Data lineage is the comprehensive tracking of data usage within an organization. This includes how data originates, how it is transformed, how it is calculated, its movement between different systems, and ultimately how it is utilized in applications, reporting, analysis, and decision-making.
With the increasing complexities of business technology, data lineage analysis has become essential for most organizations. This article provides an overview of the fundamentals, importance, uses, and challenges of data lineage.
The Fundamentals of Data Lineage
Data lineage facilitates improved data transparency, quality, and consistency by enabling organizations to track and understand the complete lifecycle of their data assets. It helps with decision-making when sourcing and using data. It also helps with transforming data, especially for larger organizations with mission-critical applications and intricate data landscapes.
There are several factors to consider with data lineage:
- Origin: Where did the data originate? The origin might be an application, a database, or a spreadsheet. It could come from another part of the organization or a third-party source.
- Flow: How has the data moved across different databases, files, APIs, and internal and external business systems over time?
- Transformation: Data typically undergoes multiple changes over time due to changes in representation, cleansing, merging with other data, or when the data is generated by or used in a calculation. The changes can also come from data conversions, including ELT (extract, load, transform), ETL, and Reverse ETL processes.
- Destination: Where is the data now? Does it reside in an application database or data warehouse. Is it used in a report or an analysis? Has it been sent outside the organization? It may be stored in multiple places.
The Importance of Data Lineage
Data lineage plays a key role in keeping data valuable and effective in a business setting. Here are a few ways that data lineage can deliver benefits to an organization.
Transparency and Trust
Data has incredible value in an information age. To realize the full value, data must be accurate and accessible. In other words, it becomes trustworthy only when it can be understood by anyone using it, and when the processing steps keep the data accurate. Data lineage provides transparency into the flow of data. It increases understanding and makes it easier for non-technical users to capture insights from existing datasets, especially for aggregated or calculated data.
Compliance and Regulatory Requirements
Data management regulations are becoming more stringent each year. Regulatory standards are tightening, and effective data management is becoming increasingly important. Data lineage can help organizations comply with GDPR, CCPA, and other data privacy laws. The transparency of data lineage makes data access, audits, and overall accountability easier. Accurate data lineage is crucial for demonstrating compliance with regulatory requirements, thereby mitigating the risk of project delays, fines, and other penalties.
Data Governance
Data lineage enables stronger data governance by providing the data to monitor, manage and ensure compliance to issued standards and guidelines. Because data lineage offers traceability of origin, flow, transformation and destination, it allows businesses to improve data quality, reduce inconsistencies and errors, and strengthen data management practices.
Improved Data Quality
Data lineage allows companies to trace the path of data from its current form back to its source. Data lineage offers a transparent record, facilitating the understanding and management of data variability and quality throughout its journey, and ensuring reliable data for decision-making. This is particularly relevant for companies modernizing existing systems.
Facilitating Collaboration Across Teams
With data lineage, trust in data accuracy and accessibility, improved data quality, and stronger ability to govern data all triangulate for better collaboration across teams. Data lineage avoids data siloing and facilitates interdepartmental activity. When data engineers and analysts utilize the same set of data, it fosters cross-functional teamwork and minimizes errors due to bad or in consistent data. Data lineage encourages a sense of unification as team members across an organization work from the same, trusted data.
Real-world Applications of Data Lineage
There are multiple ways that data lineage can add business value to organizations.
Use Case 1: Data Migrations
Zengines has invested in data lineage capabilities to support end-to-end migration of data from existing source systems to new target business systems. Data lineage is often the first research step required to ensure an efficient and accurate data migration.
Use Case 2: Improving Data Analytics
Data lineage exposes data quality issues by providing a clear view of the data journey, highlighting areas where inconsistencies or errors may have occurred. This makes it easier to engage in effective, detailed data analytics.
Consider, for instance, a financial services company with decades-old COBOL programs. Data lineage provides insights for organizations trying to replicate reporting or other outputs from these aging programs.
Use Case 3: Troubleshooting and Root Cause Analysis
Data lineage makes it easier to identify and trace errors back to their source. Finding the root cause of an error quickly is extremely valuable in a world where time is at a premium.
Use Case 4: Enhancing Data Security and Privacy
An important aspect of data security and privacy compliance is keeping data safe guarded at all times. Data lineage provides an understanding of the data life cycle that can show information security groups the steps that must be reviewed and secured.
