Beyond Surface-Level Data Lineage: How Zengines Strengthens Mainframe Understanding

Your new core banking system just went live. The migration appeared successful. Then Monday morning hits: customers can't access their accounts, transaction amounts don't match, and your reconciliation team is drowning in discrepancies. Sound familiar?

If you've ever been part of a major system migration, you've likely lived a version of this nightmare. What's worse is that this scenario isn't the exception—it's becoming the norm. A recent analysis of failed implementations reveals that organizations spend 60-80% of their post-migration effort on reconciliation and testing, yet they're doing it completely blind, without understanding WHY differences exist between old and new systems.

The result? Projects that should take months stretch into years, costs spiral out of control, and in the worst cases, customers are impacted for weeks while teams scramble to understand what went wrong.

The Hidden Crisis: When Reconciliation Becomes Archaeology

Let's be honest about what post-migration reconciliation looks like today. Your team runs the same transaction through both the legacy system and the new system. The old system says the interest accrual is $5. The new system says it's $15. Now what?

"At this point in time, the business says who is right?" explains Caitlin Truong, CEO of Zengines. "Is it that we have a rule or some variation or some specific business rule that we need to make sure we account for, or is the software system wrong in how they are computing this calculation? They need to understand what was in that mainframe black box to make a decision."

The traditional approach looks like this:

1. Business analyst discovers the discrepancy
2. Tickets get raised to investigate the difference
3. Someone (usually an expensive SME) digs through legacy code
4. Weeks or months pass while they navigate thousands of lines of COBOL
5. Finally, an answer emerges—but it spawns three new questions
6. The cycle repeats

The real cost isn't just time—it's risk. While your team plays detective with legacy systems, you're running parallel environments, paying for two systems, and hoping nothing breaks before you figure it out.

The Root Problem: Legacy Systems as Black Boxes

Here's what most organizations don't realize: the biggest risk in any migration isn't moving the data—it's understanding the why behind the data.

Legacy systems, particularly mainframes running COBOL code written decades ago, have become black boxes. The people who built them are retired. The business rules are buried in thousands of modules with cryptic variable names. The documentation, if it exists, is outdated.

"This process looks like the business writing a question and sending it to the mainframe SMEs and then waiting for a response," Truong observes. "That mainframe SME is then navigating and reading through COBOL code, traversing module after module, lookups and reference calls. It’s understandable that without additional tools, it takes some time for them to respond."

When you encounter a reconciliation break, you're not just debugging a technical issue—you're conducting digital archaeology, trying to reverse-engineer business requirements that were implemented 30+ years ago.

One of our global banking customers faced this exact challenge. They had 80,000 COBOL modules in their mainframe system. When their migration team encountered discrepancies during testing, it took over two months to get answers to simple questions. Their SMEs were overwhelmed, and the business team felt held hostage by their inability to understand their own system.

"When the business gets that answer they say, okay, that's helpful, but now you've spawned three more questions and so that's a painful process for the business to feel like they are held hostage a bit to the fact that they can't get answers themselves," explains Truong.

The AI-Powered Revolution: From Reactive to Predictive

What if instead of discovering reconciliation issues during testing, you could predict and prevent them before they happen? What if business analysts could investigate discrepancies themselves in minutes instead of waiting months for SME responses?

This is exactly what our mainframe data lineage tool makes possible.

"This is the challenge we aimed to solve when we built our product. By democratizing that knowledge base and making it available for the business to get answers in plain English, they can successfully complete that conversion in a fraction of the time with far less risk," says Truong.

Here's how it works:

1. Intelligent System Understanding

AI algorithms ingest your entire legacy codebase—COBOL modules, JCL scripts, database schemas, and job schedulers. Instead of humans manually navigating 80,000 lines of code, pattern recognition identifies the relationships, dependencies, and calculation logic automatically.

2. Business Rule Extraction

The AI doesn't just map data flow—it extracts the underlying business logic. That cryptic COBOL calculation becomes readable: "If asset type equals equity AND purchase date is before 2020, apply special accrual rate of 2.5%."

3. Self-Service Investigation

When your new system shows $15 and your old system shows $5, business analysts can immediately trace the calculation path. They see exactly why the difference exists: perhaps the new system doesn't account for that pre-2020 equity rule embedded in the legacy code.

4. Informed Decision-Making

Now your team can make strategic decisions: Do we want to replicate this legacy rule in the new system, or is this an opportunity to simplify our business logic? Instead of technical debugging, you're having business conversations.

Real-World Transformation: From 2 Months to 0.4% Exception Rate

Let me share a concrete example of this transformation in action. A financial services company was modernizing their core system and moving off their mainframe. Like many organizations, they were running parallel testing—executing the same transactions in both old and new systems to ensure consistency.

Before implementing AI-powered data lineage:

• Each reconciliation question took 2+ months to resolve
• SMEs were overwhelmed with investigation requests
• The business team felt dependent on scarce technical resources
• Project timelines were constantly slipping due to reconciliation delays

After implementing the solution:

• Business analysts could research discrepancies independently
• Investigation time dropped from months to minutes
• The team achieved a 0.4% exception resolution rate
• Project risk was dramatically reduced

"The business team presents their dashboard at the steering committee and program review every couple weeks," Truong shares. "Every time they ran into a break, they have a tool and the ability to answer why that break is there and how they plan to remediate it."

