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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?

Published in: Data & Analytics
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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

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