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Address Your Blind
Spots Around
Mission-Critical Data
Ashwin Ramachandran,
Senior Director, Product Management
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Agenda
• The benefits of modernization
• Modernization challenges
• The complexity of mission-critical assets
• Solutions for Integration success
• Customer story
What are
the benefits
of modern
analytics
platforms?
Enable
competitive
differentiation
Optimize
existing spend
Leverage
market-
available skills
Achieve insight
at any scale
What systems
are mission
critical?
Deeply
entrenched
history in the
business
Run core
business
operations
that directly
impact revenue
High-visibility,
with tight
security
restrictions
in place
Contain rich
data assets
that are
constantly
changing
>50%
2019 Mainframe
transaction growth
$5.3 Billion
2022 Mainframe market
growing at 2.6% CAGR
>15%
2020-2025 CAGR for
cloud database and
DBaaS markets
Market growth trends
How can mission-critical data
contribute to success?
Mission-critical systems that generate data
are not going away
The need to reliably integrate this kind of
data at speed continues to grow
Treasure trove of historical data Decisions can be made
Comprehensive views of business entities Can enable data-driven initiatives like
Customer 360, fraud prevention, and more
The integration challenge remains complex…
0
10
20
30
40
50
60
Real-time
CDC
Skills Governance Data
Accesibility
Budget Data Quality Legacy
%
of
People
Who
Consider
this
a
Top
Challenge
(Rated
1
or
2)
• Real-time Change Data Capture
• Shortage of Skills/Staff
• Data Accessibility for Users Across
the Business
• Budget
• Poor Data Quality
• Difficulty leveraging Mainframe, IBM i,
and other mission-critical systems
• Scalability
Q. What are your top challenges when it comes to
implementing a modern data architecture?
…and some challenges
are nebulous!
Cultural barriers within an organization cannot
be ignored!
• Onboarding data from SaaS-based assets is
straightforward
• Mainframe teams and IBM i teams are
typically siloed from data teams implementing
new applications and analytics on the cloud
• Strict limitations exist around installing
products, opening ports, and accessing data
• Need to implement a solution that clears all
these cultural hurdles while still delivering a
technically viable solution
Steps to successfully
leverage mission-
critical data
1. Start by eliminating a silo
2. Escape the delay of batching
3. Identify opportunities to scale up
4. Explore modernizing existing applications
Eliminate the silos!
• Choose standard connectivity protocols
• JDBC
• SFTP
• FTP(S) or Connect: Direct for
mainframe platforms
• Leverage existing metadata to minimize
error and maximize reusability
• Copybooks, table DDLs, schema registries
• Ensure encryption of data in flight at every
point in the journey
• Be flexible to a combination of data delivery
styles and topologies:
• One-way
• Broadcast
• Consolidation
Replicate data at the speed of business
• Use log-based change data capture
techniques to identify transactions
as they occur
• Minimize impact to online transactional
backends
• Example mechanisms include:
• IFCID 306
• CICS
• IBM i journals
• Oracle redo and archive logs
• Ensure that data availability is treated with
the same urgency as infrastructure or
application availability
• Perform data translation and conversion
off-platform wherever possible
• zIIP offloading for complex mainframe
operations
• MIPS reduction to minimize impact to
platform
Scale it up and drive new use cases!
Warehousing
modernization, BI,
and analytics
Real-time applications
and to facilitate lambda
architectures
Rehosting and
modernizing on-premises
deployments
Modernize more than just data
1
Use replication to
connect live applications
on mission-critical
platforms to new
applications on the cloud
2
Opens up opportunities
for migrating existing
applications that run on
the mission-critical
platform
3
Mainframe applications
can be migrated to cloud
platforms without
redevelopment by leveraging
a rehosting solution or a
tool like Connect
“We soon
determined that
Connect was doing
exactly what it said
on the box. It was
working as
designed...[it] opens
up the whole
database to us. If
we add any new
tables, then it’s easy
to add them to the
extraction process
and replications.”
