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with Lars von Sneidern
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 sil...
Who is this person? • Yes, Lars is my real name • I am the Customer Data and Insights Lead for Connective DX • What the he...
About Us: • 19 years old • 80 people strong • 2 offices (Portland & Boston) • 5 “Best Places to Work” awards • Recognized ...
Experience Strategy Customer Data & Insights DX Strategy & Roadmap Technology Consulting Data Consulting Innovation Analyt...
Select technology partners
Select Clients
Need Compare Discover Research Purchase Decide Use Get Help Share Personalize PartnerEvangelize Tribe Search Customer Expe...
It starts with a framework; Builds a process; Establishes governance; Links with strategy; Is built with technology; And m...
Known user attributes Voice of customer Observed behavior Known user opinions Known user behaviors Touchpoint opinions Cus...
Customer Data Platforms
The reality of “business” data
What do we mean by business data?
Foible #1: Connecting Silos
What are your silos?
Customer Support
Connecting Data Silos CRM Web Analytics CMS Email Personalization Cobweb Ad serve Hub-and-Spoke Customer Data Platform Per...
Use case: How do we create a single view of customer data? Option 1: Figure out and use a universal ID Option 2: Behavior ...
You and UUIDs
Customer Support
This can get big in non-relational systems
HOLY SHIT WHAT IS GOOGLE DOING
Who’s in charge of your data? …depends on who you ask!
Remember Master Data Management?
These days many people are talking about Governance instead
Regardless, the idea is having one Source of Truth
Foible #2: Scheming the schema
1. Do you have the right data? 2. Are you normal? 3. Can you scale?
Do I have right data?
What are you trying to accomplish?
Remember Data Dictionaries? Yeah, let’s do that more.
To Normalize, or not? IT IS A QUESTION!
Pretty Normal Too Normal?
To Normal or to Not Pros: • Keeps data clean • Is a best-practice • It scales Cons: • Will cause table bloat • It’s exactl...
What about Scalability?
I have seen several examples of PostgreSQL systems that were built to be quick and easy, but had some major performance is...
Foible #3: Ongoing integrity
Noting is as it has always been
Data Drift Three major types: Structural: Changes at the source. Ex: Fields added, deleted, or changed Semantic: The meani...
API “Jitter”
Makes your ETL, SOL
So, what can you do about it?
checking
Foible #4: Making people care
Developers need ____?
Developers need requirements.
Business people need ____?
Business people need insights.
The key is Empathy
Thanks!
Four Short Foibles of Organizational Data
Four Short Foibles of Organizational Data
Four Short Foibles of Organizational Data
Four Short Foibles of Organizational Data
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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.

Published in: Data & Analytics
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Four Short Foibles of Organizational Data

  1. 1. with Lars von Sneidern
  2. 2. Hi! Thanks for letting me crash the party.
  3. 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. 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
  5. 5. 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
  6. 6. 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
  7. 7. Select technology partners
  8. 8. Select Clients
  9. 9. 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.
  10. 10. It starts with a framework; Builds a process; Establishes governance; Links with strategy; Is built with technology; And measures success.
  11. 11. Known user attributes Voice of customer Observed behavior Known user opinions Known user behaviors Touchpoint opinions Customer insights
  12. 12. Customer Data Platforms
  13. 13. The reality of “business” data
  14. 14. What do we mean by business data?
  15. 15. Foible #1: Connecting Silos
  16. 16. What are your silos?
  17. 17. Customer Support
  18. 18. 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
  19. 19. 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
  20. 20. You and UUIDs
  21. 21. Customer Support
  22. 22. This can get big in non-relational systems
  23. 23. HOLY SHIT WHAT IS GOOGLE DOING
  24. 24. Who’s in charge of your data? …depends on who you ask!
  25. 25. Remember Master Data Management?
  26. 26. These days many people are talking about Governance instead
  27. 27. Regardless, the idea is having one Source of Truth
  28. 28. Foible #2: Scheming the schema
  29. 29. 1. Do you have the right data? 2. Are you normal? 3. Can you scale?
  30. 30. Do I have right data?
  31. 31. What are you trying to accomplish?
  32. 32. Remember Data Dictionaries? Yeah, let’s do that more.
  33. 33. To Normalize, or not? IT IS A QUESTION!
  34. 34. Pretty Normal Too Normal?
  35. 35. 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
  36. 36. What about Scalability?
  37. 37. 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.
  38. 38. Foible #3: Ongoing integrity
  39. 39. Noting is as it has always been
  40. 40. 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/
  41. 41. API “Jitter”
  42. 42. Makes your ETL, SOL
  43. 43. So, what can you do about it?
  44. 44. checking
  45. 45. Foible #4: Making people care
  46. 46. Developers need ____?
  47. 47. Developers need requirements.
  48. 48. Business people need ____?
  49. 49. Business people need insights.
  50. 50. The key is Empathy
  51. 51. Thanks!

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