In this talk, Simo will explore a two-tiered approach to data in organizations. On the one hand, data is the footprint of an organisation’s maturity and ability to communicate across silos. It is brutally honest in revealing the flaws and imperfections of the environment, especially when it comes to building pipelines that gather data from all corners of the organization. Simo will show how healthy communication structures are instrumental in building robust data collection and processing pipelines, and he will share his views on how taking a leaf out of the agile workbook will help in healing the broken communication structures of the organization. On the other hand, data is the product of tools and platforms used for data collection, processing, ETL, and querying. This means that in order for these tools to be actually useful, they must be customized to accommodate the idiosyncrasies of the organization using the tools. In this part of the talk, Simo zooms in from the bird’s-eye view of the first half of the talk to look at web analytics specifically, and how tools like Google Analytics must be customized to be able to produce any useful insight into the machinations of the organization. The talk will leave you with inspiration to look beyond the silos of your organization, and your fingers will itch at the opportunity to put all the customization tips to use with your web analytics tool.

1. There’s data everywhere!
@SimoAhava from @8_bit_sheep

2. There’s data everywhere!
@SimoAhava from @8_bit_sheep

3. Simo Ahava
Partner, co-founder, 8-bit-sheep
Google Developer Expert, Google Analytics
Blogger, developer, www.simoahava.com
Twitter-er, @SimoAhava

4. data quality is a symptom

5. data quality is a symptom
communication structures are the cure

6. How can data be(come) meaningful?
Tool
expertise
Quick wins. Low-hanging fruit. Parlor tricks. Tactics.

7. How can data be(come) meaningful?
Biz
expertise
Measurement plans. Solutionism. Vacuous hand-waving. Strategies.

8. How can data be(come) meaningful?
Tool
expertise
Biz
expertise
Purposeful execution. Tactics and goals aligned. Territorialism. Finger-pointing.

9. How can data be(come) meaningful?
Tool
expertise
Biz
expertise
Maturity
Holy
grail
Communication.
Participation.
Transparency.
Shared goals.
Agility.
Purpose.

10. Any organization that designs a
system (defined broadly) will
produce a design whose structure
is a copy of the organization's
communication structure.
- (Mel) Conway’s law
Good data quality is an investment.

11. IT
UX
Data
Biz
Marketing
The Project
WORST: Lack of involvement, lack of participation, lack of collaboration, lack of trust.
Encourage multi-disciplinary teams.

12. IT
UX
Marketing
BETTER: Good communication, lack of shared goals, lack of involvement.
Data
Biz
The Project
Encourage multi-disciplinary teams.

13. BEST: Transparency, involvement, shared knowledge and skills, constant learning.
Encourage multi-disciplinary teams.
The Project

14. A consultant should work in a manner that inevitably
makes them redundant.
https://goo.gl/aPvVjb

15. BAD: Experts are hired as extra pairs of hands and as reporting tools.
The Project
Hire for education, not for delegation.

16. BEST: Experts work with the team, teaching, facilitating, building processes.
The Project
Hire for education, not for delegation.

17. pipelines are fragile

18. pipelines are fragile
communication is necessary in all stages

19. communication breakdown results
in rusty pipelines and debt

20. Potential conflicts

21. Naming conventions, unified IDs, authentication, privacy

22. Batch or stream, misaligned schemata, chronology issues, hit validation (+ previous)

23. Separate ETL processes, patch or discard invalid data (+ previous)

24. Aggregation of pipelines (+ previous)

25. Visualizing what, when, and how (+ previous)

26. each individual component is
susceptible to conflict, too

27. Google Analytics Session
Group of interactions that takes place on a website
expiration after 30 minutes of non-interaction
midnight per configured timezone
reacts to campaign changes
referral exclusion override
manual override with session control
interactive vs. non-interactive hits
requires persistent storage
cross-domain tracking
campaign timeout resets session

