Simo Ahava discusses data quality and the importance of a data-driven process and culture. He advocates for breaking down silos between teams by implementing a shared data layer and involving stakeholders from all teams in iterative development through a definition of done that incorporates data tracking requirements. Empowering developers to facilitate data collection and analysis, and hiring hybrid profiles with both business and technical skills can also improve data quality.

1. Reaktor
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garbage in, garbage outData quality in a TMS world
Simo Ahava
Senior Data Advocate

2. Simo Ahava
Senior Data Advocate, Reaktor
Google Developer Expert, Google Analytics
Blogger, developer, www.simoahava.com
Twitter-er, @SimoAhava
Google+:er, +SimoAhava

3. Data quality isn’t fixed.
Depending on the
hypothesis, a single data
set can shift from
useless to incredibly
insightful without a
single datum changing
shape, size, form, or
function.
#1 Data is subjective

4. Plug-and-play Analytics
@SimoAhava from @ReaktorNow | #SPWK | 5 Feb 2016

5. Plug-and-play Analytics
Data quality isn’t acquired — it’s earned.
@SimoAhava from @ReaktorNow | #SPWK | 5 Feb 2016

6. from online-behavior.com

7. from online-behavior.com

8. Claim 1:
Data quality is destroyed
by laziness and lack of
ambition.

9. Claim 2:
A TMS empowers
developers more than
others.

10. The root of all evil
@SimoAhava from @ReaktorNow | #SPWK | 5 Feb 2016

11. The root of all evil
The "project"
@SimoAhava from @ReaktorNow | #SPWK | 5 Feb 2016

12. Your organization is
creating absurd
amounts of data with
every passing second,
and it’s very difficult to
adapt to the fluctuations
without an agile,
process-driven mindset.
#2 Data is a process

13. The project is often a
series of handovers,
breeding non-
involvement.

14. Specification Implementation Analysis Results

15. Specification Implementation Analysis Results
Business
owner

16. Specification Implementation Analysis Results
Business
owner
Marketing

17. Specification Implementation Analysis Results
Business
owner
Marketing
Developer

18. This leads necessarily to
silos, which have entry
and exit conditions.

19. Implementation Analysis Results
Business
owner
Marketing
Developer
Specification

20. Implementation Analysis Results
Business
owner
Marketing
Developer
Specification

21. Implementation Analysis Results
Business
owner
Marketing
Developer
Specification

22. Implementation Analysis Results
Business
owner
Marketing
Developer
Specification

23. Implementation Analysis Results
Business
owner
Marketing
Developer
Specification

24. Implementation Analysis Results
Business
owner
Marketing
Developer
Specification

25. Silos, so what?
@SimoAhava from @ReaktorNow | #SPWK | 5 Feb 2016

26. Silos, so what?
As long as the work gets done, right?
@SimoAhava from @ReaktorNow | #SPWK | 5 Feb 2016

27. Data is the lifeblood of the
organization. It flows
through all departments,
across job titles,
permeating the very
fabric of the organization,
reinforcing its
foundations for growth.
#3 Data abhors silos

28. Do these sound familiar:

29. Monthly reports which lack relevance, are rife with generic suggestions that lack research
in the context of your business, reiteration of previous month’s points, even if there are
solid reasons why they weren’t addressed.

30. Ridiculously ugly and ineffective JavaScript hacks for measurement points which should
be tackled in the Data Layer.

31. Hiding behind data, and passing blame to other silos.
Could someone fix the
Bounce Rate metric on our
site?

32. Analytics feature requests are deprioritized, and deployed extremely infrequently.
Fix
transactionRevenue
to show revenue, not
customer weight.

33. Communication is difficult due to the overhead of meeting face-to-face, project plans are
set in stone during sales, and it’s difficult to change existing project goals or set new ones
due to consultants being hired as "extra pairs of hands" rather than advisors.

34. These are symptoms of
data being treated as a
project outcome.

35. Cure I: The Data Layer
@SimoAhava from @ReaktorNow | #SPWK | 5 Feb 2016

36. Cure I: The Data Layer
Using technology to solve communication problems
@SimoAhava from @ReaktorNow | #SPWK | 5 Feb 2016

37. Typically, there are three
definitions
of Data Layer that we use in
the digital world.

39. 1. Set of business
requirements
for tracking
digital assets,
visits, and
visitors.

40. 1. Set of business
requirements
for tracking
digital assets,
visits, and
visitors.
2. Encoded, global
data structure,
accessed and
modified by
connected
platforms.

41. 1. Set of business
requirements
for tracking
digital assets,
visits, and
visitors.
2. Encoded, global
data structure,
accessed and
modified by
connected
platforms.
2. Data model of a
connected
platform, which
copies or digests
information in the
global structure.

