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Increasing Agility Through Data Virtualization
May 11th, 2016
BJ Fesq
Chief Data Officer
CIT Group, Inc.
Ravi Shankar
Chief Marketing Officer
2
Abstract
Increasing Agility Through Data Virtualization
Enterprises face a variety of data management challenges that
influence their ability to leverage accurate, meaningful
information, quickly and efficiently. Data virtualization is an
enabling technology which can address many of these
challenges.
This session will explore how data virtualization is being used to
dramatically reduce data proliferation and ensure that all
consumers are working from a single source of the truth. It will
also look at how data virtualization can drive standardization,
measure & improve data quality, abstract data consumers from
data providers, expose data lineage, enable cross-company data
integration, and serve a common provisioning point from which to
access all authoritative sources of data.
3
The Problem: Data Everywhere
• Whether within IT or the business, employees find ways
to access the data they need to do their job.
• Often times, data is copied, processed offline (eg Excel
& Access) , and somehow fed back into the sausage
grinder of data movement that exists in many
enterprises.
• Data is often enriched and adjusted along the way,
potentially resulting in inconsistent information across
internal organizations, sometimes requiring additional
reconciliations and duplicated efforts.
• Self-serve data can be powerful, but requires the guide
rails of standards, access control, certified provisioning
points, and strong data governance.
• Old habits die hard. Culture is a difficult thing to change.
4
Regulatory Backdrop
• Financial Services companies are experiencing unprecedented
regulatory scrutiny with an increased focus on data management
practices.
• Banks need to evolve their critical data flows to provide increased
frequency, granularity, and auditable aggregation of data used to
manage risk.
• Systemically Important Financial Institutions (SIFIs) or “too big to
fail” banks must operate with strict controls around their data and
increasingly mature their data management practices to meet
evolving industry standards.
• Regulatory authorities continue to move the goal post with
publications like BCBS 239, which is positioned as a guideline, but
expected to become a requirement.
• Other industries like Pharma, Insurance and Energy face similar
challenges with their own data management practices and
challenging regulatory requirements.
5
The Solution: The Data Services Layer (or “DSL”)
• Common provisioning point from which to access all authoritative sources of
data.
• Beyond data integration capabilities, the “DSL” provides usage metering,
monitoring of in-flight data movement, and orchestration of data APIs.
• The DSL is not a data repository, it is a framework to leverage data that is
persisted, mastered and managed elsewhere.
• Created with a collection of technologies, from traditional ETL and sftp, to more
modern RESTful interfaces supported by data virtualization and API gateway
technology.
• Provides metadata and lineage around data flows that leverage the DSL.
• Data virtualization can reduce unnecessary copies, the root of data proliferation.
• Consumers need to trust that historical data is durable and consistent
• “Publish ready” APIs don’t just serve up data, they apply data quality monitoring
rules and trigger data stewardship activities.
• Data virtualization is a foundational technology within the DSL.
6
Data Architecture – Key Principles
• One Source of the Truth
• Authoritative sources of data by domains
• Access all data via common provisioning point
• Build for Reuse, Leverage Investments
• Build once, use many – CCAR, Liquidity, Risk
• Avoid point-to-point integration, simplify
• Minimize Data Replication
• Eliminate data redundancy and proliferation
• Eliminate redundant data reconciliation effort$
• Enable Effective Data Governance
• Enforcing policies, standards and procedures
• Defining authoritative source of data
• Efficient data lineage and metadata management
• Monitoring data quality
• Empowering data stewards
• Simplified Architecture, Agile Data Integration
• Faster time-to-market delivery
• Incremental information delivery via standard APIs
• Simplified architecture, data makes minimal hops
SystemsofRecordDataDeliveryAuthoritativeSources
Party Master Finance
Platform
Product Hubs
Loan Hub
Lease Hub
Bank Hub
Mortgages …
Origination Systems Receivable Systems Risk Systems
CCAR
Stress Testing
Data Marts
(Finance, Risk, others)
Reg Reporting Tool
Reference
Data
Data Services Layer
Data Governance and Data Quality Monitoring
Data Services Layer
Data Governance and Data Quality Monitoring
CIT Systems
Data Virtualization in the Target State Architecture
7
We have the technology … Now what?