Comprehensive data lineage makes it easier to demonstrate compliance with data privacy regulations. For example, Banks and Payments Processors are subject to GLBA (Gramm-Leach-Bliley Act), PCI DSS(Payment Card Initiative - Data Security Standards), EU GDPR (European General Data Protection Regulation), and many other regulations that protect Personally Identifiable Information (PII). The knowledge of how any data element is used allows it to be protected, masked, or hidden when appropriate.
Use Case 5: Implementing Data Mesh and Data Fabric
Data Mesh and Data Fabric are advanced data architectures that help to decentralize data and integrate it across diverse data sources. Understanding the data lineage allows data management teams to make trustworthy data available to Data Mesh / Data Fabric consumers. Data lineage makes it possible to determine the correct data to store and use for a given purpose (decision making, analytics, reporting, etc.). Data lineage is typically part of any new Data Mesh / Data Fabric initiative.
Challenges in Data Lineage
Data lineage is useful but can also face challenges. Here are a few potential issues.
Data Complexity and Fragmentation
Siloed data continues to be a major hurdle for tracing business data across departments and organizations. Consider when a security trade is being made. The security details are usually maintained in a reference data / Master Data Management application. The bid / ask information comes from many different market vendors and is updated continuously. The trading application computes the value of the trade, and any tax impact is computed in an investment accounting application. Is the same data being used across them all? Do they use different terminology? Do the applications all use the same pricing information? For accurate reporting and good decision making, it is vital that the same data is used in every step.
Mapping data lineage in increasingly complex environments is also a concern. Things like on-site and cloud storage, as well as remote, hybrid, and in-person work environments, make data complexity and fragmentation a growing issue that requires attention.
Resource Intensive
Historically, capturing and maintaining data lineage has been resource-intensive work performed by analysts with a deep understanding of the business. Given the quantity of data and code involved, a manual approach is prohibitively expensive for most companies. Most software solutions provide a partial view, only showing data stored in relational databases or excluding logic found in computer programs.
The best option is to find a balance between manual and automated solutions that enable cost-effective data lineage frameworks.
Evolving Data Systems
Data lineage is more than a backward-looking activity. Organizations also need to maintain up-to-date lineage information as systems are changed and replaced over time. In an era of constant change, data lineage teams are challenged to incorporate new forms of data usage or data transformation.
Investing in Data Lineage
Data lineage is becoming a critical part of any company’s data management strategy. In an information age where data and analytics are king, data lineage enables companies to maintain clean, transparent, traceable datasets. This empowers data-driven decision-making and encourages cross-collaborative efforts.
Data lineage addresses a central part of business operations. It provides a powerful sense of digital clarity as organizations navigate increasingly complex tools, systems, and regulatory landscapes.
Forward-thinking technical and non-technical leaders alike should be encouraging their organizations to improve their data lineage strategies. Investments in data lineage result in a valuable new data assets that provide greater business agility and competitive advantage.
Unlock the Power of Seamless Data Lineage with Zengines
Data lineage isn’t just a nice-to-have—it’s essential for modern businesses navigating system changes, compliance pressures, and complex tech stacks. Whether you're migrating from legacy systems, improving analytics, or strengthening data governance, data lineage empowers teams to move faster, reduce risk, and make better decisions.
At Zengines, we’ve built our data lineage capabilities to do more than just document data flow. Our lineage engine integrates deeply with legacy codebases, like mainframe COBOL modules, and modern environments alike—giving you full visibility into how data is transformed, used, and governed across your systems. With AI-powered analysis, automation, and an intuitive interface, Zengines transforms lineage from a bottleneck into a business advantage.
Ready to see what intelligent data lineage can do for your organization?
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Three Keys to Successful Mainframe Refactoring: A Practical Guide
With 96% of companies moving mainframe workloads to the cloud, yet 74% of modernization projects failing, organizations need a systematic approach to refactoring legacy systems. The difference between success and failure lies in addressing three critical challenges: dependency visibility, testing optimization, and knowledge democratization.
The Hidden Challenge
Mainframe systems built over decades contain intricate webs of dependencies that resist modernization, but the complexity runs deeper than most organizations realize. Unlike modern applications designed with clear interfaces, documentation standards and plentiful knowledge resources, legacy systems embed business logic within data relationships, file structures, and program interactions that create three critical failure points during mainframe refactoring:
Hidden Dependencies: Runtime data flows and dynamic relationships that static analysis cannot reveal, buried in millions of lines of code across interconnected systems.
