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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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Mainframe Managed Service Providers (MSPs) have built impressive capabilities over the last several decades. They excel at infrastructure management, code conversion, and supporting complex hosting environments. Many have invested millions in advanced tools for code parsing, refactoring, and other technical aspects of mainframe management and modernization. Yet despite these strengths, MSPs consistently encounter the same bottlenecks that threaten mainframe modernization project timelines, profit margins, and client satisfaction.

In this article, we’ll explore the most common gaps MSPs face, how Zengines platform helps fill those gaps, and why Mainframe MSPs are partnering with Zengines.

The Critical Gaps in Current MSP Toolkits

Gap 1: Business Rules Discovery

While MSPs have sophisticated tools for parsing and reverse engineering COBOL code—they can extract syntax, identify data structures, and map technical dependencies—they lack capabilities for intelligent business logic interpretation. These parsing tools tell you what the code does technically, but not why it does it from a business perspective.

Current approaches to understanding the embedded business rules within parsed code require:

• Manual analysis by teams of expensive mainframe experts and business teams to interpret the business meaning behind technical code structures
• Line-by-line COBOL review to understand calculation logic and translate technical operations into business requirements
• Manual correlation between parsed code elements and actual business processes
• Expert interpretation of conditional logic to understand business rules and decision trees
• Guesswork about the original business intent behind complex branching statements and embedded calculations

Even with advanced parsing capabilities, MSPs still need human experts to bridge the gap between technical code structure and business logic understanding. This discovery phase often represents 30-40% of total project time, yet MSPs have limited tools to accelerate the critical transition from "code parsing" to "business intelligence."

The result: MSPs can quickly identify what exists in the codebase, but still struggle to efficiently understand what it means for the business—creating a bottleneck that no amount of technical parsing can solve.

Gap 2: Data Migration Tools

A critical step in any mainframe modernization project involves migrating data from legacy mainframe systems to new modern platforms. This data migration activity often determines project success or failure, yet it's where many MSPs face their biggest challenges.

While MSPs excel at physical data ETL and have tools for moving data between systems, they struggle with the intelligence layer that makes migrations fast, accurate, and low-risk:

• Manual data mapping between legacy mainframe schemas and modern system structures—a time-intensive process due to guesswork of trial-and-error
• Insufficient data quality analysis relevant to the business context, leading to "garbage in, garbage out" scenarios in the new system
• Manual transformation rule creation that requires expensive technical resources and extends project timelines
• Limited validation capabilities to ensure data integrity during and after migration
• Rules-based tools that constantly need to be adjusted when the data doesn’t fit the expected pattern - more often then not, this is the case.

These gaps expose organizations to costly risks: project delays, budget overruns, compromised data integrity, and client dissatisfaction from failed transfers. Delays and cost overruns erode margins and strain client relationships. Yet the most significant threat remains post-go-live discovery of migration mistakes. Today’s approach of manual processes are inherently time-constrained—teams simply cannot identify and resolve all issues before deployment deadlines. Unfortunately, some problems surface only after go-live, forcing expensive emergency remediation that damages client trust and project profitability.

The result: MSPs can move data technically, but lack intelligence tools to do it efficiently, accurately, and with confidence—making data migration the highest-risk component of mainframe modernization projects.

Gap 3: Testing and Validation Blind Spots

Once data is migrated from mainframe systems to modern platforms, comprehensive testing and validation becomes critical to ensure business continuity and data integrity. This phase determines whether the migration truly preserves decades of embedded business logic and data relationships.

Without comprehensive understanding of embedded business logic and data interdependencies, MSPs face significant validation challenges:

• Incomplete test scenarios based on limited business rules knowledge—testing what they assume exists rather than what actually exists in the legacy system
• Inefficient testing based on what the code presents versus what actually exists within the data
• Manual reconciliation between old and new systems that's time-intensive, error-prone, and often misses subtle data discrepancies
• Inadequate validation coverage of complex business calculations and conditional logic that may only surface under specific data conditions
• Reactive testing methodology that discovers problems during user acceptance testing rather than proactively during migration validation
• Post-go-live surprises when undocumented logic fails to transfer, causing business process failures that weren't caught during testing

The consequences: validation phases that stretch for months, expensive post-implementation fixes, user confidence issues, and potential business disruption when critical calculations or data relationships don't function as expected in the new system.