28. Google Analytics Session
Group of interactions that takes place on a website
expiration after 30 minutes of non-interaction
midnight per configured timezone
reacts to campaign changes
referral exclusion override
manual override with session control
interactive vs. non-interactive hits
requires persistent storage
cross-domain tracking
campaign timeout resets session

29. the schema is inflexible but your
application thereof is not

30. sessionized
the schema is inflexible but your
application thereof is not

31. “default” is the data killer

32. “default” is the data killer
data sources must reflect the idiosyncrasies of YOUR org

33. True
View
Ecom
m
erce
Im
pressions!https://bit.ly/2Vx0A9Z

34. Dev
UX
Marketing
Data
Biz
There is a flaw in the current
model. Impressions are tracked for views
that did not happen.
Identify a flaw in the status quo.

35. Dev
UX
Marketing
Data
Biz
Identify a flaw in the status quo.
Measuring qualified conversions
of individual creatives has always been a
pain point.
Introduce the business problem.

36. Dev
UX
Marketing
Data
Biz
Identify a flaw in the status quo.
We can use more accurate CTR
reports to better design the creatives and
their context in our campaigns.
Introduce the business problem.
Explore the impact of the change.

37. Dev
UX
Marketing
Data
Biz
Identify a flaw in the status quo.
It’s doable with our current
Ecommerce integration combined with
scroll and visibility tracking.
Introduce the business problem.
Explore the impact of the change.
Produce a technological solution.

38. Dev
UX
Marketing
Data
Biz
Identify a flaw in the status quo.
Introduce the business problem.
Explore the impact of the change.
Produce a technological solution.
This will help us choose between
lazy/infinite load and pagination of
impressions, too!
Uncover fringe benefits.

40. Align measurement across
data sources.
Adjust enrichment and ETL
to fit data into schema.
Design the pipeline flow with
queries, reports, and
visualizations as the drivers.

42. Triggers

43. Dummy tag
Triggers

44. Dummy tag
Variables
Triggers

45. Dummy tag
Variables
Triggers
Test

46. Dummy tag
Variables
Triggers
Test

47. Dummy tag
Variables
Triggers
Test

48. Dummy tag
Variables
Triggers
Test

49. Dummy tag
Variables
Triggers
Test

50. Views Clicks CTR Cart adds Purchases Revenue
Quick Buy
Retargeting
Footer
Offers
Promo_Jan
X-sell
62,065
46,281
33,237
26,412
21,745
21,045
1,472
6,162
581
3,181
520
1,270
2.37%
13.31%
1.75%
12.04%
2.39%
5.85%
1,546
58
0
12
306
1,259
594
17
0
3
137
297
€297,638.42
€5,605.92
€0.00
€1,485.92
€11,234.30
€157,169.30

51. Views Clicks CTR Cart adds Purchases Revenue
Quick Buy
Retargeting
Footer
Offers
Promo_Jan
X-sell
62,065
46,281
33,237
26,412
21,745
21,045
1,472
6,162
581
3,181
520
1,270
2.37%
13.31%
1.75%
12.04%
2.39%
5.85%
1,546
58
0
12
306
1,259
594
17
0
3
137
297
€297,638.42
€5,605.92
€0.00
€1,485.92
€11,234.30
€157,169.30
True Qualified Qualified Qualified Qualified Qualified

52. data is difficult

53. data is difficult
data quality is earned, not acquired

54. 1. Data quality is an investment
Tool
expertise
Biz
expertise
Maturity
Holy
grail
Key takeaways:

55. 2. Communication structures are everything
Any organization that designs a
system (defined broadly) will
produce a design whose structure
is a copy of the organization's
communication structure.
- (Mel) Conway’s law
Key takeaways:

56. 3. Customization is necessary, but requires…
Key takeaways:

57. 4. …a robust and consistent data pipeline
Key takeaways:

58. log(`Thank you, ${yourName}`)