34.63% 30.03% 26.98% Brand 6 30.58% 60.82% 20.61% 55.34% 75.00% 64.70% Brand 7 11.47% 72.02% 1.93% 83.76% 59.86% 28.94%
  • 13. Hide the Decimal Places % Agreeing Sexy Cheap Stylish Friendly Strong Traditional Brand 1 42% 63% 21% 71% 42% 32% Brand 2 87% 22% 65% 40% 96% 61% Brand 3 72% 49% 54% 30% 28% 28% Brand 4 97% 10% 94% 20% 97% 98% Brand 5 93% 13% 88% 35% 30% 27% Brand 6 31% 61% 21% 55% 75% 65% Brand 7 11% 72% 2% 84% 60% 29% Note, don’t change the numbers, just the way they are displayed
  • 14. Conditional Formatting % Agreeing Sexy Cheap Stylish Friendly Strong Traditional Brand 1 42% 63% 21% 71% 42% 32% Brand 2 87% 22% 65% 40% 96% 61% Brand 3 72% 49% 54% 30% 28% 28% Brand 4 97% 10% 94% 20% 97% 98% Brand 5 93% 13% 88% 35% 30% 27% Brand 6 31% 61% 21% 55% 75% 65% Brand 7 11% 72% 2% 84% 60% 29% We can see there are differences, but the patterns are not clear yet
  • 15. Sum the Rows and Columns % Agreeing Sexy Cheap Stylish Friendly Strong Traditional Sum Brand 1 42% 63% 21% 71% 42% 32% 271% Brand 2 87% 22% 65% 40% 96% 61% 371% Brand 3 72% 49% 54% 30% 28% 28% 260% Brand 4 97% 10% 94% 20% 97% 98% 416% Brand 5 93% 13% 88% 35% 30% 27% 285% Brand 6 31% 61% 21% 55% 75% 65% 307% Brand 7 11% 72% 2% 84% 60% 29% 258% Sum 432% 290% 344% 335% 428% 340%
  • 16. Sort the Rows % Agreeing Sexy Cheap Stylish Friendly Strong Traditional Sum Brand 4 97% 10% 94% 20% 97% 98% 416% Brand 2 87% 22% 65% 40% 96% 61% 371% Brand 6 31% 61% 21% 55% 75% 65% 307% Brand 5 93% 13% 88% 35% 30% 27% 285% Brand 1 42% 63% 21% 71% 42% 32% 271% Brand 3 72% 49% 54% 30% 28% 28% 260% Brand 7 11% 72% 2% 84% 60% 29% 258% Sum 432% 290% 344% 335% 428% 340% Largest number to the top
  • 17. Sort the Columns % Agreeing Sexy Strong Stylish Traditional Friendly Cheap Sum Brand 4 97% 97% 94% 98% 20% 10% 416% Brand 2 87% 96% 65% 61% 40% 22% 371% Brand 6 31% 75% 21% 65% 55% 61% 307% Brand 5 93% 30% 88% 27% 35% 13% 285% Brand 1 42% 42% 21% 32% 71% 63% 271% Brand 3 72% 28% 54% 28% 30% 49% 260% Brand 7 11% 60% 2% 29% 84% 72% 258% Sum 432% 428% 344% 340% 335% 290% In Excel, select Options in the Sort dialogue to access left-to-right sorting
  • 18. Calculate Distances from top row & left column % Agreeing Sexy Strong Stylish Traditional Friendly Cheap Distance Brand 4 97% 97% 94% 98% 20% 10% 0% Brand 2 87% 96% 65% 61% 40% 22% 54% Brand 6 31% 75% 21% 65% 55% 61% 124% Brand 5 93% 30% 88% 27% 35% 13% 99% Brand 1 42% 42% 21% 32% 71% 63% 146% Brand 3 72% 28% 54% 28% 30% 49% 117% Brand 7 11% 60% 2% 29% 84% 72% 173% Distance 0% 101% 38% 92% 141% 154% To calculate the distances, use the formula =SQRT(SUMXMY2(range1,range2)) This calculates the distance using Pythagoras.