The result: MSPs have inadequate and non-optimized testing where teams test what they think is important rather than what the business actually depends on.

How Zengines Fills These Critical Gaps

Zengines has built AI-powered solutions that directly address each of these critical gaps in MSP capabilities. Our platform works alongside existing MSP tools, enhancing their technical strengths with the missing intelligence layer that transforms good modernization projects into exceptional ones.

Zengines Mainframe Data Lineage: Bridging the Business Logic Intelligence Gap

While parsing tools can extract technical code structures, Zengines Mainframe Data Lineage translates that technical information into actionable business intelligence:

• Automated Business Logic Interpretation: Our AI doesn't just parse COBOL—it understands what the code means from a business perspective. Zengines automatically identifies calculation logic, conditional business rules, and decision trees, then presents them in business-friendly visualizations that eliminate the need for manual interpretation.
• Intelligent Business Rules Extraction: Transform months of manual analysis into minutes of automated discovery. Zengines maps the relationships between data elements, business processes, files, and embedded logic, creating comprehensive documentation of how your mainframe actually implements business requirements.
• Visual Logic Flow Mapping: Interactive visualizations show not just technical data flow, but business process flow—helping teams understand why certain calculations exist, when conditional logic triggers, and how business rules cascade through the system.
• Contextual Information based on Analyst Research: Automatically generates business-readable information that explains the "why" behind technical code, enabling business analysts and technical teams to collaborate effectively without requiring scarce mainframe expertise.

MSP Impact: Transform your longest project phase into your fastest. Business logic discovery that previously required months and years of expert time now completes in days with comprehensive information that your entire team can understand and act upon.

Zengines AI Data Migration: Solving the Data Intelligence and Migration Gap

Our AI Data Migration platform transforms data migration from a risky, manual process into an intelligent, automated workflow:

• Intelligent Schema Discovery and Mapping: AI algorithms automatically understand both source mainframe schemas and target system structures, then predict optimal field mappings with confidence scoring. No more guesswork—get AI-recommended mappings that you can validate and refine.
• Automated Data Quality Analysis: Comprehensive data profiling identifies quality issues, completeness gaps, and anomalies before migration begins. Address data problems proactively rather than discovering them post-go-live.
• AI-Powered Transformation Rule Generation: Describe your transformation requirements in plain English, and Zengines Transformation Assistant generates the precise transformation syntax. Business analysts can create complex transformation rules without needing technical programming expertise.
• Automated Load File Generation: Execute data mapping and transformation into ready-to-use load files that integrate seamlessly with your existing migration tools and processes.

MSP Impact: Accelerate data migration timelines by 80% while dramatically reducing risk. Business analysts become 6x more productive, and data migration transforms from your highest-risk project component to a predictable, repeatable process.

Comprehensive Testing and Validation: Closing the Validation Loop

Zengines doesn't just help with discovery and migration—it ensures successful validation:

• Business Rules-Based Test Scenario Generation: Because Zengines understands the embedded business logic, it informs test scenarios that cover the actual business rules in the legacy system, not just the ones you know about.
• Automated Data Reconciliation: Zengines offers helpful tools like reconciliation to expose discrepancies between source and target systems, with intelligent filtering that highlights meaningful differences while ignoring irrelevant variations.
• Comprehensive Audit Trail: Complete documentation of what was migrated, how it was transformed, and validation that it works correctly—providing confidence to stakeholders and regulatory compliance where needed.

MSP Impact: Transform validation from an uncertain phase into a systematic process that focuses on exceptions. Reduce validation timelines by 50% while dramatically improving coverage and reducing post-go-live surprises.