• Build the team and the infrastructure capacity to provide an enterprise service
• Establish policy requiring all strategic data flows to go through the DSL
• Validate data lineage, ensure data consumption from authoritative sources
• Disassemble the sausage grinder of data movement:
• Start to unwind legacy ETL and rewire strategic data flows through the DSL
• Aspire to have all data movement occur within a “single hop” of the DSL
• Explore metadata discovery tools to understand non-DSL data movement
• Smart automation (not everything) – quality rules, remediation workflow, etc
• Study metering data, understand how data is consumed to help optimize services
• Establish standards around how data is exposed:
• Everyone consumes data via a shared canonical model
• Expose data as services at the finest granularity that makes sense
• Rationalize data service APIs, ensure consistency & referential integrity across business segments
• Establish foundational data management platform with evolutionary path towards a microservices architecture
• Start small, evolve with demand and growth
8
Data Governance is Critical
• Data governance is critical to the success of any data virtualization effort
• Consumers need to trust that data is owned, managed, and certified
• Establish a data governance framework that ensures accountability, empowers owners of data, and fosters a culture of
good data hygiene:
• Firm-wide policy establishing the data governance framework & governing bodies
• Data management committee aligned to senior-most governing body of firm
• Accountable executives on point for data quality by segment
• Data standards against which to measure data quality
• Data stewards empowered to own & remediate critical data elements
• Insightful, actionable metrics / dashboards targeted at executives, stewards, data consumers
• Data quality has many dimensions, prioritize the ones that matter most
• eg completeness, validity, accuracy, timeliness, granularity, etc.
• Use a canonical model with shared terms defined in a firm-wide business glossary
• The Chief Data Officer owns the policy, provides stewardship of the data governance framework, and serves as an
evangelist for good data management practices
• Data virtualization can be an enabling technology for smart data governance
9
Common Data Virtualization Use Cases
Data Virtualization
BIG DATA, CLOUD INTEGRATION
 Advanced Analytics
 Data Warehouse Offloading
 Big Data for Enterprise
 Cloud / SaaS Integration
AGILE BUSINESS INTELLIGENCE
 Logical Data Warehouse
 Virtual Data Marts
 Self-Service BI
 Operational BI / Analytics
SINGLE VIEW APPLICATIONS
 Single Customer View - Call Centers, Portals
 Single Product View - Catalogs
 Single Inventory View - Inventory Reconciliation
 Vertical Specific - Single View of Wells
DATA SERVICES
 Unified Data Services Layer
 Logical Data Abstraction
 Agile Application Development
 Linked Data Services
10
Denodo
The Leader in Data Virtualization
DENODO OFFICES, CUSTOMERS, PARTNERS
Palo Alto, CA.
Global presence throughout North America,
EMEA, APAC, and Latin America.
LEADERSHIP
 Longest continuous focus on data
virtualization – since 1999
 Leader in 2015 Forrester Wave –
Enterprise Data Virtualization
 Winner of numerous awards
CUSTOMERS
300+ customers, including many
F500 and G2000 companies across every
major industry have gained significant
business agility and ROI.
FINANCIALS
Backed by $8B private equity firm.
50+% annual growth; Profitable.
See us at booth
11
Finis
BJ Fesq
Chief Data Officer
CIT Group, Inc.
Ravi Shankar
Chief Marketing Officer

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Increasing Agility Through Data Virtualization

  • 1. Increasing Agility Through Data Virtualization May 11th, 2016 BJ Fesq Chief Data Officer CIT Group, Inc. Ravi Shankar Chief Marketing Officer
  • 2. 2 Abstract Increasing Agility Through Data Virtualization Enterprises face a variety of data management challenges that influence their ability to leverage accurate, meaningful information, quickly and efficiently. Data virtualization is an enabling technology which can address many of these challenges. This session will explore how data virtualization is being used to dramatically reduce data proliferation and ensure that all consumers are working from a single source of the truth. It will also look at how data virtualization can drive standardization, measure & improve data quality, abstract data consumers from data providers, expose data lineage, enable cross-company data integration, and serve a common provisioning point from which to access all authoritative sources of data.
  • 3. 3 The Problem: Data Everywhere • Whether within IT or the business, employees find ways to access the data they need to do their job. • Often times, data is copied, processed offline (eg Excel & Access) , and somehow fed back into the sausage grinder of data movement that exists in many enterprises. • Data is often enriched and adjusted along the way, potentially resulting in inconsistent information across internal organizations, sometimes requiring additional reconciliations and duplicated efforts. • Self-serve data can be powerful, but requires the guide rails of standards, access control, certified provisioning points, and strong data governance. • Old habits die hard. Culture is a difficult thing to change.
  • 4. 4 Regulatory Backdrop • Financial Services companies are experiencing unprecedented regulatory scrutiny with an increased focus on data management practices. • Banks need to evolve their critical data flows to provide increased frequency, granularity, and auditable aggregation of data used to manage risk. • Systemically Important Financial Institutions (SIFIs) or “too big to fail” banks must operate with strict controls around their data and increasingly mature their data management practices to meet evolving industry standards. • Regulatory authorities continue to move the goal post with publications like BCBS 239, which is positioned as a guideline, but expected to become a requirement. • Other industries like Pharma, Insurance and Energy face similar challenges with their own data management practices and challenging regulatory requirements.
