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Introduction
IBM’s Information Management System (IMS) is a long-standing hierarchical database and transaction system first introduced in 1968. IMS remains widely used in many Fortune 1000 companies, yet a growing number of organizations are looking to migrate their IMS databases to modern relational databases (like IBM Db2) in order to leverage standard SQL and tap into a larger pool of database talent
. Migrating from IMS (a hierarchical model) to a relational database is often a critical part of legacy modernization, enabling better integration with contemporary applications and analytics, improved flexibility, and ease of maintenance. However, such a migration is a significant undertaking with many hazards. Organizations often worry about “breaking something that isn’t broken,” akin to replacing an old but working plumbing system – the fear is that ripping out the old system could introduce new problems
precisely.com. Indeed, IMS applications tend to be highly complex and deeply embedded in business processes developed over decades, so migrating to relational technology carries risks in data integrity, system performance, and business continuity if not done properly.
This paper provides a comprehensive analysis of the hazards involved in an IMS-to-relational database migration and how to mitigate them. We begin with background on IMS hierarchical databases versus relational databases, explaining key differences and why organizations undertake such migrations. Next, we delve into the major hazards in the migration process, including data mapping challenges, performance issues, application compatibility hurdles, cost/time overruns, personnel and training needs, and cutover risks. We then examine real-world case studies of IMS-to-relational migrations, extracting lessons learned from successes and failures. Finally, we discuss best practices and mitigation strategies – from automated tools to phased approaches – to help organizations avoid or minimize these hazards. The goal is to provide a research-backed, structured guide for ensuring a successful IMS-to-relational migration.
Background
IMS Hierarchical Databases vs. Relational Databases: IMS is based on a hierarchical data model, meaning data is organized in a tree-like structure of records and segments. An IMS database record is made up of a hierarchy of segments (similar to “nodes” in a tree) that represent parent-child relationships. Each segment type defines a set of fields (attributes), analogous to how a relational table defines columns
. In an IMS hierarchy, the parent-child link is implicit: a child segment is always stored subordinate to its parent segment, and the path through the hierarchy defines the relationships. By contrast, relational databases (such as Db2, Oracle, or SQL Server) organize data into tables (flat two-dimensional structures of rows and columns) and require explicit relationships defined by keys. In a relational model, primary keys and foreign keys are used to link tables, and joins in queries establish relationships between entities, rather than the physical hierarchical linkage used by IMS
ibm.com. In other words, an IMS segment instance is inherently joined to its parent and children in the hierarchy, whereas in a relational database those associations must be recreated logically via matching key values
Because of these fundamental differences, IMS and relational systems require different approaches to data access. IMS applications typically retrieve data using hierarchical path calls (via DL/I calls) navigating the tree, often retrieving an entire hierarchy of child segments when a parent is fetched. Relational applications use Structured Query Language (SQL) to retrieve data from multiple tables through joins and set-oriented operations. This means that certain data patterns easy to handle in IMS (like reading a whole set of child records under a parent) may require multiple SQL queries or complex joins in a relational system. Conversely, ad-hoc querying and multi-dimensional access are easier in relational systems because any column can be indexed and queried with SQL, whereas IMS usually requires predefined access paths (e.g. primary key or specific segment search arguments).
Why Organizations Migrate from IMS to Relational: There are several motivations for moving away from IMS hierarchical databases to relational platforms. A primary driver is the need to modernize and integrate with newer technologies. IMS, with its hierarchical structure and proprietary access methods, can struggle to meet today’s demands for real-time data access, easy integration with web services, and advanced analytics
zmainframes.com. Relational databases, by virtue of the relational model and SQL, offer greater flexibility for integrating with modern applications, business intelligence tools, and data warehouses. Another key factor is talent availability and skills: as veteran IMS developers and DBAs retire, companies find it harder to hire people with IMS expertise. In contrast, skills in relational databases (and SQL) are far more common in the labor pool
precisely.com. Migrating to a relational system like Db2 can therefore alleviate the growing skills gap and reduce dependence on niche legacy knowledge. A related point is that maintaining IMS environments can be costly and complex – IMS often requires specialized skillsets and third-party tools, and the licensing and support costs for mainframe IMS can be high
zmainframes.com. By consolidating onto a relational platform (especially if the organization already uses relational databases for other applications), companies can reduce operational costs (for example, eliminating IMS-specific licensing and tools) and streamline their technology stack
There are also business agility reasons. Relational databases support faster application development in many cases: using SQL and modern 4GL or object-oriented languages can accelerate new features compared to the older record-at-a-time IMS programming model
zmainframes.com. For instance, IMS applications are often written in COBOL or PL/I with embedded DL/I calls, which operate at a low level of abstraction; switching to a relational back-end allows use of contemporary frameworks and easier data access for new applications. Additionally, relational systems are generally more “open” and extensible – they can run on a variety of platforms (including distributed and cloud environments) and interface readily with standard tools. Many organizations also pursue IMS-to-relational migration as part of a larger cloud or digital transformation initiative, seeking to replatform away from the mainframe or to enable new capabilities (such as real-time analytics on formerly siloed IMS data)
That said, migrating away from IMS is not undertaken lightly. Companies often maintain IMS for critical systems (financial transactions, manufacturing control, government records, etc.) because “if it isn’t broken,” they hesitate to fix it
. IMS systems are proven to be extremely reliable and high-performance for the workloads they handle, and the cost and risk of migration can be a major barrier
cmr-journal.org. The decision to migrate typically comes when the long-term benefits (strategic flexibility, lower staffing risk, integration, and sometimes cost savings) outweigh the short-term risks and investment. As we discuss next, those short-term hazards are significant – involving data transformation, performance uncertainties, application changes, project costs, and more. A clear understanding of these hazards is essential before embarking on an IMS-to-relational migration project.
