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?
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Data lineage is the process of tracking data usage within your organization. This includes how data originates, how it is transformed, how it is calculated, its movement between different systems, and ultimately how it is utilized in applications, reporting, analysis, and decision-making. This is a crucial capability for any modern ecosystem, as the amount of data businesses generate and store increases every year.
As of 2024, 64% of organizations manage at least one petabyte of data — and 41% have at least 500 petabytes of information within their systems. In many industries, like banking and insurance, this includes legacy data that spans not just systems but eras of technology.
As the data volume grows, so does the need to aid the business with trust in access to that data. Thus, it is important for companies to invest in data lineage initiatives to improve data governance, quality, and transparency. If you’re shopping for a data lineage tool, there are many cutting-edge options. The cloud-based Zengines platform uses an innovative artificial intelligence-powered model that includes data lineage capabilities to support clean, consistent, and well-organized data.
Whether you go with Zengines or something else, though, it’s important to be strategic in your decision-making. Here is a step-by-step process to help you choose the best data lineage tools for your organization’s needs.
Understanding Data Lineage Tool Requirements
Start by ensuring your selection team has a thorough understanding of not just data lineage as a concept but also the requirements that your particular data lineage tools must have.
First, consider core data lineage tool functionalities that every company needs. For example, you want to be able to access a clear visualization of the relationship between complex data across programs and systems at a glance. Impact analysis also provides a clear picture of how change will influence your current data system.
In addition, review technology-specific data-lineage needs, such as the need to ingest legacy codebases like COBOL. Compliance and regulatory requirements vary from one industry to the next, too. They also change often. Make sure you’re aware of both business operations needs and what is expected of the business from a compliance and legal perspective.
Also, consider future growth. Can the tool you select support the data as you scale? Don’t hamstring momentum down the road by short-changing your data lineage capabilities in the present.
Key Features to Look for in Data Lineage Tools
When you begin to review specific data lineage tools, you want to know what features to prioritize. Here are six key areas to focus on:
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
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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:
- Business analyst discovers the discrepancy
- Tickets get raised to investigate the difference
- Someone (usually an expensive SME) digs through legacy code
- Weeks or months pass while they navigate thousands of lines of COBOL
- Finally, an answer emerges—but it spawns three new questions
- 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.
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