Same Mission, Bold New Look: Zengines' Visual Identity Evolution

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.

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.