The Future of Mainframe Data is in the Cloud

Editor's Notes

• #4: Mainframes are still the backbone for the biggest organizations in the world 71% of the fortune 500 rely on the mainframe for their mission critical transactional systems and they are present in every vertical from FinServ to Insurance to Retail. When talking to these organizations, it’s not unusual to hear that up to 80% of their corporate data originates on the mainframe and that business is growing. The mainframe market is expected to grow to $2.9 billion by 2025. 90% of credit card transactions happen on mainframe systems Worldwide, mainframe systems handle 68% of information technology workloads. This accounts for only 6% of the total IT spending. Hence, usage of mainframe systems is still more cost-effective than other solutions Mainframe computers can withstand bizarre natural calamities like an earthquake of up to 8.0 magnitude A 2020 Deloitte-run survey on companies that use mainframes systems revealed the following interesting data: 74% of the survey-participant businesses consider mainframe systems important for their operations though they actively subscribe to cloud computing services. Businesses mainly consider mainframe systems as strategic technology solutions for long-term survival and operation. 91% of the participants expressed interest in expanding their big iron footprint as a moderate to critical priority in the upcoming 12 months. 72% of businesses who took the survey revealed that they are in conversation with IBM for system upgrades in the next three years.
• #5: Research shows that: 85% of organizations will embrace a cloud-first principle by 2025 55% of leaders site data modernization as the reason for their shift to cloud Approximately $100 billion of wasted migration spend is expected over the next three years So despite the excitement around getting to the cloud, organizations need to be careful…
• #6: Because moving to cloud comes with a heavy cost if not done strategically. McKinsey & Company recently conducted a study that shows that: Over 75% of cloud migration projects are overbudget 37% is spent on systems integrators since they do not have the cloud skills in house to manage these projects 15% on decommissioning costs for other platforms 38% of cloud migration projects run behind schedule Companies are looking to staff 50% of their cloud talent in house so they do not need to rely as heavily on third parties (If you are interested in learning more about this topic, all of this data came from this McKinsey Study https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/cloud-migration-opportunity-business-value-grows-but-missteps-abound)
• #7: Inefficient migrations can be costly. Choosing the right solutions, services and people is critical. Recently, AWS was named a Leader in the 2023 ISG Provider Lens for Mainframe Application Modernization. Why us? At Amazon, we work backwards from customer needs. Our customers told us that migrating mainframe workloads to the cloud was a priority and a challenge. We committed to helping them address this by building strong mainframe-to-cloud capabilities. Customers modernize mainframes with us for 3 main reasons: Agility. Agility is a business’s ability to respond quickly and inexpensively to change. We provide agility for mainframe workloads in many ways, e.g. DevOps, elasticity, APIs, and managed services. Cost reduction. Customers have seen between 60% and 90% cost savings when migrating mainframe workloads to our cloud. We also offer services and mechanisms to track and optimize costs further. Our transparent, cost-aware architecture helps customers control spending and align costs with needs. And Savings can fund further modernization and innovation. Risk mitigation. Customers want to avoid risks like mainframe specialists retiring, slow change due to complexity, siloed platforms, and vendor lock-in. In summary, We deliver agility, cost reduction, and risk mitigation. Let's look at the technical approach to achieving this through AWS mainframe modernization service.
• #8: AWS Mainframe Modernization service is a cloud native platform to modernize, migrate, run, and operate mainframe applications. We accelerate modernization and innovate continually for customers. Our service provides the capabilities to analyze, transform, develop and operate mainframe workloads. It leverages cutting-edge toolchains, AWS and partner expertise, and a dynamic, unified platform. There are four steps in the journey and we carefully chose in each step. The service supports four popular patterns: The automated Refactoring pattern and replatforming pattern are used for application migration. Available as fully managed runtime environments and pay-as-you-go services. The Data replication pattern and file transfer pattern are for data modernization. Available through integrations. Today I am going to introduce The Data Replication pattern. It Replicates mainframe data to AWS in real-time with the integrated Precisely replication unleashing data-based innovations and use-cases, leveraging AWS Mainframe Modernization service and the wide choice of AWS services. It also enables advanced analytics, machine learning, seamless data migration, and new functions and channels. There are three major use cases for the AWS Mainframe Modernization Data Replication pattern with Precisely.