The New Reconciliation Methodology

The most successful migrations we've seen follow a fundamentally different approach to reconciliation:

Phase 1: Pre-Migration Intelligence

Before you migrate anything, understand what you're moving. Use AI to create a comprehensive map of your legacy system's business logic. Know the rules, conditions, and calculations that drive your current operations.

Phase 2: Predictive Testing

Instead of hoping for the best, use pattern recognition to identify the most likely sources of reconciliation breaks. Focus your testing efforts on the areas with the highest risk of discrepancies.

Phase 3: Real-Time Investigation

When breaks occur (and they will), empower your business team to investigate immediately. No more waiting for SME availability or technical resource allocation.

Phase 4: Strategic Decision-Making

Transform reconciliation from a technical debugging exercise into a business optimization opportunity. Decide which legacy rules to preserve and which to retire.

"The ability to catch that upfront, as opposed to not knowing it and waiting until you're testing pre go-live or in a parallel run and then discovering these things," Truong emphasizes. "That's why you will encounter missed budgets, timelines, etc. Because you just couldn't answer these critical questions upfront."

Beyond Migration: The Ongoing Value

Here's something most organizations don't consider: this capability doesn't become obsolete after your migration. You now have a living documentation system that can answer questions about your business logic indefinitely.

Need to understand why a customer's account behaves differently? Want to add a new product feature? Considering another system change? Your AI-powered lineage tool becomes a permanent asset for business intelligence and system understanding.

"When I say de-risk, not only do you de-risk a modernization program, but you also de-risk business operations," notes Truong. "Whether organizations are looking to leave their mainframe or keep their mainframe, leadership needs to make sure they have the tools that can empower their workforce to properly manage it."

The Bottom Line: Risk Mitigation vs. Risk Acceptance

Every migration involves risk. The question is whether you want to manage that risk proactively or react to problems as they emerge.

Traditional reconciliation approaches essentially accept risk—you hope the breaks will be manageable and that you can figure them out when they happen. AI-powered data lineage allows you to mitigate risk substantially by understanding your system completely before you make changes.

The choice is yours:

• Continue the cycle of expensive, time-consuming reconciliation archaeology
• Or transform your approach with intelligent, self-service system understanding

Ready to Transform Your Reconciliation Process?

If you're planning a migration or struggling with an ongoing reconciliation challenge, you don't have to accept the traditional pain points as inevitable. AI-powered data lineage has already transformed reconciliation for organizations managing everything from simple CRM migrations to complex mainframe modernizations.

Schedule a demo to explore how AI can turn your legacy "black box" into transparent, understandable business intelligence.

If you've invested in enterprise data governance tools like Collibra, Alation, BigID, or Informatica, you probably expected them to transform how your organization manages, discovers, and protects data. But if you're like most firms, you're likely not seeing the full return on that investment.

The problem isn't with these tools themselves—it's with the foundation they need to work effectively: metadata management.

The challenges with manual metadata management

Data governance tools are only as good as the metadata that feeds them and most organizations struggle with three critical metadata challenges:

1. Highly Manual Load and Update Processes

Teams consistently underestimate the manual effort required to import and update data into governance tools. What should be a streamlined process becomes a resource-intensive bottleneck that delays implementation and reduces adoption.

2. Incomplete and Stale Data

Data is only updated when business teams provide refreshed information, leading to governance tools that quickly become outdated. Even worse, these tools often miss huge chunks of institutional data hidden in "black box" systems like mainframes and legacy COBOL applications. This creates a false sense of data completeness while critical business logic and data relationships remain invisible to your governance framework.

3. Lack of Standardization

Data enters governance tools in whatever format it was provided, without standardization or validation. This creates inconsistencies that undermine the tool's ability to provide reliable insights and lineage tracking.

The cost of poor metadata management

When metadata management fails, your data governance initiatives suffer across multiple dimensions:

• Data Cataloging becomes unreliable, making it difficult for users to find and trust the right data
• Data Lineage tracking breaks down, leaving gaps in understanding how data flows through your ecosystem
• Data Privacy compliance efforts are hampered by incomplete or inaccurate metadata about sensitive information

The result? Expensive governance tools that deliver a fraction of their potential value.