Invisible Testing Gaps: Traditional validation approaches fail to catch the complex data transformations and business logic embedded in mainframe applications, leaving critical edge cases undiscovered until production.
Institutional Knowledge Scarcity: The deep understanding needed to navigate these invisible complexities exists only in the minds of departing veterans.
Any one of these challenges can derail a refactoring project. Combined, they create a perfect storm that explains why 74% of modernization efforts fail. Success requires ensuring this critical information is available throughout the refactoring effort, not left to chance or discovery during code transformation.
Key 1: Master Data Dependencies Before Code Conversion
The Problem: Runtime data flows and dynamic dependencies create invisible relationships that static analysis cannot reveal.
The Problem: Complex data flows and dynamic dependencies create invisible relationships that span program execution flows, database navigation patterns, and runtime behaviors.
Implementation Checklist
□ Trace Data Element Journeys Across All Systems
- Identify program actions that reads, modifies, or depends on specific data structures
- Map cross-application data sharing through job control language (JCL) and program execution sequences
□ Understand Database and Program Execution Patterns
- Analyze JCL/CL job flows to understand program dependencies and execution order
- Map hierarchical (IMS) and network (IDMS) database structures and navigation paths
- Identify data-driven business logic that changes based on content and processing context
□ Access Hidden Business Rules
- Identify validation logic embedded in program execution sequences
- Discover error handling routines that function as business rules
- Uncover edge cases handled through decades of modifications
□ Generate Impact Analysis
- Visualize effects of modifying specific programs or data structures
- Understand downstream impacts from changing data formats or program execution flows
- Access comprehensive decomposition analysis for monolithic applications
What It Looks Like in Real Life
Manual Approach: Teams spend months interviewing SMEs, reading through millions of lines of undocumented code, and creating spreadsheets to track data flows and job dependencies. The scale and complexity make it impossible to find all relationships—critical dependencies exist in JCL execution sequences, database navigation patterns, and runtime behaviors that are buried in decades of modifications. Even after extensive documentation efforts, teams miss interconnected dependencies that cause production failures.
With Zengines: Complete data lineage mapping across all systems in days. Interactive visualization shows exactly how customer data flows from the 1985 COBOL program through job control sequences, database structures, and multiple processing steps, including execution patterns and database behaviors that documentation never captured.
Success Metrics
- Complete visibility into data flows, program dependencies, and execution patterns
- Real-time access to comprehensive refactoring complexity analysis
- Zero surprises during code conversion phase
Key 2: Implement Data Lineage-Driven Testing
The Problem: Traditional testing approaches fail to validate the complex data transformations and business logic embedded in mainframe applications. While comprehensive testing includes performance, security, and integration aspects, the critical foundation is ensuring data accuracy and transformation correctness.
Implementation Checklist
□ Establish Validation Points at Every Data Transformation
- Identify test checkpoints at each step where data changes hands between programs
- Monitor intermediate calculations and business rule applications
- Track data transformation throughout the process
□ Generate Comprehensive Data-Driven Test Scenarios
- Create test cases covering all conditional logic branches based on data content
- Build transaction sequences that replicate actual data flow patterns
- Include edge cases and error conditions that exercise unusual data processing paths
□ Enable Data-Focused Shadow Testing
- Process test data through refactored systems alongside legacy systems
- Compare data transformation results at every lineage checkpoint
- Monitor data accuracy and consistency during parallel data processing
□ Validate Data Integrity at Scale
- Test with comprehensive datasets to identify data accuracy issues
- Monitor for cumulative calculation errors in long-running data processes
- Verify data transformations produce identical results to legacy systems
What It Looks Like in Real Life
Manual Approach: Testing teams manually create hundreds of test cases, then spend weeks comparing data outputs from old and new systems. The sheer volume of data transformation points makes comprehensive coverage impractical—when data discrepancies appear across thousands of calculation steps, teams have no way to trace where in the complex multi-program data flow the difference occurred. Manual comparison of data transformations across interconnected legacy systems becomes impossible at scale.
With Zengines: Enable test generation automation to create thousands of data scenarios based on actual processing patterns. Self-service validation at every data transformation checkpoint to pinpoint exactly where refactored logic produces different data results—down to the specific calculation or business rule application.