42. 1. Set of business
requirements
for tracking
digital assets,
visits, and
visitors.
2. Encoded, global
data structure,
accessed and
modified by
connected
platforms.
2. Data model of a
connected
platform, which
copies or digests
information in the
global structure.
dataLayer.push({
'pageType' : 'home'
});
google_tag_manager['GTM-123']
.dataLayer
.set('pageType', 'home');

43. Across all three definitions,
the purpose of a Data Layer
is simple:

44. DMP / DWH / TMS / etc.
X X
Actions Presentation
Data Layer

45. DMP / DWH / TMS / etc.
X X
Actions Presentation
Data Layer
The purpose of a Data Layer is to provide a bilateral layer on the digital
asset, which decouples, normalises, and uniformly encodes semantic
information passed through and stored within.

46. The Data Layer is a joint
venture, where people and
systems communicate
across silos.

47. DMP / DWH / TMS / etc.
let tracker = GANTracker.sharedTracker()
tracker.trackEvent("revenue", action:"Q1",
value:"15678000")
tracker.trackEvent("revenue", action:"Q2",
value:"16888000")
tracker.trackEvent("revenue", action:"Q3",
value:"15991000")
tracker.trackEvent("revenue", action:"Q4",
value:"19133000")
rq12014,rq22014,rq32014,rq42015
15677998,16887988,15990988,19133400
analytics.collect({
'revenueQ1' : '15677998.00',
'revenueQ2' : '16887988.00',
'revenueQ3' : '15990988.00',
'revenueQ4' : '19133400.00'
})

48. DMP / DWH / TMS / etc.
let dataLayer = new Array()
dataLayer.push({
"revenue_Q1_2014" : "15677998.00",
"revenue_Q2_2014" : "16887988.00",
"revenue_Q3_2014" : "15990988.00",
"revenue_Q4_2014" : "19133400.00"
})

49. Cure II: The Process
@SimoAhava from @ReaktorNow | #SPWK | 5 Feb 2016

50. Cure II: The Process
Involve, involve, involve
@SimoAhava from @ReaktorNow | #SPWK | 5 Feb 2016

51. An iterative, agile
process is necessary for
optimal utilization of a
TMS.

52. Definition of Done

53. Definition of Done
Developed features
do not impede
measurement.
Developed features
are trackable.
Sprint

54. Definition of Done
Developed features
do not impede
measurement.
Developed features
are trackable.
Sprint
If necessary, feature
is encoded with
tracking attributes.
If necessary, feature
is linked to a Data
Layer object.
Feature

55. Definition of Done
Developed features
do not impede
measurement.
Developed features
are trackable.
Sprint
If necessary, feature
is encoded with
tracking attributes.
If necessary, feature
is linked to a Data
Layer object.
Feature
Attribute syntax is
correct for tracking.
Data Layer object
syntax is correct.
Task

57. Constant participation

58. Constant participation
Transparency

59. Cure III: Empowerment
@SimoAhava from @ReaktorNow | #SPWK | 5 Feb 2016

60. Cure III: Empowerment
We are all hybrid beings
@SimoAhava from @ReaktorNow | #SPWK | 5 Feb 2016

61. The entire life cycle of a
single data point, from
collection to reports,
requires knowledge and
expertise to manage.
#4 Data is difficult

62. Developer facilitation is
crucial to data quality
and optimized data
collection.

63. 1: Education

64. 1. JavaScript: www.codecademy.com, www.codeschool.com,
Professional JavaScript for Web Developers, DOM
Enlightenment…
2. Digital analytics: www.kaushik.net, www.simoahava.com,
Successful Analytics, Practical Google Analytics and Google Tag
Manager for Developers…
3. Training, courses, certifications: Digital Analytics Association,
Digital Analytics Fundamentals (Google), Market Motive…
4. Conferences: MeasureCamp, SMX, eMetrics, Digital Analytics
Hub, ConversionXL, Superweek, All Things Data…

65. treat content as a product2: hybrid skills

66. "Business owner"
- No operational skills
+ Strategic
"Developer"
- Uncooperative
+ Methodical
"Marketer"
- Bully
+ Consultative

67. + Passionate, actively interested
+ Understands ever-changing requirements
+ Good grasp of digital tech
+ Statistical mindset
+ Knows the product / service inside and out
+ Critical about the present, curious about the future

68. treat content as a product3: Passionate interest

69. + Dedicated sandbox
+ Website or blog to test
new ideas on
+ Test and debug setups in
Google Analytics and
Google Tag Manager
+ Utilization of GTM
environments

70. Hire to educate, not to delegate
PO
Developer Analyst

71. Hire to educate, not to delegate
PO
Developer Analyst

72. Data is difficult
@SimoAhava from @ReaktorNow | #SPWK | 5 Feb 2016

73. Data is difficult
Data quality is earned, not acquired
@SimoAhava from @ReaktorNow | #SPWK | 5 Feb 2016

74. Thank you!
simo.ahava@reaktor.com
www.simoahava.com
Twitter: @SimoAhava
Google+: +SimoAhava
Data is difficult - http://goo.gl/53aFUU
The Schema Conspiracy - http://goo.gl/o2Pwys
Further reading:
10 Truths About Data - http://goo.gl/EpesEj