Major Hazards in Migration
Migrating from a hierarchical IMS database to a relational database is a complex project fraught with risks. Here we discuss the major hazard areas in depth:
Data Mapping and Transformation Challenges
One of the most fundamental challenges is mapping IMS’s hierarchical data structures to relational tables. An IMS database often contains deeply nested segments, repeating groups of fields, and sometimes redefined fields (where the same bytes might represent different data under certain conditions). Converting this into a well-structured relational schema is not trivial. Data that was stored as a single hierarchical record may need to be split into multiple relational tables. Each IMS segment type typically becomes one or more tables, and the parent-child links turn into foreign key relationships or separate linkage tables
cmr-journal.org. For example, an IMS segment that had an array of repeating sub-elements (occurs clauses in COBOL) might be represented as a separate child table in a relational design – essentially a form of normalization
virtualusergroups.com. Deciding how to map these repeating groups (“multi-valued fields”) is a hazard: if not normalized properly, the migrated design could violate relational normal forms or perform poorly. On the other hand, over-normalizing without considering how the data is used can complicate the application layer. Striking the right balance requires deep data analysis.
Figure: Example of an IMS hierarchical database structure (a simple company database). “Company” is the root segment, with child segments for Employee information and Project information. Each of those segments contains fields (e.g., Employee segment has Emp Number and Emp Name fields). In a migration to relational, these segments would be converted into separate but related tables (Company, Employee, Project), with foreign keys to preserve the parent-child relationship.
In IMS, relationships between segments are sometimes managed via physical pointers or IMS-specific constructs (like logical parent/child pointers, or secondary indexes) rather than foreign keys. During migration, all these implicit linkages must be converted to explicit keys and indexes in the relational database
cmr-journal.org. If an IMS database wasn’t designed with unique keys on each segment (which is common – many IMS segments don’t have a primary key field), the migration team must invent surrogate keys for the new relational tables
ibm.com. This introduces the risk of data integrity issues: one must ensure that every child record gets correctly linked to the right parent via the new keys. Missing or inconsistent keys can lead to orphaned records or broken relationships that did not exist in IMS (because IMS would enforce the hierarchy during data load).
Another challenge is data type and format conversions. IMS segment fields are often defined by COBOL copybooks or PL/I structures, using data types that may not directly translate to modern relational types. For instance, IMS may store dates as numeric year/month/day in an integer or even as a character string, which might need conversion to a DATE type in the relational system. There could be packed decimal fields, bit flags, or encoded values that require transformation. During the ETL (Extract-Transform-Load) process, careful handling is needed to avoid truncation, rounding errors, or format mismatches. If the IMS data contains deprecated or custom encoding (EBCDIC vs ASCII, etc.), character conversion must also be handled without corrupting the data.
Data quality issues in the legacy IMS data can pose hazards too. Over decades, the data may not conform strictly to relational integrity rules. For example, there could be orphaned child segments that exist due to earlier data loads or application quirks (IMS logically requires a parent for each child, but in practice, logical relationships or manual data fixes might result in some anomalies). When loading into relational tables with foreign key constraints, such anomalies could cause load failures. Thus, a thorough data profiling and cleansing step is usually required as part of the migration to detect and resolve inconsistencies before they wreak havoc on the new system.
Not all of the IMS data model can be converted automatically. Automated migration tools can assist by capturing the IMS metadata (DBD – Database Description, and COBOL layouts) and generating a baseline relational schema, but human design input is crucial. As an academic review on legacy migrations notes, if the IMS record structures are not already in a “normalized” form, automated conversion will fall short and “extra effort using an analysis tool is required to identify common data items and to transform files into normal forms”
cmr-journal.org. In practice, data modelers need to reverse-engineer the IMS schema into a conceptual model, then redesign a target relational schema. This process carries the risk of misinterpretation of legacy data meanings. If subject matter experts (SMEs) who understand the IMS data semantics are not consulted, the new schema might not accurately reflect business rules (e.g., certain codes or flags might be mis-modeled, or optional relationships might be missed). Secure access to experienced IMS SMEs is thus critical – their knowledge of the data and business rules can significantly decrease the risk of mapping errors
virtualusergroups.com. Unfortunately, such experts are becoming harder to find due to outsourcing and retirements
virtualusergroups.com, making this a non-trivial hazard for many organizations.
