Your trusted source for data conversion and transformation insights.

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.

What do the Phoenix Suns, a Regional Healthcare Plan, Commercial HVAC software, and a Fortune 500 bank have in common? They all struggle with data migration headaches.

This revelation – while not entirely surprising to me as someone who's spent years in data migration – might shock many readers: every single organization, regardless of industry or size, faces the same fundamental data conversion challenges.

With over 3,000 IT executives gathered under one roof – I was able to test my hypotheses about both the interest of AI in data migrations and data migration pain points across an unprecedented cross-section of organizations in just three days. The conversations I had during networking sessions, booth visits, and between keynotes consistently reinforced that data migration remains one of the most pressing challenges facing organizations today – regardless of whether they're managing player statistics for a professional sports team or customer data for a local bank with three branches.

Day 1: Practical AI Over Theory

The conference opened with Dr. Tom Zehren's powerful keynote, "Transform IT. Transform Everything." His message struck a chord: IT leaders are navigating unprecedented global uncertainty, with the World Uncertainty Index spiking 481% in just six months. What resonated most with me was his call for IT professionals to evolve into "Enterprise Technology Officers" – leaders capable of driving organization-wide transformation rather than just maintaining systems.

This transformation mindset directly applies to data migration across organizations of all sizes – especially as every company races to implement AI capabilities. Too often, both large enterprises and growing businesses treat data conversion as a technical afterthought rather than the strategic foundation for business flexibility and AI readiness. The companies I spoke with that had successfully modernized their systems were those that approached data migration as an essential stepping stone to AI implementation, not just an IT project.

Malcolm Gladwell's keynote truly resonated with me. He recounted his work with Kennesaw State University and Jiwoo, an AI Assistant that helps future teachers practice responsive teaching. His phrase, "I'm building a case for Jiwoo," exemplified exactly what we're doing at Zengines – building AI that solves real, practical problems.

Gladwell urged leaders to stay curious when the path ahead is unclear, make educated experimental bets, and give teams freedom to challenge the status quo. This mirrors our approach: taking smart bets on AI-powered solutions rather than waiting for the "perfect" comprehensive data management platform.

Day 2: Strategic Bets and Addictive Leadership

John Rossman's "Winning With Big Bets in the Hyper Digital Era" keynote challenged the incremental thinking that plagues many IT initiatives. As a former Amazon executive who helped launch Amazon Marketplace, Rossman argued that "cautious, incremental projects rarely move the needle." Instead, organizations need well-governed big bets that tackle transformational opportunities head-on.

Rossman's "Build Backward" method resonated particularly strongly with me because it mirrors exactly how we developed our approach at Zengines. Instead of starting with technical specifications, we worked backward from the ultimate outcome every organization wants from data migration: a successful "Go Live" that maintains business continuity while unlocking new capabilities. This outcome-first thinking led us to focus on what really matters – data validation, business process continuity, and stakeholder confidence – rather than just technical data movement.

Steve Reese's presentation on "Addictive Leadership Stories in the League" provided fascinating insights from his role as CIO of the Phoenix Suns. His central question – "Are you the kind of leader you'd follow?" – cuts to the heart of what makes technology transformations successful.

Beyond the keynotes, Day 2's breakout sessions heavily focused on AI governance frameworks, with organizations of all sizes grappling with how to implement secure and responsible AI while maintaining competitive speed. What became clear across these discussions is that effective AI governance starts with clean, well-structured data – making data migration not just a technical prerequisite but a governance foundation. Organizations struggling with AI ethics, bias detection, and regulatory compliance consistently traced their challenges back to unreliable or fragmented data sources that added challenge and complexity to implement proper oversight and control mechanisms.

Universal Validation: The AI-Driven Data Migration Imperative

The most valuable aspect of Info-Tech LIVE wasn't just the keynotes – it was discovering how AI aspirations are driving data migration needs across organizations of every size. Whether I was talking with the CIO of a major healthcare system planning AI-powered diagnostics, a mid-market logistics company wanting AI route optimization, or a software development shop building AI-solutions for their clients, the conversation inevitably led to the same realization: their current data challenges couldn't support their AI ambitions.

The Universal AI-Data Challenge: Every organization, regardless of size, faces the same fundamental bottleneck: you can't implement effective AI solutions on fragmented, inconsistent, or poorly integrated data. This reality is driving a new wave of data migration projects that organizations previously might have delayed.

AI's Real Value

Throughout three days, the emphasis was clear: apply AI for measurable value, not trends. This aligns perfectly with our philosophy. We're solving specific problems:

• Reducing data migration timelines from months to weeks
• Eliminating guesswork in field mapping and transformation
• Making business analysts 6x more productive
• De-risking transformations through enhanced and accelerated data lineage

Transform IT, Transform Everything

Info-Tech's theme perfectly captures what we're seeing: organizations aren't just upgrading technology – they're fundamentally transforming operations. At the heart of every transformation is data migration. Organizations that recognize this shift early – and build migration capabilities rather than just executing migration projects – will have significant advantages in an AI-driven economy.

Zengines not just building a data migration tool – we're building an enduring capability for business transformation. When organizations can move data quickly and accurately, they can accelerate digital initiatives, adopt new technologies fearlessly, respond to market opportunities faster, and reduce transformation costs.

The Road Ahead

Malcolm Gladwell's thoughts on embracing uncertainty and making experimental bets stayed with me. Technology will continue evolving rapidly, but one constant remains: organizations will always need to move data between systems.

Our mission at Zengines is to make that process so seamless that data migration becomes an enabler of transformation rather than a barrier. Based on the conversations at Info-Tech LIVE, we're solving one of the most universal pain points in business technology.

The future belongs to organizations that can transform quickly and confidently. We're here to make sure data migration never stands in their way.

Interested in learning how Zengines can accelerate your next data migration or help you understand your legacy systems? Contact us to discuss your specific challenges.

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.

Our CEO Caitlyn Truong was recently featured as a guest contributor in AI Journal, exploring how artificial intelligence is fundamentally transforming data migration from a costly, risky burden into a strategic business enabler.

Key Takeaways from the Article

• ‍The Knowledge Gap Crisis: Traditional data migration fails because current-state experts don't understand new systems, while implementation teams lack visibility into source data nuances—creating costly delays and failed implementations.‍
• Full-Spectrum AI Integration: Unlike point solutions, successful AI data migration leverages the complete AI ecosystem—from clustering algorithms to LLMs—addressing every phase of the migration lifecycle within a unified platform.‍
• Rapid Source Data Comprehension: AI automatically profiles source data in minutes, identifying quality issues and anomalies that traditionally take analysts weeks or months to discover manually.‍
• Mapping Revolution: AI-powered pattern recognition generates mapping recommendations with 85-95% accuracy in seconds, compared to manual approaches that take months—delivering 6x productivity improvements for business analysts.‍
• Plain English Transformations: LLMs enable business analysts to describe requirements in natural language ("split the full name field") and receive production-ready transformation code instantly, eliminating technical handoffs.‍
• Black Box Liberation: AI automatically parses decades-old COBOL code and maps data flows through thousands of modules in minutes, democratizing access to trapped legacy system knowledge.

Ready to Learn More?

Want to see how Zengines can transform your organization's approach to data migration? Schedule a demo to experience frictionless data conversions firsthand.

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 

‍

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.

‍

‍

We're excited to unveil our refreshed visual identity, a transformation that mirrors our company's evolution, recent funding success, and expanding product portfolio. This strategic rebrand marks a pivotal chapter in our story, reflecting our company's growth and evolution while honoring the core principles that have guided us from the beginning.

The evolution of Zengines

Born from a vision of technological freedom in 2020, Zengines emerged with a clear purpose: to ensure data maintains its utility and integrity when transitioning between systems to remain valuable regardless of technological evolution. We recognized that companies everywhere were being handcuffed by their data - trapped in legacy systems and hindered by the complex, time-consuming process of data migration. What should have been a straightforward path to digital transformation was instead a journey filled with roadblocks, delays, and frustration.

Our journey began with a powerful AI-driven data migration solution that dramatically simplified how organizations convert and migrate their valuable data end-to-end. As we partnered with more clients, we discovered a critical insight: many businesses couldn't even begin their migration journey because they were paralyzed by "black box" legacy systems. Decades of mainframe COBOL code and undocumented processes had created an impenetrable maze of data paths that no one fully understood.

This revelation led to our groundbreaking Mainframe Data Lineage product, technology that illuminates these black boxes by exposing the hidden data paths, calculation logic, and relationships buried within complex legacy systems. By helping companies understand their current data environment first, we enable them to confidently plan and execute their migration or modernization strategies.