Quintin McKenzie
Head of Corporate Core
Sky realizes real-time,
error-free data replication
OBJECTIVE
• In digital transformation mode
• Consolidating toolsets across the enterprise
and moving functions like analytics to Snowflake
on AWS
• Struggled to populate Snowflake with their
mission-critical data
CHALLENGES
• Business-critical systems such as billing,
subscriber management, and chart of accounts
all run on IBM i, each with its own file structure
• Existing bespoke ETL processes were inflexible,
slow to run (4-10 hours), and prone to network
issues
• Needed a new process to automatically feed
Snowflake with this critical data without
impacting IT
SOLUTION
• Precisely Connect
• Snowflake on AWS
BENEFIT
• Sales now has a view into subscriber behavior
that is current
• IT focused on making higher-value contribution
to the business instead of rebuilding pipelines
that are prone to break upon change
• Innovation is occurring on Snowflake in the form
of new applications, while the mission-critical,
reliable, IBM i backends run unchanged
Key Takeaways
Mission-critical data
helps fulfill the
promises of digital
modernization
There are numerous
challenges both
technical and
organizational
Get early wins,
moving to real-time
data delivery
where possible
Use your
unlocked data
to intelligently
modernize your
applications
1 2 3 4
Questions?
Address Your Blind Spots Around Mission-Critical Data

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Address Your Blind Spots Around Mission-Critical Data

  • 1. Address Your Blind Spots Around Mission-Critical Data Ashwin Ramachandran, Senior Director, Product Management
  • 2. Housekeeping Webinar Audio • Today’s webcast audio is streamed through your computer speakers • If you need technical assistance with the web interface or audio, please reach out to us using the Q&A box Questions Welcome • Submit your questions at any time during the presentation using the Q&A box. If we don't get to your question, we will follow-up via email Recording and slides • This webinar is being recorded. You will receive an email following the webinar with a link to the recording and slides
  • 3. Agenda • The benefits of modernization • Modernization challenges • The complexity of mission-critical assets • Solutions for Integration success • Customer story
  • 4. What are the benefits of modern analytics platforms? Enable competitive differentiation Optimize existing spend Leverage market- available skills Achieve insight at any scale
  • 5. What systems are mission critical? Deeply entrenched history in the business Run core business operations that directly impact revenue High-visibility, with tight security restrictions in place Contain rich data assets that are constantly changing
  • 6. >50% 2019 Mainframe transaction growth $5.3 Billion 2022 Mainframe market growing at 2.6% CAGR >15% 2020-2025 CAGR for cloud database and DBaaS markets Market growth trends
  • 7. How can mission-critical data contribute to success? Mission-critical systems that generate data are not going away The need to reliably integrate this kind of data at speed continues to grow Treasure trove of historical data Decisions can be made Comprehensive views of business entities Can enable data-driven initiatives like Customer 360, fraud prevention, and more
  • 8. The integration challenge remains complex… 0 10 20 30 40 50 60 Real-time CDC Skills Governance Data Accesibility Budget Data Quality Legacy % of People Who Consider this a Top Challenge (Rated 1 or 2) • Real-time Change Data Capture • Shortage of Skills/Staff • Data Accessibility for Users Across the Business • Budget • Poor Data Quality • Difficulty leveraging Mainframe, IBM i, and other mission-critical systems • Scalability Q. What are your top challenges when it comes to implementing a modern data architecture?
  • 9. …and some challenges are nebulous! Cultural barriers within an organization cannot be ignored! • Onboarding data from SaaS-based assets is straightforward • Mainframe teams and IBM i teams are typically siloed from data teams implementing new applications and analytics on the cloud • Strict limitations exist around installing products, opening ports, and accessing data • Need to implement a solution that clears all these cultural hurdles while still delivering a technically viable solution
  • 10. Steps to successfully leverage mission- critical data 1. Start by eliminating a silo 2. Escape the delay of batching 3. Identify opportunities to scale up 4. Explore modernizing existing applications
  • 11. Eliminate the silos! • Choose standard connectivity protocols • JDBC • SFTP • FTP(S) or Connect: Direct for mainframe platforms • Leverage existing metadata to minimize error and maximize reusability • Copybooks, table DDLs, schema registries • Ensure encryption of data in flight at every point in the journey • Be flexible to a combination of data delivery styles and topologies: • One-way • Broadcast • Consolidation
  • 12. Replicate data at the speed of business • Use log-based change data capture techniques to identify transactions as they occur • Minimize impact to online transactional backends • Example mechanisms include: • IFCID 306 • CICS • IBM i journals • Oracle redo and archive logs • Ensure that data availability is treated with the same urgency as infrastructure or application availability • Perform data translation and conversion off-platform wherever possible • zIIP offloading for complex mainframe operations • MIPS reduction to minimize impact to platform