  • 19. Sort the Rows % Agreeing Sexy Strong Stylish Traditional Friendly Cheap Distance Brand 4 97% 97% 94% 98% 20% 10% 0% Brand 2 87% 96% 65% 61% 40% 22% 54% Brand 5 93% 30% 88% 27% 35% 13% 99% Brand 3 72% 28% 54% 28% 30% 49% 117% Brand 6 31% 75% 21% 65% 55% 61% 124% Brand 1 42% 42% 21% 32% 71% 63% 146% Brand 7 11% 60% 2% 29% 84% 72% 173% Distance 0% 101% 38% 92% 141% 154% Sort the rows by distance, smallest number from the top
  • 20. Sort the Columns % Agreeing Sexy Stylish Traditional Strong Friendly Cheap Distance Brand 4 97% 94% 98% 97% 20% 10% 0% Brand 2 87% 65% 61% 96% 40% 22% 54% Brand 5 93% 88% 27% 30% 35% 13% 99% Brand 3 72% 54% 28% 28% 30% 49% 117% Brand 6 31% 21% 65% 75% 55% 61% 124% Brand 1 42% 21% 32% 42% 71% 63% 146% Brand 7 11% 2% 29% 60% 84% 72% 173% Distance 0% 38% 92% 101% 141% 154% Sort the columns by distance, smallest number on the left
  • 21. Tidy up the Data % Agreeing Sexy Stylish Traditional Strong Friendly Cheap Brand 4 97 94 98 97 20 10 Brand 2 87 65 61 96 40 22 Brand 5 93 88 27 30 35 13 Brand 3 72 54 28 28 30 49 Brand 6 31 21 65 75 55 61 Brand 1 42 21 32 42 71 63 Brand 7 11 2 29 60 84 72 Remove the extra columns, multiply by 100 to remove the % signs. Pattern 1, Brands 4, 2 & 5 are Sexy and Stylish Pattern 2, Brands 6, 1 & 7 are Friendly and Cheap
  • 22. Do Not (Normally) use the Questionnaire Sequence 0 5000 10000 15000 20000 25000 0 100 200 300 400 500 600 700 Brazil China France Germany India Italy Japan Russia South Africa Sweden UK USA World Cases Per Million Deaths Per Million Deaths and Cases Per Million of Population (until 27 September 2020) Deaths / 1Million Case / 1Million https://www.worldometers.info/coronavirus/ - downloaded 27 September 2020
  • 23. Sort the data by something meaningful 0 5000 10000 15000 20000 25000 0 100 200 300 400 500 600 700 Brazil USA UK Italy Sweden France South Africa Russia World Germany India Japan China Cases Per Million Deaths Per Million Deaths and Cases Per Million of Population (until 27 September 2020) Deaths / 1Million Case / 1Million https://www.worldometers.info/coronavirus/ - downloaded 27 September 2020 World = benchmark Countries with high death rates include LatAm, USA, Europe and SA Lower rates include Europe and Asia
  • 24. Sort the data by something meaningful 0 5000 10000 15000 20000 25000 0 100 200 300 400 500 600 700 Brazil USA UK Italy Sweden France South Africa Russia World Germany India Japan China Cases Per Million Deaths Per Million Deaths and Cases Per Million of Population (until 27 September 2020) Deaths / 1Million Case / 1Million https://www.worldometers.info/coronavirus/ - downloaded 27 September 2020 Some countries break the link between Cases and Deaths Idea to test = Were UK, Italy, Sweden & France testing fewer people?
  • 25. Derived Variables • Younger people • People in London • Men If a concept is preferred (slightly) by • The differences may be much larger Create a variable ‘Young Men in London’
  • 26. Downloaded from Worldmeters.info 3 March 2021 https://www.worldometers.info/coronavirus/country/uk/
  • 27. Downloaded from Worldmeters.info 3 March 2021 https://www.worldometers.info/coronavirus/country/uk/
  • 28. Downloaded from Worldmeters.info 3 March 2021 https://www.worldometers.info/coronavirus/country/uk/
  • 29. Simplify and Compare Downloaded from The Guardian 22 Jan 2022 • Pattern = Cases, Hospitalisations, then Deaths • Alpha had the deaths and hospitalisations • Delta had the vaccinations and lockdowns • Omicron had boosters and light restrictions • The link between cases and deaths changed
  • 31. Add comparators Deaths, raw numbers, new, linear scale
  • 32. Derived Scale – per 100K Deaths, per 100K, new, linear scale
  • 33. Cumulative Deaths, per 100K, cumulative, linear scale
  • 34. Cumulative – longer time span Deaths, per 100K, cumulative, linear scale
  • 35. Consider other comparators Deaths, per 100K, cumulative, linear scale
  • 36. Consider other comparators Deaths, per 100K, cumulative, linear scale
  • 37. The Absence of a Strong Pattern Can be Interesting Source: Statista, 2020 Older people cook from scratch more often – but the differences between age groups are not large. 35-64 almost identical, 18-24 a bit lower, 65+ a bit higher
  • 38. Analytics We use analytics when the patterns are not visible without analytics Key tools include • Correspondence Analysis – shows relationships on a map • Factor analysis – great for simplifying the data • Cluster analysis – shows groupings that exist in the data • Regression analysis – shows the scale of relationships • Latent Class – allows techniques to be combined, e.g. regression and cluster analysis
  • 39. The Building Blocks of Stories Fact 1 Fact 2 Fact 3 Fact 4 Fact 5 Fact 6 Fact 7 Fact 8 Fact 9 Finding 1 Finding 2 Finding 3 Finding 4 Finding 5 Finding 6 Finding 7 Finding 8 Insight
  • 40. Categorizing the Facts Fact a Fact d Fact c Nice to Know Fact b Fact f Fact g Fact l Fact e Fact h Fact i Fact j Fact k
  • 41. Extract the Findings Fact a Fact d Fact c Nice to Know Fact b Fact f Fact g Fact l Fact e Fact h Fact i Fact j Fact k Finding a Finding b Finding c Finding d Finding e
  • 42. Secondary Findings Fact a Fact d Fact c Nice to Know Fact b Fact f Fact g Fact l Fact e Fact h Fact i Fact j Fact k Finding a Finding b Finding c Finding d Finding e Finding f
  • 43. Extract the Insight Fact a Fact d Fact c Nice to Know Fact b Fact f Fact g Fact l Fact e Fact h Fact i Fact j Fact k Finding a Finding b Finding c Finding d Finding e Finding f Insight
  • 44. Finding the Patterns in the Data 1. Using the question as a lens 2. Making the patterns easier to see 3. Look for Connections, Correlations, Contradictions and Surprises 4. Assemble the story
  • 45.