Today, with our recent funding round success, our comprehensive platform addresses the full spectrum of data transformation challenges, from unveiling the mysteries of legacy code to seamlessly migrating data into modern systems. As we've grown from a promising startup to an established technology partner for global enterprises, we needed a visual identity that reflected this evolution and maturity.

Our new visual language

Our refreshed identity system embodies our brand attributes of being trusted, exciting, and hyper-intelligent. The new design system is built around a sophisticated geometric serif logotype that communicates intelligence and innovation.

Our new color palette distinguishes the Zengines brand, adding meaning, energy, and depth to our identity.

A key visual element in our new identity is the Sankey illustration language, which makes visible the invisible connections between systems. Inspired by the fluid connections formed during data migration, this visual element adds movement and reflects the intelligent transformation at the heart of our work – all to maintain data continuity and utility through the inevitability of enterprise and systems change.

Why this matters

This new identity isn't just about aesthetics - it's a reflection of our growth and maturity as an organization. Our client roster has expanded significantly to include global enterprise organizations that recognize the critical importance of managing data transitions and modernization efforts effectively. These partnerships with Fortune 500 financial institutions, global wealth managers, and leading technology providers have pushed us to elevate every aspect of our business, including how we present ourselves visually.

As we've expanded our capabilities from AI Data Migration to include Mainframe Data Lineage, our visual system needed to convey the sophisticated technology and trusted expertise we bring to solving complex data challenges. Enterprise clients with decades of legacy systems and millions of customer records require a partner they can trust implicitly with their most valuable asset - their data. Our refined visual identity now properly reflects the enterprise-grade solutions we deliver and the confidence our clients place in us.

Our commitment remains unchanged

While our look has evolved, our fundamental mission remains steadfast: to create a world where data retains utility and integrity regardless of technological evolution, and technology decisions are based on business needs - not migration fears or legacy constraints. We remain committed to helping organizations reduce time, cost and risk while ensuring successful project outcomes through our AI-powered solutions that empower the business user.

As we continue to grow and evolve, this new visual identity will serve as a strong foundation for clearly communicating the value we deliver - making complex data migrations and research simpler, faster, and more reliable for our customers.

We will continue to serve our customers with the same dedication and innovation they've come to expect from Zengines, now with a visual identity that better reflects who we are today.

Looking to learn more about our products and capabilities?

Schedule a demo with our team today.

In this episode of The Disruptor Podcast, host John Kundtz interviews Caitlyn Truong, CEO and Co-founder of Zengines.

Mainframe Modernization Prioritization and Challenges

While mainframes are highly performant and reliable, many organizations are seeking to modernize them to de-risk legacy technology.

Mainframe legacy technology is risky to organizations for several reasons, including:

• Code written decades ago with SMEs no longer available
• Fewer people familiar with older programming languages
• Lack of standards in older code making maintenance difficult

According to Caitlyn, "There are more lines of COBOL code in existence than any other programming language," making mainframe modernization particularly challenging.

Zengines Mainframe Data Lineage Product

Caitlyn explains that Zengines' Mainframe Data Lineage product was developed when they discovered a client struggling to modernize because their mainframe was a "black box." The product ingests schemas, metadata, code (including COBOL and JCLs) to create a data lineage information base, helping business analysts understand calculation logic and conditions critical to modernization success.

Zengines' Mainframe Data Lineage product helps organizations understand their legacy systems by providing:

• Interactive research application and data
• Data lineage with graphical visualization
• Relationship mapping between modules, tables, fields, and variables
• Tables and fields inventory with profiling
• Calculation logic analysis
• Branching/conditional statement analysis

Recommendations for Mainframe Modernization Success

Caitlyn's recommendations for mainframe modernization success include:

• Understand what you're converting and the full lineage/interdependencies
• Have a guiding strategy and design - use phased approaches rather than "big bang"
• Employ proper tools for data lineage to answer questions about the system
• Start small with something you know you can convert, then celebrate successes

Her most important advice is to "get started" rather than spending too much time planning: "You'll learn and get more value just by doing."

Listen to the full episode

‍

In today's increasingly regulated financial landscape, banks and financial institutions face mounting pressure to ensure complete visibility and traceability of their Critical Data Elements (CDEs). While regulatory frameworks like BCBS 239, CDD, and CIP establish clear requirements for data governance, many organizations struggle with implementation, particularly when critical information resides within decades-old mainframe systems.

These legacy environments have become the Achilles' heel of compliance efforts, with opaque data flows and hard-to-decipher COBOL code creating significant blind spots. Zengines Mainframe Data Lineage product offers a revolutionary solution to this challenge, providing unparalleled visibility into "black box" systems and transforming regulatory compliance from a time-consuming burden into an efficient, streamlined process.

The Regulatory Challenge of Critical Data Elements

For banks and financial services firms, managing Critical Data Elements (CDEs) is no longer optional - it's a fundamental regulatory requirement with significant implications for compliance, risk management, and operational integrity. Regulations like BCBS 239, the Customer Due Diligence (CDD) Rule, and the Customer Identification Program (CIP) mandate that financial institutions not only identify their critical data but also understand its origins, transformations, and dependencies across all systems.

However, for institutions with legacy mainframe systems, this presents a unique challenge. These "black box" environments, often powered by decades-old COBOL code spread across thousands of modules, make tracing data lineage a time-consuming and error-prone process. Without the right tools, financial institutions face substantial risks, including regulatory penalties, audit failures, and compromised decision-making.

‍

"Financial institutions today are trapped between regulatory demands for data transparency and legacy systems that were never designed with this level of visibility in mind. At Zengines, we've created Mainframe Data Lineage to bridge this gap, turning black box mainframes into transparent, auditable systems that satisfy even the most stringent CDE requirements." - Caitlyn Truong, CEO, Zengines

‍

The Hidden Compliance Challenge in Legacy Systems

Many financial institutions operate with legacy mainframe technology that can contain up to 80,000 different COBOL modules, each potentially containing thousands of lines of code. This complexity creates several critical challenges for CDE compliance:

1. Opacity of Data Origins: When regulators ask "Where did this value come from?", companies struggle to provide clear, documented answers from within mainframe systems.
2. Calculation Verification: Understanding how critical values like interest accruals, risk assessments, or customer identification data are calculated becomes nearly impossible without specialized tools.
3. Conditional Logic Tracing: Determining why specific data paths were followed or how specific business rules are implemented requires manually tracing through complex code branches.
4. Resource Scarcity: Limited availability of mainframe or COBOL experts makes compliance activities dependent on a shrinking pool of specialized talent.
5. Documentation Gaps: Years of system changes with inconsistent documentation practices have left critical knowledge gaps about data elements and their transformations.

‍

"The challenge with mainframe environments isn't that the data isn't there—it's that it's buried in thousands of COBOL modules and complex code paths that would take months to manually trace. Zengines automates this process, reducing what would be weeks of research into minutes of interactive exploration." - Caitlyn Truong, CEO, Zengines

‍

Introducing Zengines Mainframe Data Lineage

Zengines Mainframe Data Lineage product is purpose-built to solve compliance challenges like these by bringing transparency to legacy systems. By automatically analyzing and visualizing mainframe data flows, it enables financial institutions to meet regulatory requirements without the traditional manual effort.

How Zengines Transforms CDE Compliance

1. Automated Data Traceability

Zengines ingests COBOL modules, JCL code, SQL, and other mainframe components to automatically map relationships between data elements across your entire mainframe environment. This comprehensive approach ensures that no critical data element remains untraced.

2. Visual Data Lineage

Instead of manually tracing through thousands of lines of code, Zengines provides interactive visualizations that instantly show:

• Where data originates
• How it transforms through calculations
• Which conditions affect its processing
• Where it ultimately flows

This visualization capability is particularly valuable during regulatory examinations, allowing institutions to demonstrate compliance with confidence and clarity.

3. Calculation Logic Transparency

For BCBS 239 compliance, institutions must understand and validate calculation methodologies for risk data aggregation. Zengines automatically extracts and presents calculation logic in human-readable format, making it simple to verify that risk metrics are computed correctly.

4. Branch Condition Analysis

When regulators question why certain customer records received specific treatment (critical for CDD and CIP compliance), Zengines can immediately identify the conditional logic that determined the data path, showing exactly which business rules were applied and why.

5. Comprehensive Module Statistics

Zengines provides detailed metrics about your mainframe environment, helping compliance teams understand the scope and complexity of systems containing critical data elements.