The Integrated Zengines Advantage

Unlike point solutions in the mainframe modernization ecosystem that address isolated problems, Zengines provides an integrated platform where business logic discovery, data migration, and validation work together seamlessly:

• Light into the Black Box: Transparency into the most challenging system in the enterprise: mainframe and midranges.
• Connected Intelligence: Business rules discovered in the lineage phase automatically inform data migration mapping and validation scenarios
• End-to-End Visibility: Complete traceability from original business logic through final validation, providing unprecedented project transparency
• Unified Data Source: Single source of truth for business rules, data transformations, and validation results

This integrated approach transforms modernization from a series of risky, disconnected phases into a cohesive, intelligent process that dramatically improves outcomes while reducing timelines and risk.

The Business Impact: Why MSPs are Choosing Zengines

Accelerated Project Delivery

MSPs can deliver 50% faster overall project completion times. The discovery and data migration phases—traditionally the longest parts of modernization projects—now complete in a fraction of the time.

Improved Profit Margins

By automating the most labor-intensive aspects of modernization, MSPs can deliver projects with fewer billable hours while maintaining quality. This directly improves project profitability.

Enhanced Client Experience

Clients appreciate faster time-to-value and reduced business disruption. Comprehensive business rules documentation also provides confidence that critical logic won't be lost during migration.

Competitive Differentiation

MSPs with Zengines capabilities can bid more aggressively on timeline and cost while delivering superior outcomes. This creates a significant competitive advantage in the marketplace.

Risk Mitigation

Better understanding of business logic before migration dramatically reduces post-implementation surprises and costly remediation work.

The Strategic Imperative

As the mainframe skills shortage intensifies—with 70% of mainframe professionals retiring by 2030—MSPs face an existential challenge. Traditional manual approaches to business rules discovery and data migration are becoming unsustainable.

The most successful MSPs will be those that augment their technical expertise with AI-powered intelligence. Zengines provides that intelligence layer, allowing MSPs to focus on what they do best while dramatically improving client outcomes.

The question isn't whether to integrate AI-powered data intelligence into your modernization methodology. The question is whether you'll be an early adopter who gains competitive advantage, or a late adopter struggling to keep pace with more agile competitors.

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Data lineage is the process of tracking data usage within your 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. This is a crucial capability for any modern ecosystem, as the amount of data businesses generate and store increases every year. 

As of 2024, 64% of organizations manage at least one petabyte of data — and 41% have at least 500 petabytes of information within their systems. In many industries, like banking and insurance, this includes legacy data that spans not just systems but eras of technology.

As the data volume grows, so does the need to aid the business with trust in access to that data. Thus, it is important for companies to invest in data lineage initiatives to improve data governance, quality, and transparency. If you’re shopping for a data lineage tool, there are many cutting-edge options. The cloud-based Zengines platform uses an innovative artificial intelligence-powered model that includes data lineage capabilities to support clean, consistent, and well-organized data.

Whether you go with Zengines or something else, though, it’s important to be strategic in your decision-making. Here is a step-by-step process to help you choose the best data lineage tools for your organization’s needs.

Understanding Data Lineage Tool Requirements

Start by ensuring your selection team has a thorough understanding of not just data lineage as a concept but also the requirements that your particular data lineage tools must have.

First, consider core data lineage tool functionalities that every company needs. For example, you want to be able to access a clear visualization of the relationship between complex data across programs and systems at a glance. Impact analysis also provides a clear picture of how change will influence your current data system.

In addition, review technology-specific data-lineage needs, such as the need to ingest legacy codebases like COBOL. Compliance and regulatory requirements vary from one industry to the next, too. They also change often. Make sure you’re aware of both business operations needs and what is expected of the business from a compliance and legal perspective.

Also, consider future growth. Can the tool you select support the data as you scale? Don’t hamstring momentum down the road by short-changing your data lineage capabilities in the present.