Success Metrics
- Test coverage across all critical data transformation points
- Validation of data accuracy and business logic correctness
- Confidence in refactored data processing before cutover
Key 3: Democratize Institutional Knowledge
The Problem: Critical system knowledge exists only in the minds of retiring experts, creating bottlenecks that severely delay modernization projects.
Implementation Checklist
□ Access Comprehensive Data Relationship Mapping
- Obtain complete visualization of how data flows between systems and programs
- Understand business logic and transformation rules embedded in legacy code
- Enable team members to explore system dependencies without expert consultation
□ Extract Business Context from Legacy Systems
- Capture business rules and validation requirements from existing code
- Link technical implementations to business processes and requirements
- Create accessible knowledge bases with complete rule extraction
□ Enable Independent Impact Analysis
- Provide capabilities to show downstream effects of proposed changes
- Allow developers to trace data origins and dependencies during refactoring
- Support business analysts in validating modernized logic
□ Eliminate SME Consultation Bottlenecks
- Provide role-based access to comprehensive system analysis
- Enable real-time exploration of data flows and business rules
- Deliver complete context for development and testing teams
What It Looks Like in Real Life
Manual Approach: Junior developers submit tickets asking "What happens if I change this customer validation routine?" and wait 2 weeks for Frank to review the code and explain the downstream impacts. The interconnected nature of decades-old systems makes it impractical to document all relationships—Frank might remember 47 downstream systems, but miss the obscure batch job that runs monthly. The breadth of institutional knowledge across millions of lines of code is impossible to capture manually, creating constant bottlenecks as project velocity crawls.
With Zengines: Any team member clicks on the validation routine and instantly sees its complete impact map—every consuming program, all data flows, and business rules. Questions get answered in seconds instead of weeks, keeping modernization projects on track.
Success Metrics
- 80% reduction in SME consultation requests
- Independent access to system knowledge for all team members
- Accelerated decision-making without knowledge transfer delays
Technology Enablers
Modern platforms like Zengines - Accelerate & De-Risk Your Data Projects automate much of the dependency mapping, testing framework creation, and knowledge extraction.
Take Action
Successful mainframe refactoring demands more than code conversion expertise. Organizations that master data dependencies, implement lineage-driven testing, and democratize institutional knowledge create sustainable competitive advantages in their modernization efforts. The key is addressing these challenges systematically before beginning code transformation, not discovering them during production deployment.
Next Steps: Assess your current capabilities in each area and prioritize investments based on your specific modernization timeline and business requirements.
Executive Leadership Guide: Mainframe Data Lineage as Strategic Risk Management
Your mainframe processes billions in transactions daily, but three critical risks could blindside your business tomorrow. Whether you're steering operations as CEO or providing oversight as a board member, mainframe data lineage isn't just technical infrastructure—it's your shield against reputational and financial catastrophe.
For the CEO: Reputation, Revenue and Risk
As a CEO running a business on mainframe core, your competitive advantage may be sitting on a ticking time bomb. Here are the three critical questions every CEO must ask their CTO:
1. Skills Crisis Reality Check
"How many of our mainframe experts are within 5 years of retirement?" If that number is above 40%, you're in the danger zone. The knowledge walking out your door isn't replaceable with a job posting. Comprehensive mainframe data lineage documentation is not optional – it must capture not just what the code does, but how data flows through your critical business processes.
2. Regulatory Exposure Assessment
"Can we trace every customer data point from source to report within 24 hours?" If the answer is anything but "yes," your reputation is at risk. Regulators don't care about mainframe complexity, they care about data accuracy and auditability. Mainframe data lineage isn't optional, it's your insurance policy against million-dollar compliance failures.
3. Revenue Impact Visibility
"When mainframe data feeds our analytics wrong, how quickly do we know?" If your team can't answer this, you're making business decisions on potentially corrupted data. Mainframe data lineage answers questions about data sources, data flows and run-time considerations – which inform system changes before they impact customer experience or financial reporting.
For the Board Member: Governance and Fiduciary Responsibility
Board members face unique oversight challenges when it comes to mainframe risk. Your fiduciary duty extends to technology risks that could devastate shareholder value overnight. Here are the three governance priorities for your executive team:
1. Data Lineage Audit Readiness
Quarterly "Data Integrity Dashboard" reporting is non-negotiable, showing complete mainframe data lineage coverage. Your executive team must demonstrate: Can we trace every regulatory report back to its mainframe source data? How quickly can we identify data issues before they become compliance violations? Red flag: If they can't show data lineage maps for your core, your audit risk is unacceptable.