In summary, data mapping and transformation hazards include: structural mismatches between hierarchical and relational models, dealing with repeating groups and nested segments, creating primary keys for formerly keyless segments, converting pointers to foreign keys, data type and encoding transformations, and potential data quality problems. Each of these must be handled with careful planning, or else the migrated database could be unreliable or functionally incorrect. The effort required just for data conversion is often underestimated – it’s not uncommon for the data analysis and design phases to consume 40% or more of the total migration effort
, underscoring the complexity of this task.
Performance and Scalability Concerns
Another major risk in IMS-to-relational migration lies in system performance and scalability. IMS earned its reputation on the mainframe for extremely high performance, particularly for transaction processing with very large volumes of data. It achieves this by using hierarchical storage and direct pointer access: an IMS application often knows exactly which segment it needs and IMS can navigate the hierarchy or use hashed access (in the case of HDAM databases) to retrieve data with minimal overhead. When converting to a relational database, there is a concern that queries which were lightning-fast in IMS might slow down if not properly optimized in the new system. In fact, it is often expected that some performance degradation will occur if an IMS workload is simply moved “as is” to a relational system
. A well-designed IMS application can outperform a relational database for certain access patterns because IMS pre-joins the data (parent and children stored together) and reads it in a single sequential pass or direct get
db2portal.blogspot.com. By contrast, the equivalent relational operation might involve joining multiple tables, which could mean multiple I/O operations and more CPU overhead for join processing.
Indeed, experience shows that “a well-designed IMS application will perform very fast, perhaps faster than a well-designed Db2 application (but that does not mean that Db2 is slow)”
. The point is that IMS’s design is optimized for its specific data model and access patterns. When migrating, workload characteristics need to be re-evaluated. Some queries or batch processes may run slower on relational unless tuning or redesign is done. For example, IMS can retrieve all child segments of a given type under a parent very efficiently; in relational, one might have to query the child table by parent ID, which if not indexed well or if done repeatedly in a loop (N+1 query problem), can be costly. Thus, there is a hazard of performance bottlenecks if the new schema and queries are not optimized.
Another aspect is CPU and resource usage. IMS databases, being navigational, let the program logic dictate the retrieval path; the programmer can be very efficient in how data is accessed. In a relational database, the DBMS’s SQL engine will determine the access path (index usage, join order, etc.). Db2 and others provide sophisticated optimizers, but this also means more CPU is used to interpret and execute SQL dynamically. One analysis notes that “Db2 will consume more CPU than IMS… This additional CPU brings the benefit of query flexibility, but it is a cost to be aware of”
. In other words, after migration, overall system resource profiles may change – CPU usage might rise for the same workload, which on a mainframe can translate to higher operating cost if not offset by other savings. If the mainframe CPU consumption goes up enough, it could negate some of the cost benefits of migration, so careful performance testing and tuning are needed.
Scalability is also a consideration. IMS can handle massive transaction rates (thousands per second) with sub-second response times, as it was designed for high-volume OLTP. A poorly executed migration that doesn’t account for scaling could result in the new relational system struggling under the same load. Database locking and contention patterns will change as well. IMS uses its own concurrency control mechanisms optimized for the hierarchical access; a relational DB uses SQL locking which might introduce contention if the data model isn’t tuned for the usage patterns. For instance, if a segment that was frequently updated in IMS becomes a row in a highly contended table in the relational design, one could see lock waits or deadlocks that didn’t occur before.
To avoid these pitfalls, it’s important during migration to re-architect with performance in mind. Simply converting each IMS segment to a table one-for-one and loading the data is not enough. Experts recommend to “use Db2 as it was intended to be used… don’t just convert segments to tables and be done. Make sure that you normalize your design and come up with a [proper relational schema]”
. In some cases, denormalizing certain tables or creating additional indexes in the relational system can help recapture IMS-like performance for critical queries. It’s also crucial to conduct performance testing that simulates production load before full cutover. Without that, a major hazard is discovering post-migration that nightly batch jobs or peak hour transactions cannot be processed within required time frames – a potentially business-crippling scenario.
Finally, capacity planning must be revisited. If the target relational database will run on different infrastructure (e.g., distributed servers or cloud) instead of the mainframe, ensuring that the new environment can handle the data volume and throughput is key. The data volume might even expand after conversion: hierarchical databases sometimes store data more compactly (e.g., avoiding repetition of key fields that relational tables might introduce, or using packed formats), so the relational data might consume more storage and I/O. All these performance and scaling concerns mean that migrating IMS to relational is not just a straightforward rehosting; it often requires re-optimization of the application and database design to meet performance requirements. Ignoring this can result in a migrated system that technically works but fails to meet the business SLAs (service level agreements) for response time or throughput.
