The document provides 10 tips for analytics success. It discusses the importance of asking good questions to gain insights, thinking long-term about building an analytics program, starting with investing in people over technology, seeking truth over validating preconceptions from data, understanding data limitations, ensuring ownership of the analytics function, investing in storytellers to communicate insights, finding meaningful ways to visualize data, and transforming data into actionable insights.

1. Stephen Tracy
Data & Insight Lead, SEA
THE HUMAN SIDE OF ANALYTICS

2. INTRODUCTION

3. 3
INTRODUCTION
The big data and analytics
industry is booming

4. 4
INTRODUCTION
The technology landscape that
supports and drives the analytics
industry has become very
sophisticated, but the way we
nurture, grow and invest in talent
hasn’t kept up
TECHNOLOGY
PEOPLE

5. 5
INTRODUCTION
In APAC, the appetite for
analytics solutions is high, but
maturity is still relatively low
Adequate budgets
Impact of analytics
The right talent
The right org structure
Sophistication of use cases

6. 10 TIPS FOR ANALYTICS SUCCESS

7. 7
ask good
questionsone

8. 8
ASK GOOD QUESTIONSONE
Image Source: @nathanwright
Machines are good at
answering complex
questions, but they’re
not very good at asking
them

9. 9
ASK GOOD QUESTIONSONE
QUOTA
QUOTA

10. 10
ASK GOOD QUESTIONSONE
Do you like Starbucks?
Yes
No 1 2 3 4 5 6 7 8 9 10
On a scale of 1-10, how would you
rate your perception of Starbucks?
Extremely
Negative
Extremely
Positive
What is your perception of
Starbucks?
Very Positive
Somewhat Positive
Neutral
Somewhat Negative
Very Negative
Rank the following brands
based on your preference?
Costa Coffee
Starbucks
Coffee Bean
Coffee Connoisseur
1
2
3
4
Which of the following brands do you
like (choose top 2)
Costa Coffee
Starbucks
Coffee Bean
Coffee Connoisseur
Other
In your own words, what is your
perception of Starbucks?
Type Response….

11. 11
think
long termtwo

12. 12
THINK LONG TERMTWO
OrganizationalAnalyticsMaturity
FOUNDATION
REPORTING
ANALYSIS &
OPTIMIZATION
TESTING &
PERSONALIZATION
ADVANCED
ANALYTICS

13. 13
THINK LONG TERMTWO
OrganizationalAnalyticsMaturity
FOUNDATION
REPORTING
ANALYSIS &
OPTIMIZATION
TESTING &
PERSONALIZATION
ADVANCED
ANALYTICS

14. 14
THINK LONG TERMTWO
FOUNDATION
REPORTING
ANALYSIS &
OPTIMIZATION
TESTING &
PERSONALIZATION
OrganizationalAnalyticsMaturity
Foresight
Insight
Hindsight
Strategy
ADVANCED
ANALYTICS
Predictive Modeling
Big Data
Advanced Segmentation
Advanced Personalization
DMP
Your journey to
analytics success is
NOT LINEAR
NOT FINITE
AND FULL OF INFINITE
POSSIBILITIES

15. 15
DISCOVERY
& DESIGN
ANALYTICS
IMPLEMENTATION
REPORTING
& ANALYSIS
TESTING &
OPTIMIZATION
PERSONALIZATION ADVANCED
ANALYTICS
 Requirements closure
 Capture and categorize
requirements
 Identify KPI’s and map to
business objectives
 Review wireframes /
visual designs
 Technical assessment of
implementation
feasibility
 Solution Design
 Tagging Guide
 Tag Manager + data
layer setup
 Set-up basic reports &
dashboards
 Automate and
standardize reporting
wherever applicable
 Data consolidation from
multiple channels for a
single view
 Set-up A/B tests based
on business needs
 Audit site for
optimization and
personalization
opportunities
 Send personalized
campaigns through
emails
 Serve personalized .COM
experience based on
visitor behaviours
 Build statistical models
based on real data to
predict and forecast any
key metrics (like revenue
or orders)
 Identify correlation
between offline events
on
THINK LONG TERMTWO
FOUNDATION
HINDSIGHT
INSIGHT
FORESIGHT
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4QUARTER
PHASE
ACTIVITIES
Dynamic Tag
Manager
TECHNOLOGY
+
- Dynamic Tag
Manager
Adobe
Analytics
Adobe
Target
Example Roadmap

