To stay competitive, you need to swiftly deliver innovative web and mobile apps and analytics solutions that include all your critical data—including mainframe and IBM i. Join us to hear how forward-thinking companies are using modern cloud-based platforms to deliver solutions that drive better customer experiences and greater insight—all while extending the value of their core systems.
IAC 2024 - IA Fast Track to Search Focused AI Solutions
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
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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
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
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
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
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
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
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