Unlock the Power of Mainframe Data for Democratized Cloud Analytics
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The rapid rate at which new technologies for cloud-based computing are being developed and deployed – like big data analytics, the Internet of Things, machine learning and artificial intelligence - is truly astonishing. Looking further out into the future, the accelerating scale and speed of such advancements is compelling organizations to migrate the majority of their data and infrastructure to the cloud. Even the Mainframe. It’s not that mainframe computing systems are going away any time soon. In fact, mainframe platforms are just as essential today for running global, enterprise-scale businesses as they have ever been. And despite years of pundits advocating otherwise, actually migrating off mainframe and onto cloud platforms has very rarely proven to be worth the cost and business risk involved. But for today’s IT leaders tasked with moving enterprise IT operations forward to that integrated, future-proof, and democratized future, there is no avoiding making these changes while still running an efficient business. Organizations are turning to Precisely to help them maintain current operations while bringing their mainframe data to the cloud. During this session we discuss: Why it's imperative for IT leaders to unlock access to mainframe data in a cloud-first worldThe challenges and complexities of democratizing data with mainframe systemsHow to successfully integrate mainframe data into cloud-based environments
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Unlock the Power of Mainframe Data for Democratized Cloud Analytics
- 1. Unlock the Power of Mainframe Data for Democratized Cloud Analytics John de Saint Phalle | Senior Product Manager
- 2. Explosion of cloud computing
- 3. Business trends influencing how companies are moving to the cloud Financial responsibility for IT expenditures Data-centric cloud computing Democratized data
- 4. “85% of organizations will embrace a cloud- first principle by 2025” - Gartner “55% of leaders site data modernization as the reason for their shift to cloud” - Deloitte “Approximately $100 billion of wasted migration spend is expected over the next three years” - McKinsey & Company
- 5. Migration comes at a heavy cost • Over 75% of cloud migration projects are overbudget • 37% is spent on systems integrators since they do not have the cloud skills in house • 15% on decommissioning costs • 38% of cloud migration projects run behind schedule • Companies are looking to staff 50% of cloud talent in house
- 7. IT is challenged with moving mainframe data to the cloud IT execs and managers need to ensure security, quality, and governance of all data Huge gap between distributed, democratized, “cloud-native everything” and data coming from the mainframe 7 Mainframes are a legacy technology, why should we care? =
- 8. Confidential: Prepared for Precisely Customers and Prospects Mainframes host the most critical applications 71% Fortune 500 2.5 Billion Transactions / day / per MF $2.9 Billion Mainframe market by 2025 Top World Banks 92 of World’s Top Insurers 10 of Top 25 US Retailers 23
- 9. Modern platforms lack native mainframe integration Distributed and Cloud environments Mainframe
- 10. Democratizing mainframe data for cloud analytics IT need standards for infrastructure while it remains seamless to employees Data Integrity is needed Users demand flexibility
- 11. Exploding need for trusted data 83% of CEOs want their organization to be more data-driven Digital transformation investments to top $6.8 trillion globally by 2023 68% of Fortune 1000 businesses now have CDOs – up 6x in the last decade Global data infrastructure spending expected to reach $200 billion this year Data is the fuel for decision-making today IDC IDC Gartner Forbes
- 12. of data practitioners completely trust their data of data practitioners strongly believe their actions are driven by data analysis 30% 27% But your team doesn’t trust your data Can’t get it fast enough Don’t understand it Can’t trust it Don’t have the context to use it Don’t know when it’s going to break Source: IDC
- 13. For trusted data, you need data integrity Data integrity is data with maximum accuracy, consistency, and context for confident business decision-making Data Integrity
- 14. Democratized data impacts data integrity Data Diversity Moving and sharing data Metadata and lineage
- 15. Building an environment that can adapt 1 Data sharing models 2 Business process changes 3 Cloud environment interoperability 4 Deep IT management integration
- 17. Precisely’s single solution for your mainframe integration needs
- 18. Cloud / VPC / On-Premises Data Integration Data Observability Data Quality Geo Addressing Spatial Analytics Data Governance Data Enrichment APIs and SDKs Enterprise Business Systems • Enterprise apps • Analytics tools • Precisely industry apps • BI dashboards • AI/ML Enterprise Data Sources • Business Intelligence • CRM • Workforce mgmt. • Data warehouse • ERP • Billing Data Integrity services Data Integrity Foundation Data catalog Intelligence Agents
- 19. How the Data Integrity Suite does it better Modular Interoperable Easy Intelligent Dynamic
- 20. Data Integration Break down data silos by quickly building modern data pipelines that drive innovation
- 21. Data Integration differentiators Real-time data streaming gives you fast access to fresh data when and where you need it Business-friendly user interface allows first-time users to create data pipelines without coding Build once, deploy anywhere principle allows you to build data pipelines in the Precisely Cloud and deploy them wherever your data lives 50+ years of domain expertise in mainframe and IBM i systems is built into the Data Integration module to handle your complex data sources Integration with the Data Integrity Suite Foundational enables Data Integration to share metadata with other modules - exponentially building value and spurring innovation
- 22. • Provides a single, searchable inventory of your organization’s data assets • Allows technical users to easily search, explore, understand, and collaborate on critical data assets • Enables data stewards to monitor, audit, certify, and track data across its lifecycle through integrated data governance • Allows for visualization of data relationships, data lineage, and data’s business impact • Supports the sharing of knowledge, comments, and surveys Data Catalog A data catalog is core to managing data integrity
- 23. • Catalogs business and technical metadata from 100s of data sources – even the most complex sources like IMS and VSAM • Provides easy searchability and visibility into business and technical metadata • Automatically captures metadata through any Data Integrity Suite connection – for data replication, data governance, data quality, and more – and makes it available for the creation of inferences and recommendations such as: • Detection of PII prior to replicating it to another system • Data observability recommendations for replicated data • Data quality rule recommendations for observed data • Data enrichment recommendations and geospatial context for data quality pipelines • Recommendations for governing ownership of spatial data The data integrity suite’s data catalog is unique Data Catalog
- 24. Questions?
