How Zengines is Filling Critical Gaps in Mainframe MSP Toolkits

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

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

The Critical Gaps in Current MSP Toolkits

Gap 1: Business Rules Discovery

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

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

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

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

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

Gap 2: Data Migration Tools

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

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

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

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

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

Gap 3: Testing and Validation Blind Spots

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

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

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

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

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

How Zengines Fills These Critical Gaps

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

Zengines Mainframe Data Lineage: Bridging the Business Logic Intelligence Gap

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

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

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

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

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

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

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

Comprehensive Testing and Validation: Closing the Validation Loop

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

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

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

The Integrated Zengines Advantage

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

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

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

The Business Impact: Why MSPs are Choosing Zengines

Accelerated Project Delivery

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

Improved Profit Margins

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

Enhanced Client Experience

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

Competitive Differentiation

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

Risk Mitigation

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

The Strategic Imperative

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

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

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

‍

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.