Application Compatibility and Code Refactoring
Migrating the database layer from IMS to relational has profound implications for the application programs that use the database. IMS applications are typically batch or online programs written in languages like COBOL, PL/I, or assembler, using IMS-specific calls (DL/I calls) to retrieve and manipulate data in the hierarchical database. These calls (e.g., GU – Get Unique, GN – Get Next, ISRT – insert, etc.) are procedural and tied to the IMS data structure. Once the data resides in a relational database, all those IMS calls in the application code must be replaced or handled somehow. This is a major refactoring effort, and one of the highest-risk elements of the migration because it touches the core business logic of the system.
There are generally two approaches: rewrite the application code to use SQL queries against the new relational schema, or use some form of middleware/translation layer that intercepts IMS calls and translates them to SQL (so that the programs require minimal changes). Both approaches carry risks. Rewriting code is time-consuming and error-prone – as one state agency found, “rewriting application code is extremely difficult and extremely risky”
. Every piece of business logic embedded around those IMS calls must be preserved and re-tested. In large legacy systems, it is easy to introduce subtle bugs when making such changes, especially if some IMS calls assumed a certain retrieval order or dataset state that doesn’t directly map to SQL semantics.
On the other hand, using a bridge or emulation layer (for example, IBM’s DL/2 or other vendor tools) can minimize code changes by providing a faux-IMS interface that underneath retrieves data from the new relational database. Tools like Syncsort’s DL/2 have been used in some migrations to “migrate IMS segments to Db2 tables without making any application changes”, effectively replacing the IMS database engine with a translation layer
slideshare.net. While this approach greatly reduces immediate refactoring, it has its own hazards: the translation layer must be robust and high-performance. If the tool has any limitations, certain complex IMS calls or proprietary behaviors might not translate perfectly, potentially leading to functional discrepancies. Additionally, reliance on such a layer might postpone necessary application reengineering – it buys time, but eventually organizations often want to rewrite applications to natively use relational features.
If the choice is to rewrite or heavily modify the application code for relational, the development and testing effort is enormous. IMS programs use record-at-a-time logic, meaning code is often structured to process one segment at a time in a loop, whereas SQL encourages set-oriented operations. The application developers may need to change their programming paradigm. Studies in legacy migration have noted that programs written for non-relational databases “typically use logic that is record oriented” and thus “usually must be reverse engineered and rewritten to operate in a relational environment”
. This reverse engineering entails examining how each program uses the IMS data (which segments it reads or updates, in what order) and then determining the equivalent SQL transactions. It’s not simply find-and-replace DL/I calls with SQL – often the entire flow of the program needs refactoring. For example, a COBOL program that does a series of GN (get next) calls to traverse child segments might be replaced by a single JOIN query that retrieves all needed children at once, followed by iteration in memory. Ensuring that the new code yields the same results as the old code in all cases requires thorough testing and validation.
There is also a risk in embedded business rules. Many IMS applications have business logic interwoven with data access. The code might, for instance, navigate to a certain segment and, if not found, apply some default processing. When switching to SQL, where data access is abstracted, there is a chance that the logic needs to be adjusted (e.g., checking for an empty result set from a query instead of an IMS status code). If any such rule is overlooked, the new application might behave incorrectly. This is why subject matter experts and original developers (if available) are invaluable during code migration – they know the intent behind the code and can help catch any discrepancies.
The scale of application changes can be massive. A case in point: the insurance company Provinzial’s migration involved over 16,500 COBOL programs with 25 million lines of code that had to be handled
delta-software.com. They determined that a fully automated solution was needed to convert the programs due to the sheer volume and complexity
delta-software.com. Automation in code conversion (using code translation tools or code generators) can mitigate risk, but those tools must be carefully verified. If a code conversion tool misinterprets a scenario, it could introduce a systematic error across many programs.
In summary, the hazards around application compatibility include: extensive code refactoring or translation, potential introduction of bugs in business logic, high testing effort, and dependency on specialized tools or middleware to bridge IMS calls to SQL. The organization must plan for a long period of parallel testing where both IMS and the new system are run (or at least the outputs are compared) to ensure the applications still produce correct results. Without such validation, there’s a risk that critical business processes could fail after cutover due to an application error that slipped through. It’s noteworthy that some organizations choose to stage the migration (first migrate data to relational while still using IMS calls via a bridge, then gradually rewrite applications to use SQL natively)
. This phased approach to application migration can reduce immediate risk by not forcing a big-bang rewrite, but it requires the capability to operate in a hybrid mode during the transition.
Cost and Time Management
Cost and time overruns are common hazards in any large IT migration, and IMS-to-relational projects are no exception. Migration projects tend to be lengthy and resource-intensive, sometimes spanning years for complex environments. The risks here include both direct financial costs and the opportunity cost or disruption caused by a prolonged project.
One issue is that during migration, staff and resources are split between keeping the legacy IMS system running and building the new system. The legacy IMS applications usually require 100% availability (especially if they are core to the business), so they must be maintained and even updated with new business requirements while the migration is underway
. This division of focus can strain an IT department. As summarized in one research, “the staff must divide their time between maintenance of existing non-relational systems…and migration projects”
cmr-journal.org. This inherently slows down the migration or risks neglecting the old system. If something breaks in production IMS during the migration, attention is pulled back to firefighting legacy issues, delaying the new development. This hazard can lead to timeline slippage and budget overruns as the project stretches out.