‍

"When regulators ask where a critical value came from or how it was calculated, financial institutions shouldn't have to launch a massive investigation. With Zengines Mainframe Data Lineage, they can answer these questions confidently and immediately, transforming their compliance posture from reactive to proactive." - Caitlyn Truong, CEO, Zengines

‍

Real-World Impact: Accelerating Compliance Activities

Financial institutions using Zengines Mainframe Data Lineage have experienced transformative results in their regulatory compliance activities:

• 90% Reduction in Audit Response Time: Questions about data calculations that previously took weeks or months to research can now be answered in minutes.
• Enhanced Confidence in Regulatory Reporting: With the ability to see, follow, and explain data origins and transformations, institutions can ensure the accuracy of regulatory reports.
• Reduced Dependency on Specialized Resources: Business analysts can now answer many compliance questions without requiring mainframe expertise.
• Improved Risk Management: Comprehensive visibility into how critical risk metrics are calculated enables better oversight and governance.
• Future-Proofed Compliance: As regulations evolve, having comprehensive data lineage documentation ensures adaptability to new requirements.

Beyond Compliance: Strategic Benefits

While regulatory compliance drives initial adoption, financial institutions discover additional strategic benefits from implementing Zengines Mainframe Data Lineage:

1. System Modernization Support: The detailed understanding of data flows facilitates safer, faster and more accurate modernization from legacy systems - this may include requirements gathering, new development, data migration, data testing, reconciliation, etc.
2. Operational Efficiency: Rapid identification of data dependencies reduces development time for system changes.
3. Risk Reduction: Comprehensive visibility into mainframe operations reduces operational risk associated with mainframe management and changes.
4. Knowledge Preservation: As mainframe experts retire, their implicit knowledge becomes explicitly documented through Zengines.

‍

"What we've discovered working with financial services firms is that CDE compliance isn't just about satisfying regulators—it's about fundamentally understanding your own critical data. Our Mainframe Data Lineage solution doesn't just help banks pass audits; it gives them unprecedented insight into their own operations." - Caitlyn Truong, CEO, Zengines

‍

Getting Started with Zengines

For financial institutions struggling with CDE compliance across legacy systems, Zengines offers a proven path forward. The implementation process is designed to be non-disruptive, with no modifications required to your existing mainframe environment.

The journey to compliance begins with a simple assessment of your current mainframe landscape, followed by automated ingestion of your code base. Within days, you'll have unprecedented visibility into your critical data elements – transforming your compliance posture from reactive to proactive.

In today's regulatory environment, financial institutions can no longer afford the uncertainty and risk associated with "black box" mainframe systems. Zengines Mainframe Data Lineage brings the transparency and traceability required not just to satisfy regulators, but to operate with confidence in an increasingly data-driven industry.

‍

In this episode of The Disruptor Podcast, host John Kundtz interviews Caitlyn Truong, CEO and Co-founder of Zengines.

This show explores how Zengines disrupts organizations' efforts to automate the end-to-end data conversion process.

Caitlyn shares information from her background in electrical and computer engineering and consulting. She often saw the problems with data conversion and migration in large organizations, especially in financial services.

This led her to co-found Zengines to help ensure data stays useful during modernization.

Key points from their discussion

• Understanding Data Migration: It includes all steps to get a new system running, from getting the data mapped and moved to post migration testing.
• Common Pitfalls: Not accounting for all the steps, lack of a plan, not using tools, and not underestimating the work effort.
• Advice for Smoother Migration: Understand all the steps, plan clearly, use tools (especially AI), empower business analysts, and work in steps.

Zengines' Disruptive Way

• Zengines gives business analysts AI tools to automate mapping and changing data.
• This "shift left" approach reduces the need for large teams, making data conversion faster and more efficient by letting the business directly influence data changes.
• This approach moves the work earlier in the process, making data conversion less expensive, more productive, and quicker by letting the business directly affect how data is changed.

Stay tuned for Part 2 of this conversation, where Caitlyn and John shift gears and explore the challenges many enterprises face with Mainframe Modernization.

Listen to the full episode

‍

In today's rapidly evolving technology landscape, organizations with legacy mainframe systems face increasing pressure to modernize. Whether driven by cost concerns, skills shortages, or the need for greater agility, mainframe modernization has become a strategic imperative.

However, there's no one-size-fits-all approach. Let's explore the various paths to modernization and how platforms like Zengines can help you navigate this complex journey.

The Mainframe Modernization Spectrum

The various ways organizations are approaching mainframe modernization include:

1. Rehosting (Lift and Shift)

What it is: Moving mainframe applications to new hardware with minimal code changes, often to cloud infrastructure.

‍

2. Replatforming

What it is: Migrating applications to a new platform while making moderate modifications to the code.

‍

3. Code Translation/Automated Conversion

What it is: Automatically converting legacy code (like COBOL) to modern languages like Java or C#.

‍

4. Refactoring

What it is: Restructuring existing code without changing external behavior.

‍

5. Complete Rewrite/Rebuilding

What it is: Redeveloping applications from scratch using modern languages and architectures.

‍

6. Replacing with Commercial Software

What it is: Abandoning legacy applications for newer commercial off-the-shelf solutions.

‍

The Critical Role of Data in Modernization

Data migration remains "the highest risk during any systems change" according to industry experts. Organizations face numerous challenges including:

• Unpredictable data values and formats
• Incomplete documentation
• Unknown data quality and sources
• Legacy technology constraints
• Scarce skilled resources

Before embarking on any modernization journey, organizations need to understand their current systems deeply. This process becomes particularly challenging with legacy mainframes that have been operating for decades with limited documentation and dwindling expertise.

How Zengines Transforms the Modernization Process

Zengines tackles the two most critical aspects of mainframe modernization:

1. Mainframe Data Lineage Understanding

Zengines' Mainframe Data Lineage technology illuminates the "black box" of legacy systems by:

• Providing interactive research into applications with graphical visualizations
• Showing the relationships between modules, tables, fields, and variables
• Analyzing calculation logic and conditional statements
• Revealing data paths, sources, and transformations

This deep visibility allows organizations to understand how their current systems work before they attempt migration, preventing costly errors and unexpected outcomes.

2. AI-Powered Data Migration Tools

Zengines accelerates data migration through:

• AI algorithms that analyze data scope, schemas, and inputs
• Automated mapping predictions that save countless hours of manual work
• Intelligent data transformation with natural language interaction
• Comprehensive data quality identification and fixing
• Streamlined testing and reconciliation

Organizations using Zengines can complete data migration tasks in minutes rather than months, dramatically reducing the time, cost, and risk associated with modernization projects.

The Bottom Line

Mainframe modernization is a complex journey with multiple potential paths. The right approach depends on your organization's specific goals, timeline, budget, and risk tolerance.

What's universal, however, is the need to understand your legacy systems and data thoroughly before making changes. With Zengines, organizations gain both the deep visibility into their current mainframe operations and the powerful tools to migrate data efficiently and accurately.

By reducing the highest-risk aspects of modernization, Zengines helps organizations avoid becoming another cautionary tale of failed transformations and instead realize the full benefits of their technology investments.

Ready to learn more?

Connect with our team to learn more about how we’re supporting some of the largest and most complex mainframe modernizations today.  

‍

The credit union industry is experiencing unprecedented consolidation. In 2024, we witnessed credit unions buying a record-breaking 22 banks, representing nearly one-fifth of all banking industry deal activity that year.

This trend shows no signs of slowing down, as credit unions increasingly look to strategic mergers and acquisitions to enhance their member experience, expand their geographic footprint, and diversify their service offerings to better serve their communities.

This article will explore the leading drivers behind this trend, most common challenges, and how credit unions can leverage a tool like Zengines to expedite and de-risk their post-merger integrations.

The Record-Breaking M&A Landscape for Credit Unions

While traditional credit union-to-credit union mergers have been common for decades, what's particularly notable about the current wave is the increasing prevalence of credit unions acquiring banks. By mid-2024, the total assets of banks being acquired by credit unions had already exceeded $7.2 billion, surpassing the previous annual record of $5.15 billion set in 2022.

This surge in M&A activity is driven by several factors:

• Need for scale: Credit unions are seeking economies of scale to invest in technology and compete with larger financial institutions
• Expansion opportunities: Acquisitions offer credit unions rapid entry into new markets and customer segments
• Talent acquisition: Mergers provide access to specialized expertise, particularly in commercial banking
• Diversification: Credit unions aim to broaden their service offerings beyond traditional consumer finance

However, alongside these strategic benefits comes a significant challenges that can make or break the success of any merger: data integration and conversion.

The Hidden Challenges in Credit Union Mergers: Data Integration & Conversion

When financial institutions merge, they bring together not just their assets and customer bases, but also their systems, datastores, and historical data, which are often disparate, complex, and unpredictable. The success of a merger largely depends on how effectively the institutions can integrate their data systems to ensure seamless operations and maintain customer satisfaction.

