Phil Simpson, Red Hat

1. Foundational technologies for the data-driven business
PUTTING DATA TO WORK
Phil Simpson
Sr. Principal Product Marketing Manager
1

2. Topics
● The data challenge
● A solution framework
● Foundational technologies
● Use case - anti-money laundering

3. Data is Everywhere
● 90% of all data in existence today was created
in the past two years1
● Worldwide data is expected to hit 175
zettabytes by 2025, representing a 61% CAGR2
● 90 ZB of this data will be from IoT devices in
20252
● 51% of the data will be in data centers and 49%
will be in the public cloud2
● 80% of data will be unstructured by 20253
1. PC Reviews, “90% of the Big Data We Generate is an Unstructured Mess.” https://www.pcmag.com/news/364954/90-percent-of-the-big-data-
we-generate-is-an-unstructured-me
2. DC, “Data Age 2025”. https://www.seagate.com/our-story/data-age-2025/
3. https://solutionsreview.com/data-management/80-percent-of-your-data-will-be-unstructured-in-five-years/

4. The Data Opportunity
If we could consume all the data available we
could:
● Recognize important business events as
they happen
● Respond intelligently to business events
● Automate business functions
● Continuously improve business
operations
● Better serve customers
● ...

5. The Data Challenge
The data we have is:
● Inconsistent
● Duplicated
● Missing or inaccessible
● Irrelevant
● Unintelligible
● Too big
● Too old
● Created/changed faster than it can
be consumed
● ...

6. Putting Data to Work
A Framework

7. Intelligent Data Lifecycle
ActIngest InterpretTransform

8. Intelligent Data Lifecycle
ActIngest InterpretTransform

9. Intelligent Data Lifecycle
ActIngest InterpretTransform
● Messaging
● Data
Virtualization
● Change
Data
Capture

10. Intelligent Data Lifecycle
ActIngest InterpretTransform
● Messaging
● Data
Virtualization
● Change
Data
Capture
● ETL
● Rules
Engines

11. Intelligent Data Lifecycle
ActIngest InterpretTransform
● Messaging
● Data
Virtualization
● Change
Data
Capture
● ETL
● Rules
Engines
● Rules
engines
● CEP
● AI/ML
● NLP

12. Intelligent Data Lifecycle
ActIngest InterpretTransform
● Messaging
● Data
Virtualization
● Change
Data
Capture
● ETL
● Rules
Engines
● Rules
engines
● CEP
● AI/ML
● NLP
● BPM
● RPA

13. Intelligent Data Lifecycle
ActIngest InterpretTransform
● Messaging
● Data
Virtualization
● Change
Data
Capture
● ETL
● Rules
Engines
● Rules
engines
● CEP
● AI/ML
● NLP
● BPM
● RPA

14. Putting Data to Work
Foundational Technologies

15. Messaging
https://kafka.apache.org/
ActIngest InterpretTransform

16. Data Access
DATA VIRTUALIZATION CHANGE DATA CAPTURE
Federates multiple relational and non-relational sources
Compatible with existing SQL clients
Live CRUD access to back-end data sources
Client-driven interaction
Database changes externalized as events
Process data change events as they happen
Distributed state updates, caching, data lake, etc.
Event-driven interaction
ActIngest InterpretTransform

17. Rules Engines
Data (semi-
structured)
Rules Engine
Rules
ActIngest InterpretTransform

18. Machine Learning
Machine
Learning
Package
New
data
Predictive
Model Model server
Training data
ActIngest InterpretTransform

19. Business Process Management
Source: Object Management Group
ActIngest InterpretTransform

20. Robotic Process Automation
Transactional Processing
Accounting & Reporting
Account Management
Image Capture
Analytics
Spreadsheet integration
Email
Word
PDF’s
Data processing &
Storage
Web site integration
ActIngest InterpretTransform

21. RPA and BPM Together
Source: Object Management Group
ActIngest InterpretTransform

22. 22
Anti-Money Laundering
Open System Architecture
Marius Bogoevici
Chief Solutions Architect, Financial Services
Anti-MoneyLaunderingOpenSystemArchitecture

23. 23
▸ Increasing variety of financial products & channels
▸ Increasing amount of data sources and formats
▸ Exponential data volume growth
Complexity
▸ Money laundering regulations increasingly demand proactive measures
▸ Evolving regulatory requirements
▸ Automated decision making and reporting
Responsiveness
▸ Scaling - increased traffic throughput
▸ Digital transformation and modernization
Operation
Business Drivers
▸ Clear, explainable decision making processes
▸ Independent audit capabilities
▸ Visibility into process history and performance
Transparency

24. 24
▸ Glean insight from existing long-term and real-time data sources
▸ Integrate new types of data, structured and unstructured easily
▸ Single customer view
▸ Processed monitored via analytics and and visualized by easy-to-use dashboards
Data-driven
▸ Implement customer due diligence (CDD), enhanced due diligence (EDD), and universal
beneficiary ownership (UBO)
▸ Dynamically creating and changing business workflows, policies and procedures
▸ Automated decision making and reporting
Compliant
Outcomes
▸ Reduce false positives
▸ Reduce TCO and avoid vendor lock-in
▸ Scaling infrastructure to analyze a growing volume of transactions
Efficient

25. Anti-Money Laundering AI/ML Workflow
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26. Anti-Money Laundering Open System Architecture at a glance
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Characteristics
● Modular and decentralized
● Microservice architecture
● Event-driven and streaming
oriented
● Transparent and observable
● Agile integration ready
● Hybrid-and multi-cloud ready

27. Red Hat Intelligent Data Platforms
RUNTIMES INTEGRATION AUTOMATION
RED HAT ENTERPRISE LINUX ECOSYSTEM
Hardware, Virtualization, Cloud and Service Provider Certifications
APPLICATION LIFECYCLE MANAGEMENT
CONTAINER ORCHESTRATION AND MANAGEMENT
ENTERPRISE CONTAINER HOST
App
Monitoring
Infra
Monitoring
DATA GRID
AMQ BROKER

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