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Page 1
Enterprise Data Governance
Leveraging Knowledge Graph & AI in support of a
data-driven organization
Aftab Iqbal, Ph...
Page 2
Actively manage our data
and make it a first class function
Data Strategy – Our Mission
Generating Business Value
T...
Page 3
Key themes to our
Data Strategy
Records
Management
Regional
Compliance
Landscape
Documentation
Data
Protection
 Ce...
Page 4
Why Data Management?
Data Management is
for everyone!
?
Page 5
Hard to find data … when you
have a lot of it
Why Data Management?
UNSTRUCTURED
JPMC TECHNOLOGY LOCATIONS
StorageTi...
Page 6
Vision
Make Data a first
class function
To precisely
understand
what data we
have and,
where it goes
HOW
WHY
Better...
Page 7
Data Management Drivers
Data Landscape
What data
do I need?
What data do
we have?
Where is my
data from?
Where shou...
Page 8
Strategic components in the
Data Lifecycle
Ideally, consume conformed data
from Authoritative Data Source
Re-Use / ...
Page 9
How We Do It?
Technology
Processes
Meta Data
APIs
Application Landscape
Knowledge Graph
Page 10
Mapping our Data Landscape
Page 11
Mapping our Data Landscape
Page 12
Mapping our Data Landscape
Page 13
Mapping our Data Landscape
Page 14
Mapping our Data Landscape
Page 15
Insights – Application Complexity
Application comparison by the number of logical
attributes and physical columns
Page 16
Insights – Upstream Dependency for an Application
Page 17
Insights – Data Flows between 2 Applications
Page 18
Knowledge Graph
Data in Place
Data in Motion
Data in Situation
Regulations
Key Takeaways & Future Directions
Data ...
Page 19
Q & A
aftab.iqbal@jpmchase.com
https://www.linkedin.com/in/aftabiqbal
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Enterprise Data Governance: Leveraging Knowledge Graph & AI in support of a data-driven organization

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As one of the largest financial institutions worldwide, JP Morgan is reliant on data to drive its day-to-day operations, against an ever evolving regulatory regime. Our global data landscape possesses particular challenges of effectively maintaining data governance and metadata management.

The Data strategy at JP Morgan intends to:

a) generate business value
b) adhere to regulatory & compliance requirements
c) reduce barriers to access
d) democratize access to data

In this talk, we show how JP Morgan leverages semantic technologies to drive the implementation of our data strategy. We demonstrate how we exploit knowledge graph capabilities to answer:

1) What Data do I need?
2) What Data do we have?
3) Where does my Data come from?
4) Where should my Data come from?
5) What Data should be shared most?

Enterprise Data Governance: Leveraging Knowledge Graph & AI in support of a data-driven organization

  1. 1. Page 1 Enterprise Data Governance Leveraging Knowledge Graph & AI in support of a data-driven organization Aftab Iqbal, PhD Information Architect
  2. 2. Page 2 Actively manage our data and make it a first class function Data Strategy – Our Mission Generating Business Value Transparency and access are necessary to deliver value Well described data environments are our “data mission” and are linked to our business strategy and core operations Reducing Barriers to Access Regulatory Compliance Financial and privacy regulations are increasing in complexity Regulators expect dependable, consistent reporting We must safeguard our client and firm data Democratize data analytics capabilities Data Lake is our platform for easily accessible data
  3. 3. Page 3 Key themes to our Data Strategy Records Management Regional Compliance Landscape Documentation Data Protection  Centralized tooling to automate data management activities  Robust scanning, identification, and masking capabilities  Metadata management through the entire lifecycle Data Management Approach Data Lake Governance Archive Service
  4. 4. Page 4 Why Data Management? Data Management is for everyone! ?
  5. 5. Page 5 Hard to find data … when you have a lot of it Why Data Management? UNSTRUCTURED JPMC TECHNOLOGY LOCATIONS StorageTiers File (NAS) Block (SAN) Object (S3) Mainframe Storage Public Cloud ContentTypes STRUCTURED End User Device (e.g. Laptop) Relational Hadoop Other Non Relational TechLocations Branch Tapes Data Center Other Other SaaS Time Series
  6. 6. Page 6 Vision Make Data a first class function To precisely understand what data we have and, where it goes HOW WHY Better data > better information Better information > better decisions Better decisions > business value AI/ML Data Standards Business Glossary Processes (DC-SDLC) Platform (data catalog)
  7. 7. Page 7 Data Management Drivers Data Landscape What data do I need? What data do we have? Where is my data from? Where should my data come from? What data should be shared most? Data Requirements Data In Place Data In Motion (Lineage) Authority (ADS, SoR) Reference Data Reducing Barriers to Access
  8. 8. Page 8 Strategic components in the Data Lifecycle Ideally, consume conformed data from Authoritative Data Source Re-Use / Use shared services, build only when needed Approach activities in lowest risk manner possible Minimize duplicative and / or redundant data transformation Present data once through a single mechanism Only duplicate data if absolutely necessary Data Management Foundation
  9. 9. Page 9 How We Do It? Technology Processes Meta Data APIs Application Landscape Knowledge Graph
  10. 10. Page 10 Mapping our Data Landscape
  11. 11. Page 11 Mapping our Data Landscape
  12. 12. Page 12 Mapping our Data Landscape
  13. 13. Page 13 Mapping our Data Landscape
  14. 14. Page 14 Mapping our Data Landscape
  15. 15. Page 15 Insights – Application Complexity Application comparison by the number of logical attributes and physical columns
  16. 16. Page 16 Insights – Upstream Dependency for an Application
  17. 17. Page 17 Insights – Data Flows between 2 Applications
  18. 18. Page 18 Knowledge Graph Data in Place Data in Motion Data in Situation Regulations Key Takeaways & Future Directions Data Profiles ML/AI • Identify and protect sensitive data • Reduce digital footprint by archiving and destroying data • …
  19. 19. Page 19 Q & A aftab.iqbal@jpmchase.com https://www.linkedin.com/in/aftabiqbal
  • lizlundeberg

    Dec. 6, 2019

As one of the largest financial institutions worldwide, JP Morgan is reliant on data to drive its day-to-day operations, against an ever evolving regulatory regime. Our global data landscape possesses particular challenges of effectively maintaining data governance and metadata management. The Data strategy at JP Morgan intends to: a) generate business value b) adhere to regulatory & compliance requirements c) reduce barriers to access d) democratize access to data In this talk, we show how JP Morgan leverages semantic technologies to drive the implementation of our data strategy. We demonstrate how we exploit knowledge graph capabilities to answer: 1) What Data do I need? 2) What Data do we have? 3) Where does my Data come from? 4) Where should my Data come from? 5) What Data should be shared most?

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