Consider this scenario: Credit Union A acquires Bank B. Credit Union A now needs to integrate:

• Core banking systems
• Customer relationship management (CRM) platforms
• Loan origination and loan servicing systems
• Mobile and online banking interfaces
• Financial, compliance and reporting systems
• Legacy systems, often including mainframes with decades-old code

This integration process is typically where mergers face their greatest risks. Without proper data migration and integration, the merged entity can experience:

• Extended transition periods
• Customer data errors
• Compliance violations
• Transaction processing failures
• Customer service disruptions
• Inability to realize projected cost savings

As one credit union CEO recently confided, "We were prepared for the regulatory hurdles of our acquisition, but what nearly derailed us was the operational complexity of integrating our data systems."

Zengines: De-risking Data Migration for Credit Union Mergers

This is precisely where Zengines' AI-powered data migration and mainframe data lineage solutions become invaluable for credit unions navigating the complex post-merger landscape.

AI-Driven Data Migration: From Months to Minutes

Zengines' AI Data Migration product transforms the traditionally painful process of data conversion by:

1. Analyzing source and target systems using AI algorithms to understand schemas, inputs, and data relationships automatically
2. Predicting accurate mappings between disparate systems in minutes, not months
3. Identifying data quality issues proactively before they derail your migration
4. Automating transformation rule creation with our proprietary LLM capabilities
5. Accelerating reconciliation processes to ensure data integrity across systems

With Zengines, a regional credit union would be able to reduce its post-merger data migration timeline by 50% or more, while simultaneously de-risking the overall effort via improved data quality and accuracy.

Mainframe Data Lineage: Illuminating the Black Box

For credit unions acquiring banking institutions with legacy mainframe systems, our Mainframe Data Lineage solution provides unprecedented visibility into these "black box" environments by:

• Exposing data paths and sources through interactive visual interfaces
• Revealing relationships between modules, tables, and fields across the system
• Documenting calculation logic and business rules embedded in legacy code
• Identifying data dependencies to prevent unexpected disruptions during migration
• Enabling informed decisions about what systems to maintain, modernize, or retire

One executive who leveraged our Mainframe Data Lineage tool noted: "What would have required months of manual code review by expensive specialists was completed in days by our business analysts. We uncovered critical business logic that would have otherwise been lost during the transition."

The Zengines Advantage for Credit Union M&A

As credit unions continue to pursue strategic acquisitions, Zengines offers a compelling competitive advantage:

• Reduced risk: Minimize the possibility of data loss, inaccuracies, and compliance issues
• Accelerated timeline: Compress integration and migration schedules from years to months, and from months to weeks
• Cost efficiency: Dramatically reduce the labor costs associated with manual data mapping and transformation
• Business continuity: Maintain uninterrupted service for members throughout the transition
• Self-service capability: Empower business analysts to lead data migration efforts without heavy reliance on IT specialists

Turning Data Integration from a Risk to an Advantage

The record pace of credit union M&A activity brings tremendous opportunities for growth and innovation in the industry. However, the institutions that will realize the full value of their acquisitions are those that master the complex challenge of data integration and data conversion.

With Zengines' AI-powered data migration and mainframe data lineage solutions, credit unions can transform what has traditionally been the riskiest aspect of mergers into a strategic advantage.

By accelerating data integration, improving accuracy, and reducing costs, Zengines helps credit unions focus on what matters most: leveraging their expanded capabilities to better serve their members.

To learn how Zengines can support your credit union's merger or acquisition strategy, contact us for a personalized demonstration.

‍

In the rapidly evolving financial services landscape, credit unions across the country are facing mounting pressure to modernize their core banking systems. This transformation isn't merely about keeping up with technology trends—it's about survival, competitiveness, and meeting the changing expectations of members.

As we navigate through 2025, the urgency around core banking modernization has never been greater. This article will explore why credit unions are prioritizing these initiatives now, the challenges they face, and how Zengines is helping them navigate this complex journey.

The Growing Urgency for Core Banking Modernization

Aging Infrastructure Reaching Critical Limitations

Many credit unions are still operating on legacy core systems that are up to 40 years old, running on mainframe hardware coded with outdated programming languages such as COBOL. These systems were designed for a different era of banking and struggle to support modern digital services.

Recent incidents highlight the vulnerability of these aging systems—in late 2023, approximately 60 U.S. credit unions experienced significant outages due to a ransomware attack against a third-party service provider, exposing the fragility of outdated infrastructure.

Rising Member Expectations

Today's credit union members expect seamless, personalized digital experiences that rival those offered by fintech companies. They want real-time transaction processing, instant payments, and mobile-first interfaces.

Legacy core systems – built decades ago – simply weren’t architected or designed for today’s modern needs, so they aren’t able to deliver these capabilities at scale or at speed. Credit unions that can't meet these expectations risk losing members to competitors who can.

Competitive Pressures from Digital-First Financial Services

Traditional banks and fintech startups are aggressively targeting credit union members with innovative digital services. The competitive landscape has become more intense, particularly as we move through 2025, with fintechs offering specialized services that many credit unions struggle to match due to core system limitations. According to CUInsight's 2025 trends report, digital banking is no longer a differentiator but a baseline expectation.

Business Expansion Opportunities

Credit unions are increasingly looking to expand into commercial banking services to grow their business. The opportunity to capture market share from traditional banks in 2025 is significant, but success requires modern treasury platforms with capabilities like real-time cash management, automated loan underwriting, and advanced fraud detection—all of which demand a modern core banking foundation.

Regulatory and Compliance Requirements

Evolving regulatory requirements are putting additional strain on legacy systems. Compliance with new data privacy regulations, security standards, and reporting requirements is becoming increasingly difficult with outdated core systems, creating operational and legal risks for credit unions.

Common Challenges in Core Banking Conversions

Data Migration Complexity

One of the most significant challenges in any core conversion is migrating decades of financial data accurately and completely. Credit unions struggle with data inconsistencies, missing information, and mapping complex relationships between different data elements. The risk of data loss or corruption during migration can have severe consequences for member trust and operational continuity.  

Two key drivers of this challenge are the lack of resources (people/solutions with pattern recognition on the data conversion) and the lack of automation tools to deal with the unpredictability, messiness and volume of data.

Technical Integration Hurdles

Modern banking requires seamless integration between the core and numerous third-party systems and services. Legacy cores often lack open APIs and interoperability features, making integration with modern services difficult and expensive. Credit unions frequently find themselves trapped in a complex web of customizations and workarounds.

Operational Disruption Risks

Core conversions are high-stakes projects with significant operational risks. Any downtime or functionality issues can directly impact member services and trust. The fear of disruption often leads credit unions to delay necessary modernization, creating a vicious cycle of increasing technical debt and growing conversion complexity.

Resource and Expertise Constraints

Many credit unions lack the specialized technical expertise needed to execute a successful core conversion. The mainframe and legacy system knowledge required is increasingly scarce as skilled professionals retire. Additionally, the complexity of these projects demands higher-cost resources that may strain already tight budgets.

Extended Implementation Timelines

Core conversion projects typically span multiple years, with some credit unions reporting wait times of 2-3 years just to begin implementation with major providers.

These extended timelines delay the realization of benefits and create challenges in maintaining project momentum and stakeholder support. According to EY's case study, core modernization journeys often extend to five years or more.

How Zengines Solutions Address These Challenges

AI-Powered Data Migration

Zengines' data migration solution leverages advanced AI algorithms to dramatically accelerate and de-risk the data conversion process. Our technology automatically analyzes source data, predicts optimal mappings, and identifies data quality issues in minutes rather than months. This AI-driven approach reduces the time and cost associated with data migration while significantly improving accuracy.

An incremental modernization process is crucial for credit unions that need to unlock their mainframe data for product innovation while keeping security at the forefront.

Key Benefits:

• Automated schema matching and data mapping that's 6x faster than manual methods
• Intelligent data profiling that surfaces quality issues before they impact your conversion
• AI-assisted transformation rules that eliminate costly engineering time
• Comprehensive testing and reconciliation tools to ensure data integrity

Mainframe Data Lineage for Legacy Systems

For credit unions with mainframe-based core systems, Zengines' Mainframe Data Lineage solution provides unprecedented visibility into "black box" legacy applications.

Our technology parses mainframe code, job schedulers, and data structures to create a comprehensive map of data flows, business rules, and system dependencies. This addresses what Fiserv identifies as a critical need: understanding the business case and outcomes before undertaking modernization.