16. 16
start with
peoplethree

17. 17
START WITH PEOPLETHREE
The people (e.g. analysts) who
manage, maintain, and grow
your analytics program
PEOPLE KNOWLEDGE TECHNOLOGY
The knowledge (e.g. best
practice, documentation,
standards, protocols,
processes, etc) that ensure
effectiveness, efficiency and
consistency
The technology which
enables automation and
aggregation of data
collection and analysis

18. 18
START WITH PEOPLETHREE
10 / 90 RULE
For every $10 you spend on a tool you should be investing $90 "intelligent
resources/analysts” (i.e. people)
$25,000
$250,000$225,000
$2,250,000
Tech People

19. 19
START WITH PEOPLETHREE
10 / 90 RULE
For every $10 you spend on a tool you should be investing $90 "intelligent
resources/analysts” (i.e. people)
$25,000
$250,000$225,000
$2,250,000
Tech People

20. 20
START WITH PEOPLETHREE
10 / 90 RULE
For every $10 you spend on a tool you should be investing $90 "intelligent
resources/analysts” (i.e. people)
$25,000
$250,000$225,000
$2,250,000
Tech People

21. 21
START WITH PEOPLETHREE
10 / 90 RULE
For every $10 you spend on a tool you should be investing $90 "intelligent
resources/analysts” (i.e. people)
$25,000
$250,000$225,000
$2,250,000
Tech People

22. 22
START WITH PEOPLETHREE
10 / 90 RULE
For every $10 you spend on a tool you should be investing $90 "intelligent
resources/analysts” (i.e. people)
$25,000
$250,000$225,000
$2,250,000
Tech People

23. 23
START WITH PEOPLETHREE
10 / 90 RULE
For every $10 you spend on a tool you should be investing $90 "intelligent
resources/analysts” (i.e. people)
$25,000
$250,000$225,000
$2,250,000
Tech People

24. 24
seek truth,
not validationfour

25. 25
SEEK TRUTH NOT VALIDATIONFOUR
22,857,224
103
impressions
conversions
3,374,840
video views
234,438
website visits
89%
bounce rate
11 sec
visit duration

26. 26
understand
your datafive

27. 27
UNDERSTAND YOUR DATAFIVE

28. 28
UNDERSTAND YOUR DATAFIVE

29. 29
UNDERSTAND YOUR DATAFIVE

30. 30
UNDERSTAND YOUR DATAFIVE
 How was the data collected? (e.g. poll, census, etc)
 What metrics, data formats and units of measure are included?
 How is the data structured? (e.g. by date/time, by category, etc)
 How granular is your data set? (e.g. broken down by day, month, year, etc)
 Where and how is the data stored? (e.g. 3rd party vendor, owned databased in the
cloud, on-premise, etc)

31. 31
understand
the limits of
technology
six

32. 32
UNDERSTAND THE LIMITS OF TECHNOLOGYSIX
“We decided to test two identical versions of
our homepage against each other. You’d think
these two variants, being identical, would have
nearly the same conversion rate…. We saw
that the new variation, which was identical to
the first, saw an 18.1% improvement. Even
more troubling was that there was a “100%”
probability of this result being accurate.

33. 33
UNDERSTAND THE LIMITS OF TECHNOLOGYSIX

34. 34
“I love this” “I hate this”“meh”
contextual polarity
UNDERSTAND THE LIMITS OF TECHNOLOGYSIX

35. 35
27%
30%
35%
57%
55%
49%
43%
17%18%
21% 22%
26%
0%
10%
20%
30%
40%
50%
60%
Platform 1 Platform 2 Platform 3 Human Panel
%ofCommentsAnalyzed
Evaluating the Efficacy of Automated Sentiment
Positive Neutral Negative
UNDERSTAND THE LIMITS OF TECHNOLOGYSIX

36. 36
27%
30%
35%
57%
55%
49%
43%
17%18%
21% 22%
26%
0%
10%
20%
30%
40%
50%
60%
Platform 1 Platform 2 Platform 3 Human Panel
%ofCommentsAnalyzed
Evaluating the Efficacy of Automated Sentiment
Positive Neutral Negative
UNDERSTAND THE LIMITS OF TECHNOLOGYSIX