• #9: The first use case augments mainframes with agile AWS analytics. Mainframes hold decades of business and IT operations data. Using our AWS mainframe modernization data replication, customers replicate this data to AWS in near real-time or batch. We offer end-to-end data management: ingestion, processing, storage, analysis, visualization and automation. The replication service copies mainframe relational, hierarchical or legacy file data to AWS data lakes, date warehouse or data stores in near real-time or batch. The mainframe remains the source of record. And the real-time replication keeps data fresh, enabling up-to-date analytics and dashboards.  With our AWS analytics, Customers can create data warehouse and data lakes to combine structured and unstructured data, leverage AI/ML and other advanced technologies, gaining new insights from core mainframe data.
• #10: The second use case leverages our AWS Mainframe Modernization Data Replication with Precisely for large-scale migrations. A mainframe typically comprises multiple interdependent workloads. Each workload may have data stores (e.g. DB2, VSAM) and a transaction system (e.g. CICS). Based on business and IT needs, workloads follow different migration paths - there is no one-size-fits-all approach. For large workloads We Break the mainframe down into individual workloads and start migrating them one by one to AWS. This approach allows to gain experience and momentum rather than attempting a challenging "big bang" migration of the entire mainframe at once. Each workload is migrated separately to AWS according to its business and technical requirements. Since the various workloads on a mainframe often share data, customers can leverage our data replication service to synchronize that data in near real-time between the mainframe and AWS. This ensures that workloads on AWS have the access to the current data, even as some workloads remain on the mainframe.
• #11: The third use case involves building new channels and functions. Data can be replicated bidirectionally between mainframes and AWS. On AWS, customers can innovate quickly to develop new functions like mobile or voice apps using microservices and machine learning. Since mainframe development is typically slow, customers choose AWS to build services rapidly. These new AWS services access real-time mainframe data in AWS data stores - either relational or NoSQL databases. This resembles the first use case but the data is used not for analytics but for new communication channels and functions for end users. The agile AWS functions augment mainframe applications. This avoids increasing expensive mainframe resources by deploying new channels on AWS instead.
• #12: We work closely with partners like Precisely to drive customer success. We have curated Mainframe Competencies with partners who have deep expertise in AWS mainframe modernization and cloud services. Beyond our mainframe modernization service and partners, We also uniquely support mainframe application modernization in several ways: Dedicated mainframe modernization teams for sales, product, marketing across commercial, public sector, and FSI in North America, Europe, LatAm and APJ. No other provider invests so heavily in mainframe modernization skills. The Migration Acceleration Program (MAP) offers guidance, tooling and investment for mainframe modernization. AWS Professional Services help customers adopt and implement the latest mainframe modernization offerings. We are uniquely positioned to support mainframe workload migrations to the cloud through dedicated teams, programs, services and partners with proven expertise. Together with partners like Precisely, we provide a comprehensive solution for mainframe modernization.
• #15: You’ve likely been working on this for a long time, but legacy solutions aren’t serving you today. We have a vision for delivering data with integrity to your business.
• #16: Another key module of the Data Integrity Suite that I’m excited to introduce is Data Observability. This ensures data reliability by monitoring your organization’s data with added context, performing analysis to determine potential adverse data events, and alerting those who need to resolve the issues. For example, if you view a sales pipeline report, you may find that the report doesn’t have new sales opportunities added in recent weeks. This is data freshness. Or there are missing opportunities for a product line that were there last week, which is called data drift. Through a combination of profiling and automated anomaly detection, you can more quickly and accurately surface issues before they get into the hands of your business users. _____ Automated monitoring, alerting & triaging critical data quality issues, based on historical trends e.g. Freshness: changes in data update frequency Volume: changes in number of records added/deleted Data drifts: changes in value ranges, distributions, patterns, completeness, etc. Schema: changes in new/deleted columns Lineage: assess impact of data changes on upstream/downstream pipelines
• #17: The modular, interoperable Precisely Data Integrity Suite contains everything you need to deliver accurate, consistent, contextual data to your business - wherever and whenever it’s needed. Data Integration: Break down data silos by quickly building modern data pipelines that drive innovation Data Observability: Proactively uncover data anomalies and act before they become costly downstream issues Data Governance: Manage data policy and processes with greater insight into your data’s meaning, lineage, and impact Data Quality: Deliver data that’s accurate, consistent, and fit for purpose across operational and analytical systems Geo Addressing: Verify, standardize, cleanse, and geocode addresses to unlock valuable context for more informed decision making Spatial Analytics: Derive and visualize spatial relationships hidden in your data to reveal critical context for better decisions Data Enrichment: Enrich your business data with expertly curated datasets containing thousands of attributes for faster, confident decisions