Key Benefits:

• Visual mapping of data relationships across legacy systems
• Discovery of embedded business logic and calculation rules
• Identification of data interdependencies to reduce conversion risks
• Acceleration of requirements gathering for the new core system

Real-World Success

Credit unions and banks implementing Zengines solutions have experienced remarkable improvements in their core conversion projects:

• 80% faster data migration compared to traditional methods
• 6x productivity increase for business analysts working on mapping and transformation
• 99% reduction in reconciliation break resolution, significantly decreasing time and risk in testing, post-conversion data issues, and member or customer impact
• Months saved in the overall conversion timeline

As one executive recently noted: "What would have taken our team months to accomplish manually, Zengines helped us complete in weeks. The AI-assisted mapping and transformation capabilities dramatically accelerated our timeline while giving us confidence in the accuracy of our data migration."

The time for modernization is now - Zengines can help

The urgency for credit unions to modernize their core banking systems continues to grow as we move through 2025. As FIS emphasizes, "A bank deciding to keep playing the waiting game is taking a major risk" in today's competitive landscape. Those that successfully navigate this transformation will be positioned to thrive in an increasingly competitive and digitally-focused financial services landscape.

With Zengines' AI-powered data migration and mainframe data lineage solutions, credit unions can overcome the most challenging aspects of core conversion projects, reducing risk, accelerating timelines, and ensuring a seamless transition for their members.

As the Federal Reserve Bank of Kansas City notes, "Depository institutions (DIs) that have already completed their core system modernization and realized the benefits have a competitive advantage in the banking and payments markets."

Don't let your technology project move too slowly or your data migration become a barrier to progress. Contact Zengines today to learn how our solutions can help your credit union successfully modernize your core banking system and prepare for the future of financial services.

‍

‍

In the world of mainframe modernization, data lineage tools play a crucial role in helping organizations understand their legacy systems. However, not all data lineage solutions are created equal.

Where many data lineage solutions stop at high-level database flows, Zengines allows you to dive deeper into your mainframe ecosystem - illuminating the actual transformations, variable names, and processing logic that other tools don’t reveal.

The limitations of traditional mainframe data lineage

Most mainframe data lineage tools on the market today provide only surface-level insights. They typically:

• Focus exclusively on database queries (primarily DB2)
• Track simple movement of data between database tables
• Overlook file-based data processing that's common in mainframes
• Miss crucial data transformations happening within programs
• Fail to connect siloed systems and applications

As one Zengines expert puts it, traditional tools "simply look at the queries and see what data is being moved around in the queries." While this provides some value, it falls dramatically short of delivering the comprehensive understanding needed for most successful modernization projects.

The Zengines Difference: Depth where it matters

Zengines provides depth where it matters. Specifically:

1. Comprehensive Data Source Support

Unlike competitors that focus only on relational databases, Zengines handles the full spectrum of mainframe data. This is crucial because mainframes "often shuffle around their data in a set of files" and "almost everybody receives their data from the outside world in the form of files."

Zengines analyzes data regardless of source—whether it's in databases, flat files, reports, or interfaces—providing a truly complete picture of your data landscape.

2. Unparalleled Detail in Data Transformations

While other tools merely show that data moved from point A to point B, Zengines reveals exactly what happened to that data along the journey.

It exposes:

• Detailed calculations and formulas
• Conditional logic and branching decisions
• Data type conversions and formatting
• Field-level transformations
• Business rule applications

This level of detail is like the difference between knowing a meal was prepared versus having the complete recipe with step-by-step instructions.

3. Field Name Tracing Across Systems

A unique challenge in mainframes is that the same data element can have different names across different programs. For example, "first_name" in one program might be "fname" or "f_name" in others.

Zengines can tell you the 50 names that single piece of data had across the different modules, creating connections that other tools miss entirely. This capability is invaluable when trying to understand data as it moves through a complex ecosystem of programs.

4. Precise Sequencing of Operations

Understanding not just what happens to data but in what order is critical for accurate modernization. Zengines excels by showing "step one did this, step two did that, step three did this," revealing the exact sequence of operations applied to your data.

This sequential view is impossible to derive by simply looking at code, yet it's essential for truly understanding business logic.

5. True Diagnostic Power

The ultimate test of data lineage comes when diagnosing discrepancies between legacy and new systems. Zengines enables organizations to "diagnose why your new system didn't get the same calculation that you were expecting" by exposing every detail of how data is processed.

This capability proves invaluable when organizations must determine whether differences in calculations represent errors or intended changes in methodology.

How is Zengines different from a code parser?

When discussing mainframe data lineage, one might ask, "Isn't Zengines just another code parser?" It's a fair question that deserves clarification.

Traditional code parsers are indeed powerful technical tools that read commands in languages like COBOL, RPG, PL1, and Assembler. They can dissect code structure and show technical pathways. However, they're fundamentally built for engineers with technical use cases: understanding code impacts, managing program interdependencies, or supporting development.

Zengines stands apart from these traditional code parsers in these crucial ways:

1. Business-Oriented Rather Than Technical-Oriented

While parsers deliver technical information for technical users, Zengines transforms complex technical insights into business-relevant context. As our CEO explains: "A parser supports a technical use case. Our platform allows users to answer a business question, supported based on all of the technical analysis and lineage that understands the ‘business of the data’."

2. User-Driven Information Architecture

Zengines began by building a comprehensive information foundation similar to parsers, but then took a critical extra step by asking: "What questions does an analyst need answered during modernization?" This user-centric approach shaped how information is presented and accessed, making the vast technical details digestible and valuable for business users.

Our system can handle the same depth that technical tools provide but organizes it to deliver actionable business insights.

Translating complexity into clarity

Perhaps most importantly, Zengines translates technical complexity into plain English language so that business analysts and stakeholders—not just technical specialists—can understand what's happening in their systems.

This democratization of insight is critical in today's environment, where mainframe expertise is increasingly scarce and organizations need to bridge the knowledge gap between legacy specialists and current development teams.

The impact on modernization success

The depth of Zengines' data lineage capabilities directly translates to modernization success by:

• Reducing the risk of missing critical business logic
• Accelerating the validation of new systems
• Enabling more accurate estimates and project planning
• Bridging the knowledge gap between legacy and current teams
• Providing evidence for regulatory compliance requirements

In an era where failed modernization projects can cost organizations millions and derail strategic initiatives, Zengines' superior data lineage capabilities provide the foundation for successful transformations.

Learn more about how Zengines can accelerate and strengthen your modernization initiatives

While surface-level data lineage might satisfy basic research requirements, truly successful modernization demands the depth and precision that only Zengines delivers. By revealing not just what data exists but exactly how it's processed, transformed, and utilized, Zengines provides the comprehensive understanding needed to navigate the complex journey from legacy mainframes to new platforms.

‍

Do you know exactly where your critical data originates, how it transforms, and where it travels throughout your organization?

We're excited to share that our CEO, Caitlyn Truong, was recently a guest on The Lights On Data Show with host George Firican, revealing why data lineage is such a critical, yet often overlooked, aspect of data management. 

What you'll discover in this episode

• The true definition of data lineage and why it's a critical component for regulatory compliance, transparency, and operational efficiency
• 5 Essential Steps for Successful Data Lineage Implementation:
  • Be clear on use case and scope
  • Have a guiding strategy and design
  • Employ automation tools
  • Start with small focused use cases
  • Celebrate success and expand
• How to overcome "black box" legacy system challenges by leveraging tools like Zengines to capture metadata, understand data patterns, and parse code bases to reverse engineer business context

Why this matters

Caitlyn shares how organizations are increasingly prioritizing data lineage tools to address mainframe management and mainframe modernization projects.

The drivers behind these initiatives include:

• Scarcity of resources who understand legacy systems and code (like COBOL)
• Growing regulatory requirements for transparency
• Critical operational risk reduction

At Zengines, we're helping organizations better understand their mainframes and complex data journeys with AI-powered tools that help business users visualize, research, and discover data lineage components to answer business questions. 

Listen to the full episode

‍

Many software companies and service providers either survive or thrive based on their ability to scale customer onboarding. But for many, one step consistently stalls momentum: data migration.

The excitement of closing a new deal quickly fades when teams realize how complex and messy transferring customer data into a new system can be. And for customers? A painful onboarding experience with errors, delays, or unexpected work often becomes their first impression of your platform.

At Zengines, we’ve built a better way to migrate data and onboard customers.

The Hidden Cost of Poor Data Migration

Every vendor has felt it: a new customer signs on, and the first real ask is, “Please provide your data in our format.” From there, it’s a guessing game. The customer takes a first pass. It fails. They try again. Still wrong. Eventually, they bring in an expert or third-party firm to bridge the gap — adding time, cost, and frustration for everyone involved.

