Slides: Accelerate and Assure the Adoption of Cloud Data Platforms Using Intelligent Data Automation

Assure and Accelerate The Adoption of
Cloud Data PlatformsD A N N Y S A N D W E L L , D I R E C T O R P R O D U C T M A R K E T I N G , E R W I N I N C .
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The Drivers for Cloud Adoption and Data Platform
Modernization
Digital
Transformation
Business
Continuity
Data Driven
Innovation
Financial
Optimization
© 2020 erwin, Inc. All rights reserved.

The Data Dilemma – IDC 2020
IDC estimates 45
zettabytes of data
created in 2019 and
expected to grow at 26%
compounded annually
over five years to 2024.
95% of organizations
integrating up to six
different types of data
across 10 different types
of data management
technologies as they
manage operations, seal
strategic insights and
make business decisions.
In 2019, 94% of
organizations were
integrating data across
hybrid cloud
environments.

The Need For Data Intelligence – IDC 2020
Effects of data
environment complexity
and the state of
intelligence about data
are being seen in the
efficiency and
effectiveness of data-
native workers.
The 80/20 rule stating the
percentage of time spent
in data discovery and
preparation compared to
the percentage of time
spent in analytics is
getting worse, now
approaching 85/15 as per
the results of the 2019
IDC DII survey.
This survey also told us
that on average, data-
native workers are more
unsuccessful than
successful in their tasks
as they search for,
prepare, govern and
analyze data.

Benefits
• Performance and Scalability
• Elasticity and Agility
• Lower TCO and Future Proof
• More Value From Data
Capabilities
• High Performance Data Store
• Hybrid DBMS Modalities
• Agile Data Integration
• Integrated BI & Analytics
6
Cloud Data Platform Benefits and Capabilities

Data Governance and Intelligence
Migration
Transparency
Documenting
cutting edge
technologies
Data
democratization
enablers
Migrating Legacy Deployments
Time To Value
Conversion
Accuracy
Cost
Containment
7
Challenges To Realizing Modernization Benefits

e
Modernizing Data Architecture
Automate Key Tasks to Accelerate and Assure
Transform &
Deploy Schema
Bulk Load Data
Data
Re-Point Data
Movement
Re-Platform
Data Movement
Repeatable
Dev/Ops
Time To Value Accuracy
GovernanceReduced Costs

Migrate RDMBS structures to the cloud with data modeling
Reverse Engineer Transform Forward Engineer

Data Mapping Documents:
Activating Metadata For Maximum Utility
Data Movement Capture
Abstracted Mapping
Documents
Mapping Exploration
and Activation
Scan and Auto-
Document Code
Import Mappings from
Delimited Files
Manually Specify Mappings
Import Data Model Mappings
Source
Transformation
Target
Lineage Rendering
Impact Analysis
Automated Code Generation

Data Mapping: The “Logical Model” for Data Movement

Automated ETL conversion process
Automated ETL migration complexity assessment
Reverse engineer legacy ETL mappings
Forward engineer target ETL equivalent mappings
Unit test for completeness

Automated ETL migration complexity assessment
StratificationComponentFrequency
LoadDesignPatternsProposedTimeline

Technology Integration
• Cloud-based ETL
• Spark-based ETL
• Big Data initiatives
Legacy ETL mapping conversion to cloud native tools
Automation Benefits
• Consistent, machine-generated code
• Load design pattern standardization
• Significant time and cost savings

Cloud Governance
Enabling democratization of technical assets with a Contextual Business Asset Framework
Mind Map Associations
Technical Assets
Business Terms
Policies & Procedures
Custom Associations

Cloud Governance
Leverage AI to Automate Governance and Intelligence Framework Configuration

Cloud Governance
Automate the Classification of Sensitive Data

erwin Smart Connectors
Optional connectors that enable you to harvest metadata from a wide array of other sources, generate
code, and integrate with ecosystem environments.
Reverse Engineering
Code Generation
Ecosystem Integrations
Testing Automation
Connectors auto-document (reverse engineer) mappings from ETL, BI
Tools, and procedural code.
Connectors to generate (forward engineer) mappings for ETL, ELT
Tools, and procedural code.
Connectors to integrate ecosystem applications both from a process
and meta and/or meta data perspective.
Connectors to connect Test tools (HP ALM/Quality Center), Generate
Test Cases, Generate Test SQL, Generate Validation and Test.

Realize Maximum Business Value From Cloud Data
Platforms
Reduce costs and
mitigate risks when
migrating legacy
applications and data
to the cloud
Increase the precision,
speed, agility and
understanding of
cloud data
deployments
Assure transparency,
compliance and
governance for cloud
data and processes
Increase stakeholder
literacy and optimize
the efficiency and
accuracy of analytics
and other data usage
Cloud Data Platforms

Slides: Accelerate and Assure the Adoption of Cloud Data Platforms Using Intelligent Data Automation

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Slides: Accelerate and Assure the Adoption of Cloud Data Platforms Using Intelligent Data Automation