  • 13. Scale it up and drive new use cases! Warehousing modernization, BI, and analytics Real-time applications and to facilitate lambda architectures Rehosting and modernizing on-premises deployments
  • 14. Modernize more than just data 1 Use replication to connect live applications on mission-critical platforms to new applications on the cloud 2 Opens up opportunities for migrating existing applications that run on the mission-critical platform 3 Mainframe applications can be migrated to cloud platforms without redevelopment by leveraging a rehosting solution or a tool like Connect
  • 15. “We soon determined that Connect was doing exactly what it said on the box. It was working as designed...[it] opens up the whole database to us. If we add any new tables, then it’s easy to add them to the extraction process and replications.” Quintin McKenzie Head of Corporate Core Sky realizes real-time, error-free data replication OBJECTIVE • In digital transformation mode • Consolidating toolsets across the enterprise and moving functions like analytics to Snowflake on AWS • Struggled to populate Snowflake with their mission-critical data CHALLENGES • Business-critical systems such as billing, subscriber management, and chart of accounts all run on IBM i, each with its own file structure • Existing bespoke ETL processes were inflexible, slow to run (4-10 hours), and prone to network issues • Needed a new process to automatically feed Snowflake with this critical data without impacting IT SOLUTION • Precisely Connect • Snowflake on AWS BENEFIT • Sales now has a view into subscriber behavior that is current • IT focused on making higher-value contribution to the business instead of rebuilding pipelines that are prone to break upon change • Innovation is occurring on Snowflake in the form of new applications, while the mission-critical, reliable, IBM i backends run unchanged
  • 16. Key Takeaways Mission-critical data helps fulfill the promises of digital modernization There are numerous challenges both technical and organizational Get early wins, moving to real-time data delivery where possible Use your unlocked data to intelligently modernize your applications 1 2 3 4

Editor's Notes

  1. Visibility into all data – it provides views that make data look simpler and more unified than it actually is in today's complex, multiplatform data environments Sets course for real-time pipelines - the modern hub, it regularly instantiates data sets quickly on the fly. It may also handle terabyte-scale bulk data movement. Either, way a modern data hub requires modern pipelining for speed, scale, and on-demand processing. Limits skills gas - The IT world is full of old-fashioned data hubs that are homegrown or consultant-built. Support advanced forms of orchestration, pipelining, governance, and semantics, all integrated in a unified tools Removes data silos - Again, this is accomplished without consolidating silos. Think of the data views, semantic layers, orchestration, and data pipelines just discussed. All these create threads that weave together into a data fabric, which is a logical data architecture for all enterprise data that can impose functional structure over hybrid chaos
  2. https://www.marketgrowthreports.com/global-mainframe-market-20249408 https://www.marketsandmarkets.com/Market-Reports/cloud-database-as-a-service-dbaas-market-1112.html
  3. 451 Research: 37% of orgs have >100 data silos Talk Track Real-time CDC: Keeping data within analytics platforms in sync with ongoing business operations Skills/Staff: Shortage of skills and staff who have an understanding across cloud and mission-critical technologies Data Accessibility: Making data accessible to users across the business Budget: Spending on maintenance and not innovation Data Quality: Poor data quality and lack of trust in data Mission-critical systems: Difficult to leverage data from mission-critical systems (mainframes, EDWs, IBM i etc.) with modern cloud platforms Scalability: Ability to scale with and process massive data volumes
  4. Break down legacy data silos – removing the barriers that come with accessing and integrating data from legacy data stores, mainframe, IBM i and more Rethink – sometimes you might be already doing something with legacy data, you have the access but the needs of the organization may be changing causing you to think about how you might implement a new solution in line with or to replace existing Real-time, data is only as good as how quickly it is delivered, to do this you need to have a way to build real-time delivery of changes in legacy systems to One of the biggest hinderances to unified analytics hubs can be the lack of skills or costs associated with accessing legacy data
  5. Successful data delivery drives increased demand Identify different endpoints that satisfy new use cases. Common examples include: Snowflake for warehousing modernization, BI, and analytics Kafka for real-time applications and to facilitate lambda architectures DBaaS like Azure SQL Database for rehosting and modernizing on-premises deployments
  6. Use replication to connect live applications on mission-critical platforms to new applications on the cloud Opens up opportunities for migrating existing applications that run on the mission-critical platform; benefits include: Decreased spend on mainframe or IBM i Preserve performance of those online backends Leverage more readily available skills to build and maintain the newly modernized applications Ensure applications that don’t migrate can run at maximum performance Mainframe applications can be migrated to cloud platforms without redevelopment by leveraging a rehosting solution or a tool like Connect