The financial cost of migration tools, new software licenses, and additional hardware can be significant. Standing up a new relational database environment (licenses for the RDBMS, possibly new servers if moving off the mainframe, or new mainframe capacity if adding Db2) is an added cost that runs in parallel to ongoing IMS costs until cutover. As one source notes, “the relational DBMS, migration tools, and additional design and programming are all extra and costly”
. In the interim, the organization is essentially paying for two environments. Furthermore, any investment in the IMS (the sunk cost of existing hardware, software, and optimized processes) is eventually written off – “significant investments in non-relational systems would have to be scrapped” as part of the migration
cmr-journal.org. Senior management might view this as a waste if not convinced of the migration’s long-term ROI, so there is often pressure to contain costs and deliver results quickly.
However, rushing is dangerous. Unrealistic timelines or cutting corners can amplify other hazards (data issues, insufficient testing, etc.). It’s critical to allocate sufficient time for each stage: analysis, design, conversion, testing. Best practice breakdowns suggest that a large portion of effort should go into upfront analysis/design and thorough testing
. If a project tries to go too fast (for example, skipping comprehensive data validation to save time), the cost may surface later as a major failure in production. There is also the risk of scope creep – migrations sometimes expand in scope if, say, one decides to also reengineer parts of the application or include additional modernization (like moving to the cloud, implementing new features, etc.). Without disciplined scope management, costs can spiral.
Budgeting for an IMS migration must include not just the technical work but also training, hiring consultants or migration experts, data cleaning efforts, parallel operations, and contingencies for unexpected issues. Many organizations underestimate the cost; as an industry expert advised, one should always conduct a detailed cost/benefit analysis and project plan because “conversion can be very costly” and needs to be justified by clear benefits
. Cases abound of legacy migrations running over-budget due to unanticipated complexity.
There’s also the hazard of mid-project changes or loss of support. If a migration takes multiple years, business priorities might shift or executive sponsors might change, potentially putting the project at risk of cancellation or reduction in resources partway through. This could waste the investment already made and leave the organization in a precarious state (half on IMS, half on relational, which could be worse than either alone). Therefore, maintaining management commitment and demonstrating incremental progress (perhaps via phased deliveries) is important to keep funding secured.
To manage these risks, strong project governance is required. Still, even with good management, some costs are unavoidable. For example, dual-running systems for a period (to verify consistency) means increased operating expense. Migrating large volumes of data might require specialist tools or hardware (e.g., high-performance extractors or interim storage), which add cost. If external vendors or migration service providers are involved, their contracts need to be managed to avoid cost overruns (fixed-price engagements can mitigate that but only if the scope is well-understood).
In summary, cost and time hazards revolve around underestimation, parallel running expenses, scope creep, and the challenge of maintaining legacy operations during the transition. Organizations must prepare for a substantial investment and ensure that the migration is treated as a strategic initiative with proper funding. Those that fail to do so may end up in a worst-case scenario – having spent a lot of time and money, but with no successful migration to show for it (or a migration that delivered a system that is more expensive than the legacy one). Realistic planning and continual risk assessment are the keys to avoiding this outcome.
Staff Training and Support
The human factor is a critical element in the success of IMS-to-relational migration. The shift in technology requires new skills, and there is often resistance to change among staff. If not addressed, this can become a major hazard that undermines the project.
IMS specialists, such as IMS database administrators and application programmers, have deep knowledge of the current system but may not be proficient with relational databases or SQL. Conversely, the organization might bring in new staff or consultants skilled in relational databases who lack understanding of the legacy business rules embedded in IMS. This mismatch can cause miscommunication and errors. One risk is losing the expertise of IMS veterans (through retirement or attrition) before their knowledge is transferred or utilized for the migration. The knowledge of how data is structured and how applications behave in IMS is often undocumented and resides in people’s heads. As earlier noted, having access to subject matter experts can “significantly decrease risk” by leveraging their knowledge
. If those experts leave or are not available, the project can flounder (for instance, subtle data relationships might be overlooked in the new system design, leading to issues).
Training existing staff on the new relational database and SQL is essential. This includes developers learning to write efficient SQL and understand relational schema design, DBAs learning to administer and tune the new database, and even end-users or report writers getting up to speed on new query tools. Without adequate training, mistakes will happen – e.g., developers might recreate procedural IMS-like processing in SQL in a way that is inefficient, or operations staff might not know how to backup/restore the new database properly. A classic issue is cultural resistance: people who have worked with IMS for decades might be skeptical of the new system and reluctant to fully engage with it. As one study pointed out, “relational systems require dramatic changes in business processes, and resistance to change can be expected”
. There may be fear that jobs will be lost or roles diminished once IMS is gone, which can reduce cooperation.
