Was Big Data worth it? We were promised a data revolution when Big Data and Hadoop exploded onto the scene – but those technologies brought with them ungoverned, underexploited, complex environments that didn’t solve the analytical problems they were supposed to. All is not lost, however. This webcast explores three important things we’ve learned from Big Data that can be applied to every kind of data environment: modern approaches to data that exploit the flexibility and power of Big Data without losing the governance and management our businesses need.

1. PHIL BOWERMASTER

2. The
WORLD TRANSFORMED

3. FAST FORWARD
onthe
WORLD TRANSFORMED

6. BIG DATA

7. “BIG
DATA”

8. A MODERN APPROACH
to
DATAINTEGRATION
&
MASTERDATAMANAGEMENT

9. AMODERNAPPROACH
TODIANDMDM
• MappingData
• BusinessvsTechies
• DatainContext

10. JAKE FREIVALD

11. A Modern Approach
to Data Integration
and MDM
Jake Freivald, Information Builders

12. Problems with Normal Data Integration Processes
Data modeling. Too much time spent coping with slight changes in
our business data
Business/IT alignment. Data architects, DBAs, and others can’t
communicate with businesspeople
Processes. Too much detail lost by handing off responsibility for
business data to different people

13. Problem: Data Modeling
Too much time spent coping with slight changes in our business data
Johann Sebastian Bach
Given Middle Family
= J.S. Bach

14. ChenYi
Problem: Data Modeling
Johann Sebastian Bach
Given Middle Family
= J.S. Bach
= Chen Yi
Too much time spent coping with slight changes in our business data

15. ChenYi
Problem: Data Modeling
Johann Sebastian Bach
Ludwig van Beethoven
Given Middle FamilyHon.
= J.S. Bach
= Chen Yi
= L. van Beethoven
Too much time spent coping with slight changes in our business data

16. ChenYi
Problem: Data Modeling
Johann Sebastian Bach
Dmitri Dmitriyevich Shostakovich
Ludwig van Beethoven
Given Middle FamilyPatronymicHon.
= J.S. Bach
= Chen Yi
= L. van Beethoven
= D. Shostakovich
Too much time spent coping with slight changes in our business data

17. ChenYi
Problem: Data Modeling
Johann Sebastian Bach
Dmitri Shostakovich
Ludwig van Beethoven
Mohamed Mougi
Muhammad Qasabgi
Given Middle FamilyHon.
Dmitriyevich
el
al
Patronymic Art.
= J.S. Bach
= Chen Yi
= L. van Beethoven
= D. Shostakovich
= M. el-Mougi
= M. al-Qasabgi
Now I can alphabetize and abbreviate correctly.
Too much time spent coping with slight changes in our business data

18. Mougi = M. el-Mougi
= M. al-Qasabgi
Problem: Data Modeling
Johann Sebastian Bach
Given Middle FamilyHon.
Dmitri ShostakovichDmitriyevich
Mohamed el
Muhammad Qasabgial
Patronymic Art.
Ludwig van Beethoven
ChenYi
= J.S. Bach
= Chen Yi
= L. van Beethoven
= D. Shostakovich
Repeated changes in operational systems’ row-and-column structures
Too much time spent coping with slight changes in our business data

19. Problem: Data Modeling
Ripple effects of changes in one system lead to changes in others
Mougi
Johann Sebastian Bach
Given Middle FamilyHon
Dmitri ShostakovichDmitriyevich
Mohamed el
Muhammad Qasabgial
Patronymic Art
Ludwig van Beethoven
ChenYi
Operational, designed for transactions

20. Problem: Data Modeling
Ripple effects of changes in one system lead to changes in others
Mougi
Johann Sebastian Bach
Given Middle FamilyHon
Dmitri ShostakovichDmitriyevich
Mohamed el
Muhammad Qasabgial
Patronymic Art
Ludwig van Beethoven
ChenYi
Operational, designed for transactions
Data warehouse, designed for abstractions
Sebastian
Middle
Dmitriyevich
Patronymic
el
al
Art
Hon
van
Mougi
Bach
Family
Shostakovich
Qasabgi
Beethoven
Chen
Johann
Given
Dmitri
Mohamed
Muhammad
Ludwig
Yi

