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Reaktor
Mannerheimintie 2
00100, Helsinki Finland
tel: +358 9 4152 0200
www.reaktor.com
info@reaktor.com
Confidential
©201...
Simo Ahava
Senior Data Advocate, Reaktor
Google Developer Expert, Google Analytics
Blogger, developer, www.simoahava.com
T...
Data quality isn’t fixed.
Depending on the
hypothesis, a single data
set can shift from
useless to incredibly
insightful wi...
Plug-and-play Analytics
@SimoAhava from @ReaktorNow | #SPWK | 5 Feb 2016
Plug-and-play Analytics
Data quality isn’t acquired — it’s earned.
@SimoAhava from @ReaktorNow | #SPWK | 5 Feb 2016
from online-behavior.com
from online-behavior.com
Claim 1:
Data quality is destroyed
by laziness and lack of
ambition.
Claim 2:
A TMS empowers
developers more than
others.
The root of all evil
@SimoAhava from @ReaktorNow | #SPWK | 5 Feb 2016
The root of all evil
The "project"
@SimoAhava from @ReaktorNow | #SPWK | 5 Feb 2016
Your organization is
creating absurd
amounts of data with
every passing second,
and it’s very difficult to
adapt to the fluc...
The project is often a
series of handovers,
breeding non-
involvement.
Specification Implementation Analysis Results
Specification Implementation Analysis Results
Business

owner
Specification Implementation Analysis Results
Business

owner
Marketing
Specification Implementation Analysis Results
Business

owner
Marketing
Developer
This leads necessarily to
silos, which have entry
and exit conditions.
Implementation Analysis Results
Business

owner
Marketing
Developer
Specification
Implementation Analysis Results
Business

owner
Marketing
Developer
Specification
Implementation Analysis Results
Business

owner
Marketing
Developer
Specification
Implementation Analysis Results
Business

owner
Marketing
Developer
Specification
Implementation Analysis Results
Business

owner
Marketing
Developer
Specification
Implementation Analysis Results
Business

owner
Marketing
Developer
Specification
Silos, so what?
@SimoAhava from @ReaktorNow | #SPWK | 5 Feb 2016
Silos, so what?
As long as the work gets done, right?
@SimoAhava from @ReaktorNow | #SPWK | 5 Feb 2016
Data is the lifeblood of the
organization. It flows
through all departments,
across job titles,
permeating the very
fabric ...
Do these sound familiar:
Monthly reports which lack relevance, are rife with generic suggestions that lack research
in the context of your business...
Ridiculously ugly and ineffective JavaScript hacks for measurement points which should
be tackled in the Data Layer.
Hiding behind data, and passing blame to other silos.
Could someone fix the
Bounce Rate metric on our
site?
Analytics feature requests are deprioritized, and deployed extremely infrequently.
Fix
transactionRevenue
to show revenue,...
Communication is difficult due to the overhead of meeting face-to-face, project plans are
set in stone during sales, and i...
These are symptoms of
data being treated as a
project outcome.
Cure I: The Data Layer
@SimoAhava from @ReaktorNow | #SPWK | 5 Feb 2016
Cure I: The Data Layer
Using technology to solve communication problems
@SimoAhava from @ReaktorNow | #SPWK | 5 Feb 2016
Typically, there are three
definitions
of Data Layer that we use in
the digital world.
1. Set of business
requirements

for tracking
digital assets,

visits, and
visitors.
1. Set of business
requirements

for tracking
digital assets,

visits, and
visitors.
2. Encoded, global
data structure,
ac...
1. Set of business
requirements

for tracking
digital assets,

visits, and
visitors.
2. Encoded, global
data structure,
ac...
1. Set of business
requirements