Some of the most common challenges we see include:

• Inconsistent source formats: Customers often hand over overloaded spreadsheets or fragmented exports that don’t match the vendor’s data model.
• Lack of standardization: What one customer calls “Client ID,” another may call “Customer Number.” Field names, structures, and data types are almost never aligned.
• Complex transformation requirements: Names need to be split into first and last. Text needs to become numeric. Fields need to be concatenated, truncated, or translated.
• No visibility into data quality: Null values, inconsistent formatting, or invalid field types create downstream failures — often caught only after the fact.
• Inefficient manual workflows: Multiple iterations between the vendor and the customer drag out timelines, impact revenue recognition, and lead to customer dissatisfaction.

And perhaps most importantly, these challenges hit vendors where it hurts — in the form of higher onboarding costs, delayed time to value, and increased churn risk.

The Zengines Approach: Frictionless Data Conversions

Zengines was built to simplify and accelerate this process. 

Our platform is designed to help vendors accelerate customer onboarding by making data migration faster, more accurate, and dramatically less painful — for both the vendor and the customer.

Here’s how it works:

AI-Powered Schema Mapping

Forget the days of side-by-side spreadsheets and trial-and-error mapping. With Zengines, our AI automatically compares your source and target schemas and predicts the best field matches. You get a fast, intelligent starting point — then validate or adjust as needed.

Whether you’re dealing with flat files, legacy exports, or structured CRM tables, Zengines bridges the gap with speed and precision.

AI-Assisted Transformations

Need to split full names into first and last? Convert “TX” to “Texas”? Strip out characters from phone numbers or coerce string values into numerics?

Just describe the transformation you want in plain English — and Zengines will auto-generate the rule. You can test it, tweak it, and apply it — all without writing code or calling in a data engineer.

Data Profiling & Cleansing

Our platform automatically profiles incoming data so you can spot hygiene issues, inconsistent formats, or type mismatches before they derail your project. We highlight anomalies, nulls, or outliers — and help you fix them with either pre-built rules or AI-powered suggestions.

Think of it as a project manager’s dream: instant visibility into the size, scope, and quality of your incoming data.

Instant Load File Generation

Once your mappings and transformations are set, Zengines generates clean, validated load files — instantly. No waiting on test environments, no bouncing between systems. You can preview and approve the file directly in our interface and push it into your target system with confidence.

Data Lineage for Legacy Systems

Migrating from legacy platforms like mainframes? Our Data Lineage module lets teams decode black-box systems (like COBOL modules or JCL job control files) and trace how data is computed, transformed, and passed across systems.

Whether you’re reverse-engineering business logic or preparing for full-scale modernization, Zengines helps you understand your legacy data so you can move forward with clarity.

Why vendors love Zengines

We didn’t just build a better data migration tool. We built a scalable customer onboarding accelerator.

Vendors love Zengines because:

• It reduces onboarding time from months to days or even hours
• It slashes reliance on expensive service teams or consultants
• It improves implementation success rates and customer satisfaction
• It accelerates time-to-revenue by getting customers live faster
• It scales with your business — whether you onboard 10 or 1,000 customers a year

Let’s Make Data Migration a Growth Lever — Not a Bottleneck

Data migration doesn’t have to be the painful part of your onboarding process. With Zengines, it becomes your superpower.

You can go live faster. Delight customers from day one. And scale your implementations without scaling your services team.

Schedule a demo and see how Zengines can transform your customer onboarding.

For decades, mainframes and midrange systems like the AS/400 have been the quiet workhorses behind the world’s largest and most complex organizations. They’ve powered financial transactions, supply chains, payroll, insurance claims, and more, with unmatched reliability and scale.

But companies are under pressure to modernize as the demand for agility, cloud-native architectures, and modern user experiences grows. And yet—many are stuck.

Why? In short, because migrating off legacy platforms isn’t just a tech project; it’s an organizational, architectural, and operational challenge that touches every corner of the business.

This article will explore the technical challenges in greater detail and highlight how the Zengines platform helps organizations accelerate their mainframe modernization and migration projects. 

Critical technology issues stalling mainframe modernization and migration

Millions of lines of mystery code

Legacy systems often contain decades' worth of COBOL or RPG code, woven with custom logic and patched over time. Documentation is minimal (or missing), and it’s not unusual for a single change to trigger a ripple effect across dozens of interconnected programs.

It’s like trying to renovate a skyscraper with no blueprints—and no one left who remembers what’s behind the walls.

A shrinking talent pool

The people who built and maintained these systems are retiring or already gone. And newer developers? They’re trained in modern stacks, not in COBOL, RPG, or green-screen systems.

This talent gap creates operational risk. Who’s going to maintain these systems while you plan your migration?

Everything’s interconnected

Mainframes and midrange systems often interface with a sprawling web of other applications, including ERPs, CRMs, data warehouses, and external partners.

These systems communicate through legacy protocols like JCL, FTP, and EDI — and those connections are often undocumented or poorly understood (the mystery code we mentioned above).

High cost, higher stakes

Migration is expensive and risky. A failed migration can cause severe operational disruptions and customer fallout. That’s why even companies that want to modernize often delay or shelve the project.

Security and compliance risks

Moving sensitive data to a new platform means ensuring compliance with regulations like GDPR, HIPAA, and SOX. Ironically, legacy systems are often secure by virtue of being siloed and difficult to access—something that changes in a cloud or hybrid environment.

“If it ain’t broke…”

Mainframes are stable. They run 24/7. They rarely crash. For many companies, that’s enough reason to stay the course—until it isn’t.

Stability can breed inertia, and inertia can become a competitive disadvantage for your company.

No One-Size-Fits-All Roadmap

Should you rehost to the cloud? Refactor the codebase? Replace the system altogether?

Each path comes with trade-offs, and companies risk embarking on change management blindly without a clear view of what’s behind the scenes.

Shedding light on the Black Box with Zengines

This is where Zengines comes in.

Zengines Data Lineage is a research and discovery platform built specifically for mainframe modernization, migration, and management. It ingests and parses COBOL code, JCL, SQL queries, and application schemas to illuminate the inner workings of your legacy systems.

With Zengines, you can:

• Visualize data lineage—see exactly where each data element originates, how it's transformed, and where it's used
  
• Map relationships across programs, fields, tables, files, and variables
  
• Identify embedded business logic, hard-coded values, conditional branching, and more
  
• Inventory and profile your entire mainframe data environment
  

In short: Zengines turns your mainframe from a black box into a blueprint.

It’s the tool that makes migration possible, not just aspirational. When you finally understand your legacy systems, you can make smart, safe, strategic decisions about what to do next.

Ready to learn more?

Modernization is hard—but it’s harder when you don’t know what you’re working with.

If you’re exploring a mainframe or AS/400 migration, Zengines can provide the clarity you need to move forward confidently.

Reach out to schedule a demo or learn more about how Zengines supports data-driven modernization.

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?

BOSTON, MA / ACCESSWIRE / November 7, 2024 / Zengines, a leading provider of AI-based data management software, today announced that it has closed its oversubscribed Series Seed Preferred round of financing, led by Hyde Park Venture Partners. The new capital will be used to further accelerate the company's product development and market growth.

This announcement follows the recent Fall 2024 release of the company's end-to-end Data Migration platform and the introduction of a new Data Lineage solution.

"We are excited to enter this new phase of growth and are grateful for the opportunity to innovate with leading global enterprises," said Caitlyn Truong, Zengines co-founder and CEO. "Our customers rely on Zengines to reduce the time, cost, and risk associated with migrating data to new systems."

The Zengines Data Migration platform has three major components:

• The Zengines Analyzer identifies data requirements for new target systems through intelligent discovery and analysis of the sources of data.
• The Zengines Mapper applies AI and machine learning to generate field and pick-list matches and transforms the data according to compliance, quality, system, and business requirements.
• The Zengines Loader moves the transformed data to the target systems directly or via load files and tests and validates the converted data.

Customers benefit because the three platform components are fully integrated and share and learn from the Active Metadata captured in one knowledge source.

"We designed the Zengines platform to help our customers during every step of the end-to-end data migration process," said Carl Drisko, co-founder and CTO. "Our new Data Lineage solution helps enterprises understand where their data comes from and the logic that was used to create it. It accelerates Mainframe Modernization and supports other Data Governance, Data Design, and Data Quality initiatives."

The Zengines Data Lineage solution traces data from databases or files through complex, multi-step processes and shows exactly where it originated and how and why it reached its transformed state. The software analyzes jobs, schedules, scripts, and COBOL source code and produces high level logic flows as well as field level logic views.

"Zengines continues to make great progress using AI technology to solve some of the industry's most challenging data migration and data management problems," said Greg Barnes, Partner at Hyde Park Venture Partners. "We were excited to lead this financing and look forward to the next phase of growth."