The Zengines Solution: AI-powered metadata automation

Zengines transforms metadata management from a manual burden into an automated advantage. Our AI-powered platform addresses the root causes of governance tool underperformance by providing:

Automated Metadata Generation

• Automatically generate metadata updates based on data changes during system migrations
• Eliminate manual effort in preparing metadata for governance tool uploads
• Turn metadata updates into "push button" operations for easier and faster behavior change

Intelligent Standardization

• Automatically standardize metadata formats across multiple systems and applications
• Ensure consistency that was previously achieved only through manual cleansing efforts
• Create uniform business data glossaries within your governance tools

Data Trust and Relevance

• Automatically detect data changes and generate metadata updates
• Provide data profiling to support privacy analysis and data quality efforts
• Classify data to identify PII (Personally Identifiable Information), CDE (Critical Data Elements), and other regulatory and compliance requirements
• Unlock massive amounts of critical institutional data through Zengines Mainframe Data Lineage, bringing previously hidden "black box" information into your governance framework to create complete data trust and visibility

Seamless Integration

• Feed metadata updates and data lineage directly into governance tools via API
• Works with existing tools like Collibra, Alation, and others
• Update governance tools with rich institutional data from Zengines Mainframe Data Lineage, including previously hidden business logic, data relationships, and system dependencies
• Maintain metadata currency without disrupting existing workflows

Real impact: Case study results

A global wealth and asset manager with $300B in AUM was struggling to drive adoption and maintain data updates in Collibra, its data governance tool.  This was further exacerbated as the entire organization underwent a 5-year business and technology transformation involving cloud migration and four new platform implementations.

The Challenge:

• The team struggled to find deliver data privacy compliance reporting
• Data privacy efforts were hindered by scattered data sets and poor metadata quality
• Metadata management was completely manual
• Many teams and applications had never had their metadata loaded into Collibra
• For the teams that had engaged with Collibra, there was lack of motivation to keep it current

Zengines Results:

• Metadata now exists for applications whose metadata had never been loaded previously
• Automatic standardization across multiple applications—eliminating manual cleansing efforts
• Push-button metadata uploads—replacing entirely manual file preparation
• Enhanced privacy compliance through automated data profiling that presents patterns to support business decision-making

Maximize your Data Governance investment

Your data governance tools have the potential to be transformative—but only when they're fed with accurate, current, and standardized metadata. Zengines removes the manual barriers that prevent these tools from delivering their full value.

Ready to unlock your data governance investment?

• Accelerate implementation timelines by automating metadata preparation
• Improve data quality and consistency across your governance tools
• Reduce the manual effort that burns out your data teams
• Enhance compliance capabilities with automated data profiling and PII detection

The vision of frictionless data conversions isn't just about moving data from one system to another—it's about creating a foundation where your data governance tools can finally deliver on their promise.

When businesses change systems—whether implementing a new vendor, modernizing legacy infrastructure, or integrating data post-M&A—the journey usually begins with one daunting step: data migration. It's a critical, complex, and historically painful process that can slow down progress and frustrate teams. 

Zengines is built to fix that. Powered by AI, Zengines simplifies every step of the data conversion process - so you can go live faster, with cleaner data, and far less manual work.

This article explores how Zengines supports every phase of the data migration lifecycle. 

End-to-end data migration support 

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Migration Analysis

Get clarity before you move anything.

Migration starts with understanding your data. Zengines provides powerful migration analysis capabilities—including data profiling and cleansing insights—so teams can assess size, scope, and complexity up front.

• Profile source data to identify null values, inconsistent formats, and field usage
• Automatically surface data quality issues
• Quickly evaluate effort and risks to better allocate resources

With this foundation, project managers and analysts can plan smarter and move faster.

Migration Mapping

Skip the guesswork. Get your data mapped in minutes.

One of the most time-consuming aspects of data migration is mapping fields between systems. Zengines automates this with AI-powered data mapping tools that predict and recommend matches between your source and target schemas.

But mapping is only part of the job. Zengines also supports data transformation, allowing you to:

• Split, merge, or reformat fields (e.g., parse first/last names or convert TX to Texas)
• Use plain-English prompts to create transformation rules—no coding required
• Apply business logic directly within the platform

Mapping and transforming your data becomes fast, intuitive, and accurate.

Migration Execution

Go from draft to load file—without the delays.

Once mappings and transformations are validated, Zengines moves you into execution mode. It automatically generates ready-to-load files tailored to your destination system.

The result?

• No more spreadsheet back-and-forth
• No dependency on engineering or consultants
• Immediate visibility into how your data will appear in the new system

Zengines enables teams to generate clean, complete files in minutes, getting you to go-live faster.

Migration Testing

Validate before - and after - you go live.

Once the data is mapped and loaded, accuracy is everything. Zengines supports comprehensive ETL, data testing, and reconciliation, so you can be confident in every field you move.

• Test data integrity during the migration
• Reconcile source and target data sets
• Surface mismatches or outliers for correction

This layer of testing is essential for reducing risk and ensuring trust in high-stakes migrations, such as financial systems, ERPs, or regulatory platforms.

Use cases across the business lifecycle

Zengines supports a wide range of migration scenarios, including:

• Data onboarding (e.g. client/customer onboarding)
• Data mapping
• Software or servicer/MSP transitions
• Post M&A integrations
• Technology modernization, including mainframes and midrange systems

…just to name a few. 

Whether you're a large enterprise or a fast-moving software provider, Zengines scales with your needs.

The smarter way to migrate your data

Data migrations don’t need to be a headache. With Zengines, business analysts and engineers can own and execute the entire process. 

You get:

• Faster time to value
• Fewer manual tasks
• Cleaner, more accurate data
• A better experience for everyone involved

Whether you’re replacing legacy systems or onboarding new customers, Zengines helps you move your data migration project forward — smarter, faster, and with confidence.

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