This document discusses foundational strategies for getting data to the cloud in a trusted manner. It emphasizes that trusted data requires visibility into all data, real-time data sharing across on-premises and cloud environments, and removing data silos. A strong data integration foundation enables centralized analytics, data discovery, data democratization with governance, and next-gen projects like AI/ML. Successful data integration requires understanding architectures, extracting the right legacy data, and scaling delivery as data volumes increase across cloud environments.

1. Foundational strategies
for trusted data
Getting your data to the cloud
Charles Struijvé | Senior Sales Engineer, Data Integration
cstruijve@precisely.com

2. Accuracy
Consistency
Context
Data
integrity is
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3. Trust
Data
integrity is
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4. Trusted data requires
Visibility into all data
Real-time sharing of data
Removal of data silos –
on-prem to the cloud
Strong foundation in
data integration
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5. What does a strong data integration
foundation enable you do to do?
Centralized BI and
analytics
Data discovery Data
democratization
with governance
Next-gen projects
– AI and ML
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6. Data integration, easier said than done
Silos of multi-
structured data
Legacy IT
infrastructure
Data archives
Employees
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7. Trends impacting data integration success
• Almost every enterprise has data silos that
prevent enterprise-wide access to data
• More than half of enterprises rely on legacy
systems to run more than half of their
business-critical applications
• Distributed cloud architectures promise
agility but do not readily integrate with
existing infrastructure
• Cloud data platform market consolidation
creates uncertainty
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8. Ingredients of successful data integration
1. Clear
business case 3. Extract data
2. Understand
architecture 4. Scale delivery
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9. Understand your
architecture
Growing demand for data integration architectures
that are flexible, agile, and adaptable to rapid change.
• Distributed cloud architectures promise agility but
may not readily integrate with existing infrastructure
• New requirements for cloud data platforms may
break current data integration architectures
• Critical to understand business applications that will
be impacted by movement of legacy systems to
the cloud
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10. Shifting your architecture
Source: McKinsey & Company, July 2020
1 2 3 4 5 6
From on-
premises to
cloud-based
data
platforms
From batch
to real-time
data
processing
From pre-
integrated
commercial
solutions, to
modular
approaches
From point-
to-point to
decoupled
data access
From
enterprise
data
warehouses
to domain-
based
architectures
From rigid
data models
to flexible,
extensible
data
schemas
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11. Extracting the right data
Legacy data can provide a treasure-trove of
information that can transform your business when
leveraged via a streaming paradigm.
• Connect applications together, leveraging the
existing transactional capabilities of the current
application platform, and the wealth of new
capabilities of the cloud
• Feed analytics with up-to-date information so your
business runs on current insight
• Port workloads to less-expensive, strategic platforms
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12. The importance
of legacy data
of executives say their
customer-facing applications
are completely or very reliant
on mainframe processing.
55%
Your traditional systems
– including mainframes, IBM i
servers & data warehouses –
adapt and deliver increasing
value with each new technology
wave
•72%
increase in transaction
volume on mainframe
environments in 2019
$1.65trillion
invested by enterprise IT
to support data warehouse &
analytics workloads over the past
decade
Forrester Consulting, 2019
Wikibon “10-Year Worldwide Enterprise IT Spending 2008-2017”
BMC, 2019
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13. Scaling delivery
Growing data integration initiatives should not
require increased spend or additional expertise.
• Consider data integration frameworks that can
handle large data volumes
• Determine how legacy data will streamed into
cloud environments
• Understand how a hybrid, multi or cloud only
deployment can enable scalability
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14. Considerations
for scaling
Ask yourself these questions when looking to
address how you account for increased data
volumes, sources, and targets.
• Do you have a solution in place for data
integration that can future-proof DI workflows?
• Where is the performance “choke-point” for data
integration today? How will you address it?
• As volumes grow, how will you share only the
changed data with those that need it?
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15. Connect and Snowflake
IBM i
Traditional ETL sources,
files, RDMBS, etc.
Convert mainframe, IBM i and
data from other sources to be
shared anywhere on
Snowflake
BI and Analytics
Tools
Deploy Connect capabilities
on-prem, in cloud or hybrid
environments
Mainframe
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16. Looking at the next 90 days…
• Define your business case
• Ensure you are defining an architecture that will serve you across cloud
environments
• Remember valuable data lives in legacy data sources
• Understand how your team can scale for data integration today and
tomorrow
Join us for more sessions on developing your data integrity strategy!
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17. The Precisely Data Integrity Suite
• Delivers the essential elements of data integrity –
accuracy, consistency, and context
• Built on data integration, data quality, location
intelligence, and data enrichment trusted by over
12,000 enterprise customers
• Modular architecture allows you to choose just the
capabilities your need – and implement them
alongside your current infrastructure at scale
• Empowers faster, confident decision-making
with trusted data
Data
Integration
Data
Enrichment
Location
Intelligence
Data
Quality
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18. Learn more at
precisely.com/data-integrity
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19. Thank you