Five phases Flixmobilty went through on their journey to decentralized cross-department data analysis and business intelligence on direct customer feedback.

1. Customers feedback – from data mess to data mesh
Taras Slipets
Staff Data Engineer

2. About Flix
2

3. About Flix
3

4. About me
4
{
“First name”: “Taras”,
“Last name”: “Slipets”,
“Occupation”: “Staff Data Engineer”,
“Company”: “Flix”,
“Interests”: [
“Software Engineering”,
“Music”,
“Skiing”
]
}

5. About me
5
Products portfolio:
• Customer Surveys
• Customer 360 profile
• Customer Insights (CLV, churn, etc.)
• Tech GDPR
• Multi-country consents management system
• PII data recognition

6. High-level customer feedback data flow at FlixBus
6
Surveys
Real-time
streaming
Data Cloud BI reporting
and analytics

7. Core customers survey - NPS
7
https://trustmary.com/nps-net-promoter-score/what-is-nps-and-how-do-you-measure-it/

8. 5 Phases
8

9. Phase 1: Decoupling data layer from Monolithic
database
9

10. Phase 1: State before
10
Application (s)
Surveys
RDBMS
BI reporting
and analytics

11. Phase 1: State after
11
Application (s)
Surveys
RDBMS
BI reporting
and analytics
RDBMS

12. Phase 2: Separation of content-management
routines
12

13. Phase 2: State before
13
Surveys BI reporting
and analytics
RDBMS

14. Phase 2: State after
14
Surveys BI reporting
and analytics
RDBMS

15. Phase 3:
Design of EAV model for customer feedback data
15

16. Phase 3:
Design of EAV model for customer feedback data
16
Id First Name Last Name Age
1 John Lennon 82
2 Paul McCartney 80
3 George Harrison 82
4 Ringo Starr 79

17. Phase 3:
Design of EAV model for customer feedback data
17
ID Attribute
1 First Name
2 Last Name
3 Age
Entity ID Attribute ID Value ID
1 1 1
1 2 5
1 3 9
2 1 2
2 2 6
2 3 10
3 1 3
3 2 7
3 3 9
4 1 4
4 2 8
4 3 11
ID Values
1 John
2 Paul
3 George
4 Ringo
5 Lennon
6 McCartney
7 Harrison
8 Starr
9 82
10 80
11 79

18. Phase 3:
Design of EAV model for customer feedback data
18
Id First Name Last Name Age
1 John
2 McCartney 80
3 George 82
4 79

19. Phase 3:
Design of EAV model for customer feedback data
19
ID Attribute
1 First Name
2 Last Name
3 Age
Entity ID Attribute ID Value ID
1 1 1
1 3 4
2 2 3
2 3 5
3 1 2
3 3 4
4 3 6
ID Values
1 John
2 George
3 McCartney
4 82
5 80
6 79

20. Phase 4: Unification of historical and new tools
ecosystem
20
New tool
Historical tool
RDBMS
Snowflake
BI reporting
and analytics
Kafka

21. Phase 4: Unification of historical and new tools
ecosystem
21
BI reporting
and analytics
New tool
Snowflake
Historical tool
Kafka

22. Phase 4: Unification of historical and new tools
ecosystem
22
BI reporting
and analytics
New tool
Snowflake
Kafka

23. Phase 5 [in progress]: Domain-specific data-marts
23

24. Next steps
24
• PoC for Snowflake Data Exchange
• Unify/aggregate repetitive documentation sources
• Bringing Data Mesh concepts to the larger scope (e.g. streaming ecosystem)

25. Lessons learned
25
• Start simple, bring quick business value
• Get direct feedback, don’t assume
• Keep changes incremental
• Abstract out presentation facades from
internal storage format
• Data Mesh is not a silver bullet

26. Q&A