To mitigate this, a clear change management program is needed. The organization should communicate the reasons for migration and perhaps even offer incentives or reassurances to IMS staff that their expertise is valuable in making the new system succeed. Often, IMS experts can become key contributors (for example, helping to validate that the new system’s outputs match the old system) and can transition to roles supporting the new environment if given training. At the same time, new skills may be brought in. Modern IT environments might require knowledge of data modeling, ETL tools, or new programming languages (Java, .NET, etc., if the application layer is also modernized). The academic literature notes that IS professionals might have to become familiar with object-oriented languages and frameworks around the new system
, as legacy IMS apps are usually not object-oriented. This learning curve must be accounted for in project timelines.
Staffing the project appropriately is another hazard. Sometimes companies assume their existing team can handle the migration on top of their regular duties, but realistically, dedicated migration teams are needed. Key roles include data architects, database administrators (for both IMS and the new DB working in tandem), application developers/testers, and project managers. If the staff is stretched too thin or if critical expertise (like an IMS DBA) leaves mid-project, progress can stall. It’s recommended to secure the “proper personnel” and ensure they are allocated significantly (often, external consultants with prior migration experience are hired to supplement internal teams)
. Lack of expertise in either IMS or the target database is a risk; the project needs people who understand both sides or close collaboration between those who do.
Moreover, post-migration support must be planned. The team that built the new system will need to support it in production until the in-house team is fully comfortable. This might mean running an extended hyper-care period after go-live where additional support (from the vendor or migration experts) is on hand to quickly resolve issues. If this isn’t arranged, the internal staff (still on a learning curve) might struggle to troubleshoot problems on the new platform, jeopardizing stability.
Finally, the organization’s business users may also need to adapt. For example, if previously reports were generated via IMS-specific tools or processes, and now maybe they will use SQL queries or a new reporting system, the users should be trained. “Business users will have to be trained in the use of SQL and other data tools” in the relational world
. Without this, they might find the new system hard to work with, leading to frustration or operational mistakes.
In summary, the hazards related to staff and support include loss of IMS institutional knowledge, insufficient training on the new system, resistance to change, and underestimation of the human effort required. Mitigation involves investing in comprehensive training programs
, retaining or contracting experts for both old and new systems, and actively managing the change so that staff buy-in is achieved. The success of the migration is as much about people as it is about technology.
Cutover and Coexistence Risks
The final hurdle in a migration is the cutover – the point at which the organization switches from using the IMS database to the new relational database as the system of record. This phase carries significant risk because it often involves downtime, data synchronization, and the possibility of unforeseen issues coming to a head in a live environment. There are different strategies for cutover, broadly categorized as “big bang” vs. phased (or “parallel”) approaches, each with its hazards.
In a big bang cutover, the entire IMS database is migrated in one go, and at a chosen cutover moment, all users and applications are switched to the new relational system. The risk here is concentrated: if anything goes wrong, it affects the entire system. The cutover process itself must be executed near-perfectly. As one source on cutover strategy notes, “the cutover period needs to be executed perfectly. Otherwise, the organization runs the risk of delays.”
. For a large IMS database, the final data export from IMS and import into the relational DB may require a substantial outage window. If that window overruns (for instance, the data load takes longer than anticipated, or issues are encountered), it can directly impact business operations. During cutover, there is also the risk of data discrepancies – if any transactions occur on IMS after the data extract (during the switchover period), they might be lost unless special measures are taken.
To mitigate this, many organizations perform cutover during off-hours or a weekend and often freeze the IMS database updates for a short period (read-only mode) while final synchronization happens. Yet, not all systems can afford a lengthy freeze. If cutover fails (say the new system isn’t working correctly and you need to fall back to IMS), rolling back can be complicated if any data changes occurred in the interim. Hence, having a robust backout plan is crucial – e.g., keep IMS online and be ready to extend its usage if the new system faces critical issues.
A phased migration or coexistence approach can reduce risk by spreading it out. This might involve migrating subsets of data or functionality in stages, or running IMS and the new database in parallel for some time. One method is to perform a parallel run, where both IMS and the relational database are kept in sync (via replication or dual-write mechanisms) and the applications read from the new database while IMS is maintained as a backup for a while. Parallel runs allow comparison of outputs and performance between the two systems in real time, greatly de-risking the final switchover
. For example, the migration strategy for Provinzial’s IMS-to-Db2 project explicitly included an “in-place migration and a parallel operation concept” which allowed them to achieve “absolute security and quality during the migration” – essentially no disruption to ongoing development and operations due to this parallel approach
delta-software.com. The obvious downside is complexity: running two systems in parallel means you need reliable data replication and additional resources, and users must sometimes work with two systems (which can be confusing). It’s resource-intensive to maintain, but it provides a safety net.
Another phased approach is functional phasing: moving one application or one module at a time to the new database. For instance, if an IMS database supports multiple business domains, you might migrate one domain’s data and applications first as a pilot. This limits the blast radius of any issues. However, if the data is highly interrelated, splitting by functional area can be very tricky. Alternatively, some choose to migrate read-only analytical workloads first (feeding a data warehouse) and later migrate the core transactions – again, to learn and stabilize in steps.