for tracking
digital assets,

visits, and
visitors.
2. Encoded, global
data structure,
ac...
Across all three definitions,
the purpose of a Data Layer
is simple:
DMP / DWH / TMS / etc.
X X
Actions Presentation
Data Layer
DMP / DWH / TMS / etc.
X X
Actions Presentation
Data Layer
The purpose of a Data Layer is to provide a bilateral layer on ...
The Data Layer is a joint
venture, where people and
systems communicate
across silos.
DMP / DWH / TMS / etc.
let tracker = GANTracker.sharedTracker()
tracker.trackEvent("revenue", action:"Q1",
value:"15678000...
DMP / DWH / TMS / etc.
let dataLayer = new Array()
dataLayer.push({
"revenue_Q1_2014" : "15677998.00",
"revenue_Q2_2014" :...
Cure II: The Process
@SimoAhava from @ReaktorNow | #SPWK | 5 Feb 2016
Cure II: The Process
Involve, involve, involve
@SimoAhava from @ReaktorNow | #SPWK | 5 Feb 2016
An iterative, agile
process is necessary for
optimal utilization of a
TMS.
Definition of Done
Definition of Done
Developed features
do not impede
measurement.
Developed features
are trackable.
Sprint
Definition of Done
Developed features
do not impede
measurement.
Developed features
are trackable.
Sprint
If necessary, fe...
Definition of Done
Developed features
do not impede
measurement.
Developed features
are trackable.
Sprint
If necessary, fe...
Constant participation
Constant participation
Transparency
Cure III: Empowerment
@SimoAhava from @ReaktorNow | #SPWK | 5 Feb 2016
Cure III: Empowerment
We are all hybrid beings
@SimoAhava from @ReaktorNow | #SPWK | 5 Feb 2016
The entire life cycle of a
single data point, from
collection to reports,
requires knowledge and
expertise to manage.
#4 D...
Developer facilitation is
crucial to data quality
and optimized data
collection.
1: Education
1. JavaScript: www.codecademy.com, www.codeschool.com,
Professional JavaScript for Web Developers, DOM
Enlightenment…
2. D...
treat content as a product2: hybrid skills
"Business owner"

- No operational skills

+ Strategic
"Developer"

- Uncooperative

+ Methodical
"Marketer"

- Bully

+ C...
+ Passionate, actively interested

+ Understands ever-changing requirements

+ Good grasp of digital tech

+ Statistical m...
treat content as a product3: Passionate interest
+ Dedicated sandbox


+ Website or blog to test
new ideas on


+ Test and debug setups in
Google Analytics and
Google Tag ...
Hire to educate, not to delegate
PO
Developer Analyst
Hire to educate, not to delegate
PO
Developer Analyst
Data is difficult
@SimoAhava from @ReaktorNow | #SPWK | 5 Feb 2016
Data is difficult
Data quality is earned, not acquired
@SimoAhava from @ReaktorNow | #SPWK | 5 Feb 2016
Thank you!
simo.ahava@reaktor.com
www.simoahava.com
Twitter: @SimoAhava
Google+: +SimoAhava
Data is difficult - http://goo...
SuperWeek 2016 - Garbage In Garbage Out: Data Quality in a TMS World
SuperWeek 2016 - Garbage In Garbage Out: Data Quality in a TMS World
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Slides from my talk at the SuperWeek analytics conference. The focus was on organization transformation necessary to improve data quality, especially when using a tag management solution like Google Tag Manager.

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SuperWeek 2016 - Garbage In Garbage Out: Data Quality in a TMS World