About Zengines

Zengines has created an entirely new way of converting data. The Zengines AI platform automates the end-to-end data migration process, reducing the time, cost, and risk associated with data conversions, system migrations, and data onboarding for a variety of business solutions. Zengines is used by leading enterprises, software vendors, and consultants across multiple industries.

Please visit zengines.ai

For additional information, please contact:

Todd Stone
Zengines President
todd@zengines.ai

‍

‍

We are thrilled to announce that Zengines has been selected as one of only five startups nationwide to participate in Ally Financial's inaugural Innovation Challenge focused on Responsible AI. This recognition highlights our commitment to developing AI solutions that make data conversions frictionless while maintaining the highest standards of responsibility and security.

About the Ally Innovation Challenge

The Ally Innovation Challenge was launched in May 2025 as an Open Innovation program designed to foster learning between Ally Financial and startups that prioritize deploying AI responsibly in a safe and secure manner. The selection process was highly competitive, with less than 5% of applicants chosen to participate in this 8-week mentorship program.

Our Experience

As part of this prestigious program, the Zengines team had the opportunity to engage with Ally's AI & Technology, Risk, Security, and Supply Chain units, alongside technologists supporting all of Ally's underlying businesses. Through interactive mentoring sessions, we shared knowledge, presented our solutions to a tech-focused audience, and benefited tremendously from the expertise and feedback provided.

Why This Matters

This recognition validates our mission to help companies adopt the latest and best technologies seamlessly through frictionless data conversions. In the financial services sector, where data security and responsible AI implementation are paramount, our solutions address critical challenges that organizations face during technology transitions.

Our CEO, Caitlyn Truong, commented:

"Being selected for the Ally Innovation Challenge is a testament to our team's dedication to solving complex data migration challenges with AI while maintaining the highest standards of responsibility. The insights gained through our interactions with Ally's experts have been invaluable in refining our approach to responsible AI implementation."

About Zengines

Founded in July 2020, Zengines is an AI tech company with a vision of frictionless data conversions. Our solutions help organizations accelerate technology transformations by solving data conversion challenges with AI, reducing time, cost, and risk associated with data migrations.

Our technology allows business analysts to map, change, and move data when new data sources or systems come into the picture. Zengines is powered by AI to cover all critical steps of migrating data, including handling legacy mainframes and home-grown "black box" systems.

For more information about how Zengines can help your organization with data migration and mainframe data lineage, contact our team today.

As a fellow SaaS CEO, I understand that building and delivering a positive reputation and brand drives revenue. On the surface, this sounds like a simple and relatively straightforward task: provide a good product, take care of your customers, and deliver on the promise you made to customers. What I discovered as an executive at Accenture is that no matter how capable your SaaS product is, if you can’t effectively and efficiently onboard a customer, the reputation and revenue from your product are at significant risk. This is why I co-founded Zengines.

The Onboarding Devil is in the Data Conversion Details

Data conversion is rarely the first concern for a CEO, but understanding how this seemingly “in the weeds detail” is a strategic risk for your revenue is the purpose of this blog. We conducted a survey, and two major items jumped out:

1. 68% of customers say converting data from old systems is the #1 challenge for moving to new platforms.
2. Over 90% said they are converting/consolidating data from multiple systems when they deploy a major new SaaS platform.

The “so what” is your reputation and revenue are at risk when you (or your partner) do not successfully onboard your customer’s required data to your SaaS platform to deliver the expected value to your customers. Three fundamental issues complicate the task of data conversion:

1. Manual guesswork: Data from multiple systems must be mapped and aligned in format or structure to be directly imported to your SaaS platform, this is manual, slow, and laborious guesswork.
   
2. Lack of experienced resources, especially when you need them: The skill sets and people required to accomplish this critical task are rare resources and are only scalable with some automation assistance.
   
3. Lack of repeatability: Because data conversions are a detail-oriented task requiring contextual understanding, onboarding teams will recreate the wheel each time.

Why Data Conversion is a Good Task For AI

We started using AI at Zengines long before AI became the year's buzzword. Pattern recognition and anomaly identification are at the heart of the data conversion process, which is ideal for an AI-based tool. Zengines AI helps you accelerate and scale onboarding, whether your professional services teams, a Big Four consulting firm, or another partner is implementing your platform. Our system automates the data mapping and transformation rules, identifies anomalies, lets the experts confirm and augment the transformation changes required, and executes the data conversion to create a successful onboarding process.

Data Conversion Drives Revenue Generation

Every legacy platform integrated into your platform is more revenue for your company. This can’t happen without the ability to quickly understand, move, and convert your customer’s data. Whether you get paid by the number of users (Salesforce.com), the number of transactions (Zapier), the number of modules implemented (HubSpot), or the amount of data you manage (Splunk), your revenue growth is limited if the data in legacy systems is not converted to your platform. This is why data conversion drives revenue generation.

Zengines AI-Driven Data Conversion to Accelerate Onboarding

Zengines helps your company deliver your value faster by accelerating onboarding, optimizing your professional services teams, and adding repeatability to the process.

• Accelerate Time to Value - Zengines is an end-to-end data conversion solution that gets the job of data conversion done so customers get to value faster.
  
• AI-based Data Conversion is 6X Faster - Zengines AI-based automation is 6X faster than traditional CSV file-based data conversion methods.
  
• Scale Your Limited Resources – Zengines makes data conversion a highly repeatable process to scale your team and optimize resources.
  
• Optimize and Automate Onboarding – Zengines AI automates the data conversion, including profiling, mapping, loading, and testing data conversion to optimize onboarding.

Get started with Zengines

As CEO, your priority is delivering value to your customer in the best way to enhance your company’s reputation and top- and bottom-line. In that light, this is question you should be asking when it comes to data conversions: is your customer onboarding fast, and repeatable?  Zengines is here to achieve that with you.

System migration is a necessary skill that modern business operations teams must master. Digital transformation programs are moving fast and require organizations to keep up with the evolving technological landscape. While migrating to a new system can offer numerous benefits such as revenue growth, increased efficiency, and adaptability, the process is challenging and requires significant staff time - often augmented with system migration consultants. This article will explore the top five tips business teams can use to ensure success for their system migration programs.

Tip 1. Gain user buy-in on how the new system will change their business processes

One of the most significant steps in a system migration project is a comprehensive understanding of the new software's requirements and how your business users may need to change how they work. The impact of changes will likely radiate beyond the new system you are implementing as data flows to downstream products and processes. Users need to see how the new system represents their data. Seeing familiar data within the new system helps with the configuration of screens and business needs. The following steps can help organizations address this challenge:

• Conduct a thorough analysis of the new system, including its technical capabilities, limitations, and integration requirements.
• Engage stakeholders from different departments to understand their specific needs and expectations.
• Develop a detailed migration plan that outlines the steps required to meet the new system's requirements, including data conversion, training, and process redesign.
• Setting expectations for timelines, reporting changes, compliance concerns, third-party risk management, process changes, staff training, and delivering on expected ROI (return on investment).

PRO TIP: Loading a first cut of data into the new system early can positively influence the buy-in from business users while helping them experience the new system with familiar data. Loading data early in a system migration will help with configuration and provide early visibility to owners of downstream systems and processes.

Tip 2. Efficient Field and Data Mapping for Seamless System Migration

The next step in the system migration is mapping the fields and their data from the original system to the new one. This process is not always straightforward and requires both the source and destination systems and the outcomes to ensure the project meets expectations. To streamline this step in the process:

• Create a comprehensive mapping document that outlines the relationships between fields and data elements in the original and new systems.
• Leverage advanced data conversion tools and methodologies to automate the mapping process, reducing manual effort and minimizing the risk of errors.
• Validate the mapping by conducting extensive testing and subject matter expert reviews to ensure accuracy and alignment with project expectations

PRO TIP: Data conversion platforms, like Zengines, dramatically speed up matching fields from source to target by applying artificial intelligence to solve the problem quickly and accurately.

‍

Tip 3. Seamlessly Transform Data to Meet New System Requirements

Data transformation is a critical aspect of system migration, as the data from the original system may not be directly compatible with the new system. This challenge can be addressed by:

• Identifying the specific data transformation requirements, such as changes in data formats, units, or encoding.
• Safeguard compliance with reporting, regulatory, and process obligations, such as historical reporting, GDPR, TPRM (Third Party Risk Management), and executive reporting, using our data migration software. Optimize efficiency by developing and implementing data transformation scripts or leveraging cutting-edge data conversion tools, automating the process for swift and accurate migration. Prioritize data integrity by conducting rigorous testing and validation procedures, guaranteeing the accuracy and integrity of the transformed data.