During coexistence, data consistency is a big hazard. If both IMS and the new DB are live, keeping them synchronized can be challenging. There are tools and strategies (like change data capture from IMS logs to propagate changes to the new DB in near-real-time), but implementing them requires precision. Any lapse could lead to divergence, and reconciling two large databases is extremely difficult. So while coexistence reduces immediate cutover risk, it introduces ongoing synchronization risk.
Cutover planning must also account for peripheral systems. Often, other applications, interfaces, or reports rely on the database. When you switch databases, all those integration points (downstream systems, batch jobs, etc.) have to be adjusted to point to the new database or potentially to a new format of data. If any are forgotten, something will break. Comprehensive inventory of all consumers of the IMS data is necessary to avoid an unpleasant surprise post-cutover (for example, an FTP extract or a CICS transaction that suddenly can’t find its data).
Testing is the best mitigation for cutover risk. Rehearsing the cutover process (in dress rehearsals) can expose timing issues or steps that were overlooked. Some organizations perform multiple trial migrations with full data volume to measure exactly how long it takes and what issues arise, then refine the process. Even so, nervousness on cutover day is warranted – the final production cutover is often tense because despite all testing, the live environment can behave differently.
In summary, cutover and coexistence hazards include: potential downtime overruns, data loss or inconsistency during switchover, difficulties in rollback, complexity of parallel operations, and missing synchronization of external systems. A well-thought-out cutover plan – whether hard cutover or phased – is essential. Many experts advise opting for a phased or parallel cutover when possible, to “greatly de-risk the outcome” even though it’s more complex
. On the other hand, if the environment is small enough, a big bang might be simpler and quicker, just requiring careful execution
zmainframes.com. The choice depends on the system’s complexity and tolerance for downtime. Regardless, having key personnel on standby and a contingency plan (e.g., extend IMS availability, or have support teams ready to fix issues in real-time) can help manage this final hurdle.
Case Studies and Industry Examples
Real-world IMS-to-relational migrations provide valuable lessons about the hazards and how they can be overcome. Below we review a few industry examples:
Case Study 1: U.S. State Agency – Overcoming Manual Migration Challenges. A large U.S. state government agency undertook a project to convert their IMS data (related to client management for social services) to Db2. Initially, they experimented with a purely manual approach on a small subset of data – and found it took six months just to convert that small portion
. This foreshadowed the impractical timeline of a fully manual migration for their entire database, which handled information on a quarter million clients. During this time, their production IMS system’s data was still growing (daily updates continued to “pile up” in IMS)
precisely.com. The agency realized that by the time they manually converted data, the IMS system could become too out-of-sync or even risk failure under the growing load. Indeed, they feared that if they delayed, the aging IMS could “conceivably just stop working” as demand grew – an unacceptable scenario for a critical public service system
precisely.com. They also faced $800,000 per year in IMS licensing and maintenance costs, and making mandated changes in the IMS-based software had become “more and more unwieldy”
precisely.com. One of their database administrators explicitly noted the extreme difficulty and risk of rewriting application code for a new database
precisely.com. These factors (time, cost, and application risk) made it clear that a different approach was needed. The agency decided to use a specialized migration solution (Syncsort Optimize IMS, a tool designed for IMS-to-Db2 conversion) and the associated services team
precisely.com. By leveraging automated tools and expert help, they were able to migrate the IMS data to Db2 without “crippling their operations or busting their budget”
precisely.com. The lesson from this case is the importance of automation and expert support: a brute-force manual migration would have been too slow and risky, but with the right tools the agency accomplished the migration faster and avoided the hazard of an overstressed IMS or a massively overrun project. It also highlights the need for finding a vendor with specific IMS-to-Db2 experience – the agency discovered that not all IT service providers have the requisite expertise, so choosing the right partner was itself a critical success factor
Case Study 2: Fortune 500 Distribution Company – Avoiding Skills Gap and Ensuring Minimal Disruption. This example involves a global distribution company (a Fortune 500 firm) that faced a looming skills gap as their IMS experts neared retirement. Their IMS applications were core to their operations, but the pool of IMS talent was shrinking, and relying on expensive third-party support was not a sustainable solution
. They also incurred high costs for IMS software licenses and third-party tools. To address these challenges, the company decided to migrate from IMS to Db2 using a product called Syncsort DL/2, which provides a “transparent data migration” approach. This tool allowed them to move the IMS database contents into Db2 tables without changing the existing application code
slideshare.net. Essentially, DL/2 acted as a bridge – it replaced the IMS database calls with calls to Db2 under the covers. By doing so, the company avoided the risky, time-consuming step of rewriting millions of lines of application code, immediately closing the skills gap (their applications could now be maintained by SQL/Db2 developers rather than IMS specialists)
slideshare.net. The migration was completed in record time with minimal disruption to the business, since the end-users and existing programs did not even realize the database had changed – everything “appeared unchanged in interface and behavior” thanks to the transparency of the solution
mlogica.com. Post-migration, they eliminated the recurring costs of IMS and related tools and established a modern platform for future enhancements