37. 37
ensure
you have
ownership
seven

38. 38
ENSURE YOU HAVE OWNERSHIPSEVEN
You need a person or team to own your analytics practice and drive it
forward. This entity needs to nurture the following:
VISION ACCOUNTABILITY GOVERNANCE COLLABORATION EVANGELISM
Ensure you are on
track and pursuing a
vision
Ensure there is
accountability to your
analytics output
Ensure governance
controls are in place
and observed as it
relates to analytics
Encourage and drive
collaboration across
the business to
ensure data isn’t
stuck in silos
Advocate data driven
thinking across the
business and create
new stakeholders

39. 39
invest in
storytellers
not just data
crunchers
eight

40. 40
INVEST IN STORYTELLERSEIGHT
Empirical storytelling is the process of using data to tell a rich and compelling
story. In practice, empirical storytelling requires a proficiency in collecting, cleaning,
interpreting and visualizing data, but it also requires someone who can
communicate the data and key message in a way that resonates with the audience
Empirical Storytelling

41. 41
INVEST IN STORYTELLERSEIGHT
“You can have piles of facts and
still fail to resonate. It’s not the
information itself that’s
important but the emotional
impact of that information.”
“[few] grasp how to use data to
tell a meaningful story that
resonates both intellectually
and emotionally with an
audience”
Nancy Duarte – Writer, Speaker, CEO Daniel Waisberg - Analytics Advocate, Google

42. 42
INVEST IN STORYTELLERSEIGHT
CHALLENGE
Green Mountain sold 18 billion
coffee pods in two years. How can
you give people a concrete sense of
just how many objects that is?
SOLUTION
Use physical space to create
context.

43. 43
INVEST IN STORYTELLERSEIGHT

44. 44
find
meaningful
ways to
communicate
data
nine

45. 45
FIND MEANINGFUL WAYS TO COMMUNICATE DATANINE

46. 46
FIND MEANINGFUL WAYS TO COMMUNICATE DATANINE
DISTANCE
LOCATION &
DIRECTION
SIZE OF ARMY
TEMPERATURE
ADVANCE
RETREAT

47. 47
FIND MEANINGFUL WAYS TO COMMUNICATE DATANINE

48. 48
FIND MEANINGFUL WAYS TO COMMUNICATE DATANINE

49. 49
FIND MEANINGFUL WAYS TO COMMUNICATE DATANINE

50. 50
ten transform data
into insight

51. 51
insight is an
interpretation of data
which has value
and enables
a business decision
you need insight, not
more data
you need to actually
take action, otherwise
the insight has zero
value
TRANSFORM DATA INTO INSIGHTTEN

52. 52
TRANSFORM DATA INTO INSIGHTTEN
Average Time to Data Analysis
10%
17%
33%
22%
15%
3%
Minutes Hours Days Weeks Months Year or
more
4%
11%
24%
32%
22%
3%
6%
Minutes Hours Days Weeks Months Year or
more
Not
deployed
Average Time to Action
What’s the Value of Analytics?

53. 53
TRANSFORM DATA INTO INSIGHTTEN
Average Time to Data Analysis
10%
17%
33%
22%
15%
3%
Minutes Hours Days Weeks Months Year or
more
4%
11%
24%
32%
22%
3%
6%
Minutes Hours Days Weeks Months Year or
more
Not
deployed
Average Time to Action
Better decisions
made faster
What’s the Value of Analytics?

54. 54
TRANSFORM DATA INTO INSIGHTTEN
Average Time to Data Analysis
10%
17%
33%
22%
15%
3%
Minutes Hours Days Weeks Months Year or
more
4%
11%
24%
32%
22%
3%
6%
Minutes Hours Days Weeks Months Year or
more
Not
deployed
Average Time to Action
Better decisions
made faster
Decreasing your time to insight is
easier to do, and can be enabled
through technology/software
Decreasing your time to action is
MUCH harder, and involves talent,
processes, governance and change
management
What’s the Value of Analytics?

55. linkedin.com/in/tracystephen
@stephen_tracy
stracy@sapient.com
analythical.com
THANK YOU