2. Knowledge Preservation Strategy
Documented mainframe data lineage that captures retiring experts' institutional knowledge is essential. Key question: "When our senior mainframe developer retires, will the next person understand how customer data flows through our systems?" If management can't show comprehensive data lineage documentation, you're gambling with operational continuity.
3. Real-Time Risk Monitoring
Establish automated mainframe data lineage monitoring with board-level dashboards. Essential metrics: Data quality scores, lineage completeness percentage, time to detect data anomalies. The question that should drive executive action: "If our mainframe data feeds incorrect information to regulators or customers, how fast do we know and respond?"
Executive Action Framework
For CEOs: 90-Day Implementation Plan
Challenge your IT leadership to implement foundational, automated mainframe data lineage tracking within 90 days. Don't accept "it's too complex" as an answer. The businesses that can prove their data integrity while competitors guess at theirs will dominate regulatory discussions and customer trust.
Your mainframe data lineage isn't just compliance – it's competitive intelligence about your own operations.
For Board Members: Governance Oversight Framework
Establish clear data lineage requirements as part of your risk management framework. Set measurable targets: 95% mainframe data lineage coverage with automated data quality monitoring across all critical flows within 12 months.
Most importantly: Make mainframe data lineage a standing agenda item, not buried in IT reports. Your ability to defend data accuracy in regulatory hearings depends on it.
The Strategic Imperative
Whether you're making operational decisions as a CEO or providing oversight as a board member, mainframe data lineage represents the convergence of risk management and competitive advantage. Organizations that master this capability while competitors remain in the dark will define the next decade of business leadership.
The question isn't whether you can afford to implement comprehensive mainframe data lineage—it's whether you can afford not to.
How confident is your leadership team in the data flowing from your mainframe to your most critical business decisions?
The Billion-Dollar Cost of Operational Blindness
The financial services industry is learning expensive lessons about the true cost of treating mainframe systems as "black boxes." Over the past few years, three major banking institutions have paid nearly $1 billion in combined penalties—not for exotic trading losses or cyber breaches, but for fundamental failures in data visibility and risk management that proper mainframe data lineage could have prevented.
With mainframes processing 70% of global financial transactions daily, 95% of credit card transactions, and 87% of ATM transactions, these aren't isolated incidents—they're wake-up calls for an industry that can no longer afford operational blindness in its most critical infrastructure.
JPMorgan's $348M Wake-Up Call: When Trading Data Goes Dark
In March 2024, JPMorgan Chase paid $348 million in penalties for a decade-long failure that left billions of transactions unmonitored across 30+ global trading venues. The US Federal Reserve and Office of the Comptroller of the Currency found that "certain trading and order data through the CIB was not feeding into its trade surveillance platforms" between 2014 and 2023.
This wasn't oversight—it was systematic breakdown of market conduct risk controls required under US banking regulations.
The Mainframe Connection
JPMorgan, like 92 of the world's top 100 banks, relies heavily on mainframe systems for core trading operations. These IBM Z systems process the vast majority of transaction volume, but the critical problem emerges when trading data originates on mainframes and feeds downstream surveillance platforms. Without comprehensive data lineage, gaps create dangerous blind spots where billions in transactions can slip through unmonitored.
The $348 million penalty signals that regulators expect complete transparency in data flows. For banks running critical operations on mainframe systems without proper data lineage, JPMorgan's experience serves as an expensive reminder: you can't manage what you can't see.
Citi's $536M Data Governance Breakdown: A Decade of Blindness
The pain continued with Citibank's even costlier lesson. In October 2020, Citi received a $400M penalty from the Office of the Comptroller of the Currency, followed by an additional $136M in combined fines in 2024 from both the OCC and Federal Reserve—totaling $536M for systematic failures in data governance and risk data aggregation that regulators called "longstanding" and "widespread."
The Core Problem
The OCC found that Citi failed to establish effective risk data aggregation processes, develop comprehensive data governance plans, produce timely regulatory reporting, and adequately report data quality status. Some issues dated back to 2013—nearly a decade of compromised data visibility.