  1. 1. Reaktor Mannerheimintie 2 00100, Helsinki Finland tel: +358 9 4152 0200 www.reaktor.com info@reaktor.com Confidential ©2015 Reaktor All rights reserved garbage in, garbage outData quality in a TMS world Simo Ahava Senior Data Advocate
  2. 2. Simo Ahava Senior Data Advocate, Reaktor Google Developer Expert, Google Analytics Blogger, developer, www.simoahava.com Twitter-er, @SimoAhava Google+:er, +SimoAhava
  3. 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. 4. Plug-and-play Analytics @SimoAhava from @ReaktorNow | #SPWK | 5 Feb 2016
  5. 5. Plug-and-play Analytics Data quality isn’t acquired — it’s earned. @SimoAhava from @ReaktorNow | #SPWK | 5 Feb 2016
  6. 6. from online-behavior.com
  7. 7. from online-behavior.com
  8. 8. Claim 1: Data quality is destroyed by laziness and lack of ambition.
  9. 9. Claim 2: A TMS empowers developers more than others.
  10. 10. The root of all evil @SimoAhava from @ReaktorNow | #SPWK | 5 Feb 2016
  11. 11. The root of all evil The "project" @SimoAhava from @ReaktorNow | #SPWK | 5 Feb 2016
  12. 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. 13. The project is often a series of handovers, breeding non- involvement.
  14. 14. Specification Implementation Analysis Results
  15. 15. Specification Implementation Analysis Results Business
 owner
  16. 16. Specification Implementation Analysis Results Business
 owner Marketing
  17. 17. Specification Implementation Analysis Results Business
 owner Marketing Developer
  18. 18. This leads necessarily to silos, which have entry and exit conditions.
  19. 19. Implementation Analysis Results Business
 owner Marketing Developer Specification
  20. 20. Implementation Analysis Results Business
 owner Marketing Developer Specification
  21. 21. Implementation Analysis Results Business
 owner Marketing Developer Specification
  22. 22. Implementation Analysis Results Business
 owner Marketing Developer Specification
  23. 23. Implementation Analysis Results Business
 owner Marketing Developer Specification
  24. 24. Implementation Analysis Results Business
 owner Marketing Developer Specification
  25. 25. Silos, so what? @SimoAhava from @ReaktorNow | #SPWK | 5 Feb 2016
  26. 26. Silos, so what? As long as the work gets done, right? @SimoAhava from @ReaktorNow | #SPWK | 5 Feb 2016
  27. 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. 28. Do these sound familiar:
  29. 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. 30. Ridiculously ugly and ineffective JavaScript hacks for measurement points which should be tackled in the Data Layer.
  31. 31. Hiding behind data, and passing blame to other silos. Could someone fix the Bounce Rate metric on our site?
  32. 32. Analytics feature requests are deprioritized, and deployed extremely infrequently. Fix transactionRevenue to show revenue, not customer weight.
  33. 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. 34. These are symptoms of data being treated as a project outcome.
  35. 35. Cure I: The Data Layer @SimoAhava from @ReaktorNow | #SPWK | 5 Feb 2016
  36. 36. Cure I: The Data Layer Using technology to solve communication problems @SimoAhava from @ReaktorNow | #SPWK | 5 Feb 2016
  37. 37. Typically, there are three definitions of Data Layer that we use in the digital world.
  38. 38. 1. Set of business requirements
 for tracking digital assets,
 visits, and visitors.
  39. 39. 1. Set of business requirements
 for tracking digital assets,
 visits, and visitors. 2. Encoded, global data structure, accessed and modified by connected platforms.
  40. 40. 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.
  41. 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. dataLayer.push({ 'pageType' : 'home' }); google_tag_manager['GTM-123']
 .dataLayer .set('pageType', 'home');
  42. 42. Across all three definitions, the purpose of a Data Layer is simple:
  43. 43. DMP / DWH / TMS / etc. X X Actions Presentation Data Layer
  44. 44. 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.
  45. 45. The Data Layer is a joint venture, where people and systems communicate across silos.
  46. 46. 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' })
  47. 47. 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" })
  48. 48. Cure II: The Process @SimoAhava from @ReaktorNow | #SPWK | 5 Feb 2016
  49. 49. Cure II: The Process Involve, involve, involve @SimoAhava from @ReaktorNow | #SPWK | 5 Feb 2016
  50. 50. An iterative, agile process is necessary for optimal utilization of a TMS.
  51. 51. Definition of Done
  52. 52. Definition of Done Developed features do not impede measurement. Developed features are trackable. Sprint
  53. 53. 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
  54. 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 Attribute syntax is correct for tracking. Data Layer object syntax is correct. Task
  55. 55. Constant participation
  56. 56. Constant participation Transparency
  57. 57. Cure III: Empowerment @SimoAhava from @ReaktorNow | #SPWK | 5 Feb 2016
  58. 58. Cure III: Empowerment We are all hybrid beings @SimoAhava from @ReaktorNow | #SPWK | 5 Feb 2016
  59. 59. The entire life cycle of a single data point, from collection to reports, requires knowledge and expertise to manage. #4 Data is difficult
  60. 60. Developer facilitation is crucial to data quality and optimized data collection.
  61. 61. 1: Education
  62. 62. 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…
  63. 63. treat content as a product2: hybrid skills
  64. 64. "Business owner"
 - No operational skills
 + Strategic "Developer"
 - Uncooperative
 + Methodical "Marketer"
 - Bully
 + Consultative
  65. 65. + 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
  66. 66. treat content as a product3: Passionate interest
  67. 67. + 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
  68. 68. Hire to educate, not to delegate PO Developer Analyst
  69. 69. Hire to educate, not to delegate PO Developer Analyst
  70. 70. Data is difficult @SimoAhava from @ReaktorNow | #SPWK | 5 Feb 2016
  71. 71. Data is difficult Data quality is earned, not acquired @SimoAhava from @ReaktorNow | #SPWK | 5 Feb 2016
  72. 72. 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
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Slides from my talk at the SuperWeek analytics conference. The focus was on organization transformation necessary to improve data quality, especially when using a tag management solution like Google Tag Manager.

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