Four Short Foibles of Organizational Data

•

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Anyone that works with data downstream in an organization has seen things go...wrong, while upstream managers and business leaders are being held accountable. Whether it's a failure in process, or something technically goes wrong, working with data is not always easy. What happened? How can we prevent it from happening again? What's next? This talk, given at the Portland Data Science Group on October 27, 2016, uncovers 4 common foibles of working with organizational data.

Datos y análisis

Hi! Thanks for letting me
crash the party.

What’s up
tonight?
• Who I am and what I am doing here
• What does business reality look
like?
• Foible #1: Connecting silos
• Foible #2: Scheming the schema
• Foible #3: Ongoing integrity
• Foible #4: Making people care

Who is this person?
• Yes, Lars is my real name
• I am the Customer Data and
Insights Lead for Connective DX
• What the heck does that mean?
• I make sailors blush
• I love bulleted lists

About Us:
• 19 years old
• 80 people strong
• 2 offices (Portland & Boston)
• 5 “Best Places to Work” awards
• Recognized by Forrester as a leading Digital Experience Agency

Experience
Strategy
Customer Data
& Insights
DX Strategy
& Roadmap
Technology Consulting
Data Consulting
Innovation
Analytics
Experience
Design
Customer Journey
Mapping
Content Strategy
Digital Experience
Design
Experience
Optimization
Technology
Platform & Systems
Consulting
Content Management
Application
Development
Global Content
Delivery
Mobile
Commerce
Digital
Enablement
DX7 Assessment
Tools & Frameworks
Product Acceleration
Training
& Education
Staffing
Connected Expertise

Need
Compare
Discover
Research
Purchase
Decide
Use
Get Help
Share
Personalize
PartnerEvangelize
Tribe
Search
Customer
Experience
Journey
The Customer
Experience
Journey
Earning a unique role in
your customers’ lives.
From function, to value
to meaning.

It starts with a framework;
Builds a process;
Establishes governance;
Links with strategy;
Is built with technology;
And measures success.

Known user
attributes
Voice of
customer
Observed
behavior
Known user
opinions
Known user
behaviors
Touchpoint
opinions
Customer
insights

Connecting Data Silos
CRM
Web
Analytics
CMS
Email
Personalization
Cobweb
Ad
serve
Hub-and-Spoke
Customer
Data
Platform
Personalization
Testing
CRM
Web
Analytics
CMS
Email
Ad
serve
Testing

Use case: How do we create a
single view of customer data?
Option 1:
Figure out and
use a universal
ID
Option 2:
Behavior and
cookie
matching

This can get big in
non-relational
systems

Who’s in charge of
your data?
…depends on who
you ask!

These days many
people are talking
about Governance
instead

Regardless, the idea
is having one
Source of Truth

1. Do you have the right data?
2. Are you normal?
3. Can you scale?

Remember
Data Dictionaries?
Yeah, let’s do that
more.

To Normal or to Not
Pros:
• Keeps data clean
• Is a best-practice
• It scales
Cons:
• Will cause table
bloat
• It’s exactly not easy
• It takes time

I have seen several examples of
PostgreSQL systems that were
built to be quick and easy, but had
some major performance issues as
they didn’t grow out of the Proof of
Concept phase.
If you are planning on success, plan
for scalability too.

Data Drift
Three major types:
Structural:
Changes at the
source.
Ex: Fields added,
deleted, or
changed
Semantic:
The meaning
changes.
Ex: Field itself
doesn’t change,
but what is in it
does
Infrastructure:
Changes in
platform and tech.
Ex: A new platform
is added or is
changed
Source: Girish Pancha
http://www.cmswire.com/big-data/big-datas-hidden-scourge-data-drift/

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Four Short Foibles of Organizational Data

1. with Lars von Sneidern

2. Hi! Thanks for letting me crash the party.

3. What’s up tonight? • Who I am and what I am doing here • What does business reality look like? • Foible #1: Connecting silos • Foible #2: Scheming the schema • Foible #3: Ongoing integrity • Foible #4: Making people care

4. Who is this person? • Yes, Lars is my real name • I am the Customer Data and Insights Lead for Connective DX • What the heck does that mean? • I make sailors blush • I love bulleted lists

6. About Us: • 19 years old • 80 people strong • 2 offices (Portland & Boston) • 5 “Best Places to Work” awards • Recognized by Forrester as a leading Digital Experience Agency

7. Experience Strategy Customer Data & Insights DX Strategy & Roadmap Technology Consulting Data Consulting Innovation Analytics Experience Design Customer Journey Mapping Content Strategy Digital Experience Design Experience Optimization Technology Platform & Systems Consulting Content Management Application Development Global Content Delivery Mobile Commerce Digital Enablement DX7 Assessment Tools & Frameworks Product Acceleration Training & Education Staffing Connected Expertise

8. Select technology partners

9. Select Clients

10. Need Compare Discover Research Purchase Decide Use Get Help Share Personalize PartnerEvangelize Tribe Search Customer Experience Journey The Customer Experience Journey Earning a unique role in your customers’ lives. From function, to value to meaning.

11. It starts with a framework; Builds a process; Establishes governance; Links with strategy; Is built with technology; And measures success.

12. Known user attributes Voice of customer Observed behavior Known user opinions Known user behaviors Touchpoint opinions Customer insights

13. Customer Data Platforms

14. The reality of “business” data

15. What do we mean by business data?

16.

17. Foible #1: Connecting Silos

18. What are your silos?

19. Customer Support

20. Connecting Data Silos CRM Web Analytics CMS Email Personalization Cobweb Ad serve Hub-and-Spoke Customer Data Platform Personalization Testing CRM Web Analytics CMS Email Ad serve Testing

21. Use case: How do we create a single view of customer data? Option 1: Figure out and use a universal ID Option 2: Behavior and cookie matching

22. You and UUIDs

23. Customer Support

24. This can get big in non-relational systems

25. HOLY SHIT WHAT IS GOOGLE DOING

26. Who’s in charge of your data? …depends on who you ask!

27. Remember Master Data Management?

28. These days many people are talking about Governance instead

29. Regardless, the idea is having one Source of Truth

30. Foible #2: Scheming the schema

31. 1. Do you have the right data? 2. Are you normal? 3. Can you scale?

32. Do I have right data?

33. What are you trying to accomplish?

34. Remember Data Dictionaries? Yeah, let’s do that more.

35. To Normalize, or not? IT IS A QUESTION!

36. Pretty Normal Too Normal?

37. To Normal or to Not Pros: • Keeps data clean • Is a best-practice • It scales Cons: • Will cause table bloat • It’s exactly not easy • It takes time

38. What about Scalability?

39. I have seen several examples of PostgreSQL systems that were built to be quick and easy, but had some major performance issues as they didn’t grow out of the Proof of Concept phase. If you are planning on success, plan for scalability too.

40. Foible #3: Ongoing integrity

41. Noting is as it has always been

42. Data Drift Three major types: Structural: Changes at the source. Ex: Fields added, deleted, or changed Semantic: The meaning changes. Ex: Field itself doesn’t change, but what is in it does Infrastructure: Changes in platform and tech. Ex: A new platform is added or is changed Source: Girish Pancha http://www.cmswire.com/big-data/big-datas-hidden-scourge-data-drift/

43. API “Jitter”

44. Makes your ETL, SOL

45. So, what can you do about it?

46. checking

47. Foible #4: Making people care

48. Developers need ____?

49. Developers need requirements.

50. Business people need ____?

51. Business people need insights.

52.

53. The key is Empathy

54. Thanks!