- 25. Thank you
Editor's Notes
- 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…
- 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)
- 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.
- Significant differences which impede the integration of mainframe data into cloud environments include: Data compression and formats Data structure definitions Mainframe operations
- As businesses make the transition to cloud, the need for trusted data to make good business decisions is more vital than ever. Key business initiatives in 2022 revolve around transforming customer experiences, applying AI to proven business cases to derive new insights and increase efficiency, leveraging the power of location to solve new problems (optional light call out to covid-19 problems being solved), and ensuring that the business is secure and compliant. Each of these initiatives is heavily dependent upon integrated, clean, accurate, contextualized, enriched data in order to deliver the maximum benefit to the organization. (Transition to next slide showing stats on data challenges.)
- Unfortunately, those within the organization closest to the data can’t trust it for various reasons. For instance, they… “Can’t get it fast enough” Trapped in complex, legacy systems Not available when & where it’s needed Not as fresh as business demands “Don’t understand it” Don’t understand the lineage of data Don’t know how it’s used by the business Don’t have accountability around data changes “Can’t trust it” Full of errors Non-standardized “Don’t have the context to use it” Lacking the 3rd party data and location context needed for decision-making “Don’t know when it’s going to break” Downtime comes as a surprise Anomalies unexpectedly impact the business downstream The true root cause of data problems is unidentified Fortunately, you are not alone. Every day, we talk to companies like yours who are struggling to meet their data-driven business goals - because their data is a mess. Source for stats on left side IDC Spotlight: Improving Data Integrity and Trust Through Transparency and Enrichment Written by: Stewart Bond, Research Director, Data Integration and Intelligence Software
- To achieve your goals, you need what we call data integrity. Data integrity is data with maximum accuracy, consistency, completeness, and context for confident business decision-making.
- Mega-trends, such as the rise of AI-powered cloud technologies and the expanding ranks of executives and managers demanding full and unhindered ‘democratized’ access to data, always force big changes to business systems, at many different levels. Yet if your IT architecture includes mainframe systems, you will always have to stand with one foot in the present and the other in the future. Choosing solutions that are designed from the start to enable you to easily deal with new realities will ensure you can keep your balance, even as the future becomes ever more fluid and slippery.
- 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
- There are a handful of principles that we have held to throughout the development of the Suite, and it’s these principles that differentiate it from other solutions in the market. Modular - Unlike many products in the market, the Data Integrity Suite can be consumed in a modular fashion. Best-of-breed capabilities that deliver great value alone and incremental functionality and value by working together. Interoperable - It works seamlessly with other modules of the Suite, other products in the Precisely portfolio, and existing technology ecosystems. It is designed to work with traditional and modern technology stacks to maximize return on investment and drive innovation. Easy - The Data Integrity Suite is designed for business and data teams. The Suite’s modules share a common, business-friendly user interface and no-code experience. The Data Integrity Suite can democratize your data and enable users to serve themselves. Intelligent - Machine learning intelligence automates and streamlines data integrity processes, leveraged to understand data, identify data anomalies, suggest data quality rules, and more. Dynamic - And organizations that need more agility in their decision-making and operational processes can access data at the speed of their business. Replication delivers data wherever needed in near-real time; dynamic datasets update as new information becomes available, and potential data issues are proactively flagged before they become costly problems.
- The first module is Data Integration. Typically, in any major data initiative, you first need to connect to sources, and sometimes move or replicate data to another environment. With Data Integration, you can easily create streaming data pipelines that integrate data from core environments such as relational, and of course, mainframe and IBM I, with modern cloud-based data platforms like Snowflake to drive analytics and innovation and extend the value of your mission-critical systems. We understand that pipelines must scale for your needs today and extend for tomorrow.