21. Problem: Data Modeling
Ripple effects of changes in one system lead to changes in others
Mougi
Johann Sebastian Bach
Given Middle FamilyHon
Dmitri ShostakovichDmitriyevich
Mohamed el
Muhammad Qasabgial
Patronymic Art
Ludwig van Beethoven
ChenYi
Operational, designed for transactions
Data warehouse, designed for abstractions
Sebastian
Middle
Dmitriyevich
Patronymic
el
al
Art
Hon
van
Mougi
Bach
Family
Shostakovich
Qasabgi
Beethoven
Chen
Johann
Given
Dmitri
Mohamed
Muhammad
Ludwig
Yi
Data mart, designed for analysis
Mougi
Bach
Family
Shostak
ovich
Qasabg
i
Beetho
ven
Chen
Johann
Given
Dmitri
Moha
med
Muha
mmad
Ludwig
Yi
Mougi
Bach
Shostak
ovich
Qasabg
i
Beetho
ven
Chen
Johann
Dmitri
Moha
med
Muha
mmad
Ludwig
Yi
Mougi
Bach
Shostak
ovich
Qasabg
i
Beetho
ven
Chen
Johann
Dmitri
Moha
med
Muha
mmad
Ludwig
Yi
Mougi
Johann Sebastian Bach
Given Middle FamilyHn
Dmitri ShostakovichDmitriyevich
Mohamed el
Muhammad Qasabgial
Patronymic Art
Ludwig vn Beethoven
ChenYi
Sebastian
Sebastian
Sebastian
el
el
el
Dmitriyevich
Dmitriyevich
Dmitriyevich
Dmitriyevich
Mougi
Johann Sebastian Bach
Given Middle FamilyHn
Dmitri ShostakovichDmitriyevich
Mohamed el
Muhammad Qasabgial
Patronymic Art
Ludwig vn Beethoven
ChenYi
Mougi
Johann Sebastian Bach
Given Middle Family
Dmitri Shostakovich
Mohamed
Muhammad Qasabgi
Ludwig Beethoven
ChenYi
Sebastian
Mougi
Johann Sebastian Bach
Given Middle Family
Dmitri Shostakovich
Mohamed
Muhammad Qasabgi
Ludwig Beethoven
ChenYi
Sebastian
Sebastian
Sebastian
Sebastian
Sebastian

22. Problem: Business/IT Alignment
Data people often can’t communicate with businesspeople
Data architect thinks
▪ Model the data
▪ Govern the data
▪ Watch out for “quick fixes”
IT:
Gets it
That modeling stuff
we just talked about
Business:
Hates it
Business thinks
▪ Modeling, metadata are hindrances
▪ Analytical tools best without governance
▪ IT slows them down

23. Problem: Processes
Too much information lost by distributing responsibility for business data
Cleansing occurs in transformation step: Different rules being fired
Different tools and metadata being used by platform
Loss of timestamps, context, before-and-after: No cross-platform auditability
No comprehensive rollback, alternate history, what-if
Operational
application
Data
warehouse
Cloud
application
Fa
mi
ly
Transformation
Cleansing
Standardization
Transformation
Cleansing
Standardization

24. Fa
mi
ly
Fa
mi
ly
How much time do we
spend mapping one set of
rows and columns
to another?
What We Learned from Big Data
A modern solution:
post-relational for data capture, transformation,
subject-oriented storage (perhaps), and exchange,
rich documents instead of relational models
Operational
application
Data
warehouse
Analytics

25. How much time do we
spend mapping one set of
rows and columns
to another?
What We Learned from Big Data
A modern solution:
post-relational for data capture, transformation,
subject-oriented storage (perhaps), and exchange,
rich documents instead of relational models
Operational
application
Data
warehouse
Analytics

26. Operational
application Data warehouse Analytics
What We Learned from Big Data
A modern solution:
ELT capture/integrate to capture data as it is,
time-stamped apply trustworthy processes to it,
subject-oriented and share it in trusted ways
How much info
do we lose
by distributing
ETL processes?

27. Operational
application
Analytics
Data Capture/Transformation Hub
Transformation
Cleansing
Standardization
Application
to business
use cases
What We Learned from Big Data
How much info
do we lose
by distributing
ETL processes?
A modern solution:
ELT capture/integrate to capture data as it is,
time-stamped apply trustworthy processes to it,
subject-oriented and share it in trusted ways

28. Modern Data Integration: The Omni-Gen Approach
History
We saw the problem, felt the pain
• MDM was the starting point
• Unifying data quality with MDM
• Aligning business users with mastered subjects
• Capturing transactional subjects in MDM store

29. Modern Data Integration: The Omni-Gen Approach
Response
We built software to make ourselves successful
• Immediate capture in automatically generated data hub
• Master data: business-user-oriented, subject-oriented
• Rapid, integrated data quality rules
• Mastered and transactional subjects
• Rapid cycle times to keep the business engaged
• Support and automatically apply best practices

30. Modern Data Integration: The Omni-Gen Approach
Extending Value
We built models that include customer and supplier
Everything you get in Omni-Gen, plus
• Pre-built models
• Cross-model linking
• Pre-built data quality and data governance rules
• Pre-built match/merge rules
• Immediate 360° core view, unlimited extensions
• Supports different consumers with different, but trusted, data

31. Omni-Gen: More Value in Far Less Time
12-181-3 4-6
Project timeline, in months
Traditional
Data management tools
Build-it-yourself development environment
Omni-Gen
Software solution with built-in best practices
MDM, DQ, integration software with rules,
automatically generated data vault, remediation portal,
360° viewer, history, data interfaces, APIs, and feeds
Omnifor
Persona
Software solution with built-in best practices and complete master data models
Data vault model, data onramps; MDM, data quality, and integration software;
MDM and data quality rules, remediation portal, 360° viewer; Data interfaces, APIs,
history, & feeds; Analytical foundation for dashboarding, advanced analytics, more.

32. Discussion