The Mainframe Reality
Like virtually all major banks, Citi runs core banking operations on mainframes where critical risk data originates. Every loan, trade, and customer transaction flows through these platforms before being aggregated into enterprise risk reports that regulators require. The problem? Most banks treat mainframes as "black boxes" where data transformations remain opaque to downstream risk management systems.
Citi's penalty represents the cost of operational blindness in critical infrastructure. The regulatory failures around data governance and risk aggregation highlight exactly the kind of visibility gaps that comprehensive mainframe data lineage addresses.
Danske Bank's $2B+ Problem: BCBS 239's Persistent Challenge
The pattern culminates with Danske Bank's ongoing struggle, which has resulted in $2B+ in penalties since 2020. While these stemmed from various violations, many could likely have been exposed earlier through proper BCBS 239 compliance. The bank's transaction monitoring failures and AML deficiencies represent clear gaps in the comprehensive risk data aggregation that BCBS 239 requires.
BCBS 239: Banking's Most Persistent Challenge
Nearly 11 years after publication and 9 years past its deadline, BCBS 239 remains banking's most persistent regulatory challenge. The November 2023 progress report reveals a sobering reality: only 2 out of 31 global systemically important banks achieved full compliance. Not a single principle has been fully implemented across all assessed banks.
The Escalating Consequences
The ECB has made BCBS 239 deficiencies a top supervisory priority for 2025-2027, explicitly warning that non-compliance could trigger "enforcement actions, capital add-ons, and removal of responsible executives." With regulatory patience exhausted, the consequences are no longer just financial—they're existential.
The Mainframe Blind Spot: Why Traditional Approaches Fail
Most BCBS 239 discussions miss a critical point: the majority of banks' risk data originates on mainframe systems that handle core banking operations and risk calculations. The Basel Committee's assessment highlights the core issue: "Several banks still lack complete data lineage, which complicates their ability to harmonize systems and detect data defects."
With mainframes handling 83% of all global banking transactions, understanding these systems is no longer optional. Yet banks continue to struggle because:
- Legacy Complexity: Decades-old COBOL programs lack documentation and follow code patterns that traditional lineage tools can't interpret
- Operational Opacity: Data transformations through complex JCL jobs, VSAM files, and DB2 operations remain invisible to downstream risk management systems
- Technical Barriers: Business analysts can't access or understand mainframe data flows without deep technical expertise
How Mainframe Data Lineage Solves the Crisis
The solution lies in comprehensive mainframe data lineage that addresses these fundamental blind spots:
Complete Visibility: Modern tools can trace data flows from mainframe CICS transactions through DB2 operations to downstream systems, mapping exactly how critical risk data moves through complex transformations that conventional tools miss.
Business Accessibility: The right platforms enable business analysts to discover and act on mainframe information without requiring technical expertise—transforming data lineage from technical obscurity into actionable business intelligence.
Automated Monitoring: Real-time tracking of mainframe batch processes detects when critical risk calculations fail or produce inconsistent results, preventing the systematic failures that cost JPMorgan, Citi, and Danske Bank billions.
Regulatory Preparedness: Banks can trace exactly where specific data resides within mainframe environments and extract it rapidly when regulators demand it—the core capability that BCBS 239 requires.
The Regulatory Survival Imperative
After a decade of BCBS 239 implementation struggles and nearly $1 billion in recent penalties, it's clear traditional approaches aren't working. Banks still wrestling with data aggregation challenges haven't invested in understanding their mainframe data flows.
The evidence is overwhelming:
- JPMorgan's trading surveillance gaps cost $348M
- Citi's data governance failures cost $536M
- Danske Bank's ongoing compliance failures exceed $2B
- Only 2 of 31 major banks achieve full BCBS 239 compliance
With the ECB intensifying enforcement and supervisory patience exhausted, mainframe data lineage isn't just modernization—it's regulatory survival infrastructure.
The Path Forward: From Black Box to Transparency
The financial services industry stands at a crossroads. Banks can continue treating mainframe systems as mysterious legacy platforms while paying escalating regulatory penalties, or they can invest in the comprehensive data lineage capabilities that modern compliance demands.
The choice is clear: illuminate your mainframe data flows or continue paying the billion-dollar cost of operational blindness. With regulators expecting rapid and recurring risk data aggregation, banks can no longer afford to manage what they cannot see.
Ready to illuminate your mainframe data flows and achieve regulatory compliance? The path forward starts with understanding what you can't currently see—before regulators demand answers you can't provide.
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