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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Enterprise Data Governance
Leveraging Knowledge Graph & AI in support of a
data-driven organization
Aftab Iqbal, PhD
Information Architect

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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

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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

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Why Data Management?
Data Management is
for everyone!
?

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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

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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)

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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

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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

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How We Do It?
Technology
Processes
Meta Data
APIs
Application Landscape
Knowledge Graph

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Mapping our Data Landscape

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Mapping our Data Landscape

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Mapping our Data Landscape

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Mapping our Data Landscape

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Mapping our Data Landscape

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Insights – Application Complexity
Application comparison by the number of logical
attributes and physical columns

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Insights – Upstream Dependency for an Application

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Insights – Data Flows between 2 Applications

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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
• …

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Q & A
aftab.iqbal@jpmchase.com
https://www.linkedin.com/in/aftabiqbal