PRO TIP: Transforming data has advanced significantly with data science toolkits and Python. End-to-end data conversion platforms will help automate this process with pre-configured business rules and algorithms.

Tip 4. Testing the Accuracy of Data Conversion

Ensuring your data conversion is accurate is vital for successful system migration. Inaccurate data can cause all sorts of headaches, from operational problems to unhappy users and even potential penalties from regulators. To ensure your reporting and operations meet expectations, follow these essential steps:

• Create a thorough test plan covering a range of scenarios, spanning data integrity checks, functional testing, and end-to-end testing.
• Optimize your testing process for data migration software by harnessing the power of automated testing tools and cutting-edge techniques.
• Establish an effective feedback loop with critical stakeholders to identify and resolve data conversion issues promptly.
• Select relevant stakeholders to validate jobs, processes, reports, and API feeds/links to enable end-to-end operations.

PRO TIP: Don't cheat on double-checking your data conversion; it's vital to avoid headaches down the line. Create a solid test plan covering all bases using automated testing tools for efficiency and engaging your stakeholders for quicker feedback. By getting the right folks involved in validating everything, you'll have all bases covered, ensuring a smooth end-to-end conversion for your operations.

Tip 5. Managing Change and Ensuring User Adoption

The success of a system migration hinges on users' enthusiastic adoption and seamless integration of the new system. Achieving success in system migration relies on effective change management strategies and user adoption. To fulfill these crucial requirements, it is imperative to:

• Develop a change management plan that includes communication, training, and support strategies to help users transition to the new system.
• Facilitate learning by providing comprehensive training and resources like user guides and video tutorials.
• Establish a robust support structure featuring responsive help desks and user forums, providing prompt assistance and addressing user concerns throughout the migration process.
• Ensure executives and key stakeholders can monitor progress and your proactive communications.

PRO TIP: Foster a positive attitude towards change and user adoption by highlighting the benefits and addressing potential concerns through frequent engagement and positive interactions in your communication and training materials, helping users feel more comfortable and excited about the transition.

Conclusion

System migration is a complex and challenging process that requires careful planning, execution, communications, and management. By understanding the requirements of the new system, effectively mapping fields, seamlessly transforming data, quickly testing the accuracy of data conversion, and adeptly managing change, organizations can successfully migrate to a new system. By doing so, they can unlock the value of their digital transformation programs and drive their organizations to higher levels of growth and effectiveness.

‍

Data conversions are far too manual

Companies have moved their data from old to new since the first computer system was retired. Enterprise software has advanced a lot since then, but the process for converting data hasn’t. IT teams and consultants still rely on essential tools like spreadsheets, scripts, and SQL queries. These are labor-intensive and require high-level expertise, extending the timeline for onboarding new applications. This fragmented approach to data conversion is time-consuming and inefficient, resulting in too many projects delivered late and over budget.

Zengines has a better way of converting data with a platform that automates the entire process from end to end. With Zengines, data conversion efforts are faster and less labor-intensive resulting in reduced costs and risk.

‍

AI-powered data conversion rapidly accelerates data analysis, mapping, and testing

Companies migrating to new systems often need more expertise in the new target system. In contrast, software vendors for the new system tend to shift the responsibility for data conversion onto the data owners. This creates a data conversion gap that is challenging to navigate. Questions that customers of new software often ask include:

·       How long will it take to convert the data?

·       Will there be a gap in the data compatibility and completeness between the two systems?

·       Is my source data sufficient to populate the target system

·       Will I be able to achieve the business goals we are seeking?

·       Do I need to enrich my data from other sources?

·       Can my target store all of my current data?

With Zengines, a data conversion project never starts from scratch. Our AI algorithms understand the requirements of the target system. They will automatically analyze the source system's table structures, relationships, and data to automate the data conversion process. The result is a tremendous head start with any data conversion projects and a better understanding of what is required to achieve your business objectives.

How Zengines uses artificial intelligence to accelerate data conversions

1. Analyzer: Connect to 45 different databases or ingest multi-format data sets from CSV, Excel, XML, PDF, or APIs
2. Analyzer: Classify tables and fields. Automatically build relationships based on primary-key/foreign-key relationships.
3. Analyzer: Identify quality issues within source data.
4. Mapper: Identify source-to-target field matching.
5. Mapper: Detect and resolve data type differences.
6. Mapper: Produce field-level logic where data needs to be transformed to meet the requirements of the new target system.
7. Loader: Integrated Testing and Validation

Zengines ML models continually improve with each new conversion, offering better results with each data conversion project.

Automating the data conversion process with the Zengines platform

Zengines provides an end-to-end platform, digitizing and streamlining each step of the data conversion process. This integrated approach simplifies the user experience, with inputs and outputs for each step in the process, and they seamlessly flow from step to step until all data is converted. Four technologies underpin the flow of data throughout the process:

1. Zengines Data Catalog^TM – System migration projects need up-to-date, accurate metadata to identify critical patterns between the source and target systems. Zengines’ intelligent data catalog is rapidly constructed from all source and target system schemas and metadata generated by Zengines Analyzer, providing the foundation for data conversion analysis.‍
2. Zengines Knowledge Graph^TM – The Zengines Knowledge Graph is the repository for training our powerful data conversion models. Our patent-pending technology uniquely builds domain-specific models for each industry and commercial software platform we have worked with. Customers benefit from prior data conversion efforts, making the algorithms smarter over time.‍
3. Data Conversion Pipeline – On the Zengines platform, data flows through an integrated process: ingestion à cleansing à transformation à post-processing à loading of data, à reconciliation. Our Data Conversion Pipeline controls the flow of this data as it is prepared for the target system and enables rapid iterative cycles through the pipeline to refine outputs continuously.‍
4. Collaboration and Control – System migration efforts involve many teams, people, and environments, such as Development, Testing, User Acceptance Testing, and Production. Hundreds or even thousands of decisions need to be made and tracked during these projects. Zengines Collaboration and Control module ensures that project users and resources stay organized and can operate from a common source of truth.

Why we load data fast, early and often

Companies migrating to new systems often need more expertise in the target system, while vendors tend to shift the data conversion responsibility onto the data owners. This creates a data gap that is challenging to navigate. To address this issue, it's crucial to adopt a target system-focused approach, starting with the new system's requirements in mind.

Target and downstream system vendors, product owners, and users eagerly wait to see their data within the new system resulting in project delays and waste. Zengines believes in loading data quickly, even if it's not initially perfect, to allow for iterative improvements in data completeness and quality and to drive overall project efficiencies.

Conclusion

Zengines offers an entirely new way of converting data. With an emphasis on speed and repeatability, Zengines combines artificial intelligence and automation to transform manual and time-consuming data conversions into an efficient and predictable process.

‍

CHICAGO, March 27, 2023 -- Zengines, the first AI-powered data conversion platform, announced today the closing of $5M in funding led by Hyde Park Venture Partners with participation from Hyde Park Angels. The funding will be used to expand product, engineering, and go-to-market capabilities.  

When organizations migrate data to new applications, systems or outsourced services, Zengines automates the end-to-end data conversion process. This reduces the risk, time and cost associated with implementing new technologies.

‍

“Organizations and their systems are in constant change,” said Caitlyn Truong, Zengines co-founder and CEO. “It’s estimated that companies spend more than $100B per year converting their data to new systems. The problem is messy and complex and limits an organization’s ability to execute their business and technology strategy. With Zengines, there’s a better way.”

‍

The Zengines data conversion platform has three major components.

1. Analyzer. Identify data requirements for new target systems and analyze the data and schemas of the source systems that are being replaced.  

2. Mapper. Generate field and code list matchings, perform needed data transformations, quality checks, and business rules.

3. Loader. Load the target systems with the transformed data directly through load files or APIs. Test and validate that the conversion was completed correctly.

‍

“Data conversion efforts still rely on spreadsheets to map the fields and custom code to do the heavy lifting,” said Carl Drisko, Zengines co-founder and CTO. “Zengines has developed industry-specific AI models that dramatically improve the speed at which data is analyzed, matched, transformed, and loaded into new target systems.”

‍

“We’re thrilled to be working with the Zengines team,” said Greg Barnes, Partner at Hyde Park Venture Partners. “The founding team at Zengines is a group of industry experts solving one of the most painful problems facing all enterprises; successfully and efficiently converting critical data to new systems.”

About Zengines

Zengines has created an entirely new way of converting data. The Zengines AI platform automates the end-to-end data conversion process, reducing the time, cost and risk associated with data conversions, system migrations and data onboarding for a variety of business solutions. Zengines is used by leading enterprises, software vendors and consultants across multiple industries.