  • 5. 5 The Solution: The Data Services Layer (or “DSL”) • Common provisioning point from which to access all authoritative sources of data. • Beyond data integration capabilities, the “DSL” provides usage metering, monitoring of in-flight data movement, and orchestration of data APIs. • The DSL is not a data repository, it is a framework to leverage data that is persisted, mastered and managed elsewhere. • Created with a collection of technologies, from traditional ETL and sftp, to more modern RESTful interfaces supported by data virtualization and API gateway technology. • Provides metadata and lineage around data flows that leverage the DSL. • Data virtualization can reduce unnecessary copies, the root of data proliferation. • Consumers need to trust that historical data is durable and consistent • “Publish ready” APIs don’t just serve up data, they apply data quality monitoring rules and trigger data stewardship activities. • Data virtualization is a foundational technology within the DSL.
  • 6. 6 Data Architecture – Key Principles • One Source of the Truth • Authoritative sources of data by domains • Access all data via common provisioning point • Build for Reuse, Leverage Investments • Build once, use many – CCAR, Liquidity, Risk • Avoid point-to-point integration, simplify • Minimize Data Replication • Eliminate data redundancy and proliferation • Eliminate redundant data reconciliation effort$ • Enable Effective Data Governance • Enforcing policies, standards and procedures • Defining authoritative source of data • Efficient data lineage and metadata management • Monitoring data quality • Empowering data stewards • Simplified Architecture, Agile Data Integration • Faster time-to-market delivery • Incremental information delivery via standard APIs • Simplified architecture, data makes minimal hops SystemsofRecordDataDeliveryAuthoritativeSources Party Master Finance Platform Product Hubs Loan Hub Lease Hub Bank Hub Mortgages … Origination Systems Receivable Systems Risk Systems CCAR Stress Testing Data Marts (Finance, Risk, others) Reg Reporting Tool Reference Data Data Services Layer Data Governance and Data Quality Monitoring Data Services Layer Data Governance and Data Quality Monitoring CIT Systems Data Virtualization in the Target State Architecture
  • 7. 7 We have the technology … Now what? • Build the team and the infrastructure capacity to provide an enterprise service • Establish policy requiring all strategic data flows to go through the DSL • Validate data lineage, ensure data consumption from authoritative sources • Disassemble the sausage grinder of data movement: • Start to unwind legacy ETL and rewire strategic data flows through the DSL • Aspire to have all data movement occur within a “single hop” of the DSL • Explore metadata discovery tools to understand non-DSL data movement • Smart automation (not everything) – quality rules, remediation workflow, etc • Study metering data, understand how data is consumed to help optimize services • Establish standards around how data is exposed: • Everyone consumes data via a shared canonical model • Expose data as services at the finest granularity that makes sense • Rationalize data service APIs, ensure consistency & referential integrity across business segments • Establish foundational data management platform with evolutionary path towards a microservices architecture • Start small, evolve with demand and growth
  • 8. 8 Data Governance is Critical • Data governance is critical to the success of any data virtualization effort • Consumers need to trust that data is owned, managed, and certified • Establish a data governance framework that ensures accountability, empowers owners of data, and fosters a culture of good data hygiene: • Firm-wide policy establishing the data governance framework & governing bodies • Data management committee aligned to senior-most governing body of firm • Accountable executives on point for data quality by segment • Data standards against which to measure data quality • Data stewards empowered to own & remediate critical data elements • Insightful, actionable metrics / dashboards targeted at executives, stewards, data consumers • Data quality has many dimensions, prioritize the ones that matter most • eg completeness, validity, accuracy, timeliness, granularity, etc. • Use a canonical model with shared terms defined in a firm-wide business glossary • The Chief Data Officer owns the policy, provides stewardship of the data governance framework, and serves as an evangelist for good data management practices • Data virtualization can be an enabling technology for smart data governance
  • 9. 9 Common Data Virtualization Use Cases Data Virtualization BIG DATA, CLOUD INTEGRATION  Advanced Analytics  Data Warehouse Offloading  Big Data for Enterprise  Cloud / SaaS Integration AGILE BUSINESS INTELLIGENCE  Logical Data Warehouse  Virtual Data Marts  Self-Service BI  Operational BI / Analytics SINGLE VIEW APPLICATIONS  Single Customer View - Call Centers, Portals  Single Product View - Catalogs  Single Inventory View - Inventory Reconciliation  Vertical Specific - Single View of Wells DATA SERVICES  Unified Data Services Layer  Logical Data Abstraction  Agile Application Development  Linked Data Services
  • 10. 10 Denodo The Leader in Data Virtualization DENODO OFFICES, CUSTOMERS, PARTNERS Palo Alto, CA. Global presence throughout North America, EMEA, APAC, and Latin America. LEADERSHIP  Longest continuous focus on data virtualization – since 1999  Leader in 2015 Forrester Wave – Enterprise Data Virtualization  Winner of numerous awards CUSTOMERS 300+ customers, including many F500 and G2000 companies across every major industry have gained significant business agility and ROI. FINANCIALS Backed by $8B private equity firm. 50+% annual growth; Profitable. See us at booth
  • 11. 11 Finis BJ Fesq Chief Data Officer CIT Group, Inc. Ravi Shankar Chief Marketing Officer