Key Features to Look for in Data Lineage Tools

When you begin to review specific data lineage tools, you want to know what features to prioritize. Here are six key areas to focus on:

1. Automated metadata collection: Automation should be a feature throughout any data lineage tool at this point. However, the specific ability to automate the collection of metadata from internal solutions and data catalogs is critical to sustainable data lineage activity over time.
2. Integration capabilities: Data lineage tools must be able to comprehensively integrate across entities that store data — past, present, and future. This is where a tool like Zengines shines, where accessing legacy data in legacy technology has been the #1 challenge for most organizations. 
3. End-to-end visibility: Data lineage must provide a clear picture of the respective data paths from beginning to end. This is a fundamental element of quality data lineage analysis.
4. Impact analysis and research capabilities: Leading solutions make it easy for users to obtain and understand impact analysis for any data path or data changes showing data relationships and dependencies.. Further, the ability to seamlessly research across data entities assists in confidence for such analysis.
5. Tracking and monitoring: Data lineage is an ongoing activity, thus data lineage tools must be able to keep up with ongoing data change within an organization.
6. Visualization features: Visualizations - such as logic graphs - should provide comprehensive data paths across the data life cycle.
   

Keep these factors in mind and make sure whatever tool you choose satisfies these basic requirements.

Implementation and User Experience Considerations

Along with specific features, you want to assess how easy it is to implement the tool and how easy it is to use the tool.  

Start with setup. Consider how well each data lineage software solution is designed to implement within and configure to your system. For businesses that  built technology solutions before the 1980s, you may have critical business operations that run on mainframes. Make sure a data lineage tool will be able to easily integrate into a complex system before signing off on it.

Consider the learning curve and usability too. Does the tool have an intuitive interface? Are there complex training requirements? Is the information and operation accessible? 

Cost Analysis and ROI

When considering the cost of a data lineage software solution, there are a few factors to keep in mind. Here are the top elements that can influence expenses when implementing and using a tool like this over time:

• Direct cost: Take the time to compare the up-front cost of each option. Recognize and account for capability differentiation e.g., ability to integrate with legacy technology, necessary to generate a comprehensive end-to-end data lineage. The lowest-cost option is unlikely to include this necessary capability. . 
• Benefit analysis: : Consider benefits across both quantitative (e.g., time savings for automated data lineage versus manual data lineage search) and qualitative (e.g., cost avoidance for compliance and regulatory fines). 
• Total cost of ownership (TCO): Consider the big picture with your investment. Are there licensing and subscription fees? Implementation costs? Ongoing maintenance and support expenses? These should be clarified before making a decision.‍
• Expected return on investment: Your ROI will depend on things like speed of implementation, reduced costs, and accelerated digital transformations. Automated AI/ML solutions, like those that power Zengines, can provide meaningful benefit to TCO over time and  should be factored into the cost analysis.
  

Make sure to consider costs, benefits, TCO and ROI when assessing your options.

The Zengines Advantage

If you’re looking for a comprehensive assessment of what makes the Zengines platform stand out from other data lineage solutions, here it is in a nutshell:

• Zengines Mainframe Data Lineage offers a unique approach to data lineage with its robust ability to ingest and parse COBOL programs so that businesses can now have data lineage inclusive of legacy technology.  Zengines de-risks the mainframe “black box” and enables business to better and more quickly manage, modernize, or migrate mainframes.  
• Zengines comes backed by a wide range of software companies, businesses, consulting firms, and other enterprises that have successfully used our software solutions.
• Our data lineage is part of our larger frictionless data conversion and integration solutions designed to speed up the notoriously slow process of proper data management. We use AI/ML to automate and accelerate the process, from implementation through ongoing use, helping your data work for you rather than get in the way of your core functions.
• Our ZKG (Zengines Knowledge Graph) is a proprietary, industry-specific database that is always growing and providing more detailed information for our algorithms.
  

Our automated solutions create frictionless, sped-up solutions that reduce risk, lower costs, and create more accessible data lineage solutions.

Making the Final Decision

As you assess your data lineage tool choices, keep the above factors in mind. What are your industry and organizational requirements? Focus on key features like automation and integration capabilities. Consider implementation, training, user experience, ROI, and comprehensive cost analyses. 

Use this framework to help create stakeholder buy-in for your strategy. Then, select your tool with confidence, knowing you are organizing your data’s past to improve your present and lay the groundwork for a more successful future.

If you have any follow-up questions about data lineage and what makes a software solution particularly effective and relevant in this field, our team at Zengines can help. Reach out for a consultation, and together, we can explore how to create a clean, transparent, and effective future for your data.