slideshare.net. The outcome was clearly positive: it demonstrates that using a middleware translation approach can dramatically reduce risk by leaving applications untouched, though it requires a robust product to emulate IMS functionality on Db2. This case shows that one way to mitigate application refactoring hazards is to decouple them from the data migration – handle data first (with a tool like DL/2 serving as a compatibility layer), and then gradually update applications to use SQL natively at a later stage. The key lesson is that addressing the skill shortage was a major motivator, and the solution was tailored to that by ensuring the company could transition to Db2 skills without losing decades of application investment
Case Study 3: Provinzial Insurance – Large-Scale Automated Migration. Provinzial Rheinland, a large European insurance company, had an extensive legacy environment with 75+ IMS databases and thousands of programs
delta-software.com. They needed to migrate all of this to Db2 and simultaneously wanted to move off the mainframe to a UNIX/Linux platform in the long term
delta-software.com. Attempting a manual rewrite was out of the question due to the volume (over 25 million lines of code)
delta-software.com and the requirement that “application logic was not allowed to be changed”
delta-software.com (the new system had to behave exactly the same as the old). Another complication was that they needed to continue new development on the IMS system during the migration (“parallel further development and maintenance” had to continue)
delta-software.com. Provinzial’s solution was to use a fully automated migration toolset from a vendor (Delta Software Technology). This toolset handled both data model transformation and application code transformation in an integrated way
delta-software.com. By automating the conversion, they ensured consistency and saved time, but more importantly, it allowed for ongoing parallel operations. Their IMS and Db2 environments ran in parallel until everything was validated, and because the transformation was in-place and automated, the business experienced no downtime or freezes in development
delta-software.com. One of the success points they cite is the use of a parallel operation concept to guarantee smoothness, effectively mitigating the cutover risk by having both systems live and consistent until they were ready to fully switch
delta-software.com. Provinzial completed the migration at a fixed cost and noted that having a tailor-made solution gave them control to influence performance (they could adjust the new data model where needed for optimization)
delta-software.com. The lessons from this case are the power of automation at scale, and the effectiveness of running old and new in parallel to eliminate downtime risk. It also underscores that performance must be considered – Provinzial explicitly addressed performance by choosing how to transform certain structures and by not using any emulation (they went for a clean break such that the new applications are pure Db2, not IMS emulated)
delta-software.com. This case demonstrates that even very large IMS environments can be migrated successfully with near-zero business disruption if the right technology and planning are applied, albeit such projects are major undertakings that require experienced vendors and clear strategies.
Case Study 4: Insurance Company Mainframe to Cloud Migration (IMS to SQL Server). Another example is a leading insurance provider that migrated from IMS on the mainframe to Microsoft SQL Server on AWS cloud
. In this modernization, the goal was not only to move to a relational model but also to replatform off the mainframe to reduce costs. The company engaged a migration partner who provided an automated solution to convert the IMS data structures and even enabled the existing COBOL programs to run on the new platform with minimal changes
mlogica.com. They utilized a tool that allowed COBOL code to execute with a SQL Server back-end (essentially intercepting IMS calls as in prior examples)
mlogica.com. By doing so, their staff could keep using the same applications with almost no retraining – the interface remained the same, which ensured a “virtually seamless transition” for end users and operators
mlogica.com. The cutover was done with continuous replication until switch-over, supporting the requirement for continuous availability of business applications
mlogica.com. Post-migration, the company saw benefits like access to a broader set of reporting and integration tools on SQL Server, and significantly reduced support and licensing costs by moving off the mainframe and onto cloud infrastructure
mlogica.com. The key takeaway from this case is that migrations can also be an opportunity to replatform and not just change the database technology. However, doing both at once (moving database and moving to cloud) adds complexity – it requires ensuring performance on a different hardware architecture and might introduce latency differences, etc. The success in this story hinged on thorough assessment and planning of both data and workloads, and providing strong support during and after the move
mlogica.com. It highlights the importance of understanding interdependencies (they did comprehensive assessment of source and target environments and business requirements before migration
mlogica.com) and the value of keeping applications unchanged while the infrastructure undergoes a big change.
These case studies reinforce several themes: automation and tools can greatly reduce risk and time, preserving application logic either via transparent migration tools or careful code conversion is crucial, parallel operations or phased cutovers provide safety, and the driving reasons are often a mix of cost, agility, and skill concerns. They also show that no one-size-fits-all – some chose to not change code at all, others automated code conversion; some did in-place parallel migration, others did a quick cutover. Each strategy addressed the particular major hazards that the organization was most concerned about (be it skills, downtime, or cost). Organizations considering an IMS-to-relational migration can learn from these examples to shape their own approach, focusing on mitigating the risks most pertinent to their situation.
Best Practices and Mitigation Strategies
Migrating from IMS to a relational database is challenging, but by following best practices and proactive mitigation strategies, organizations can significantly reduce the risks. Below are key recommendations to help avoid or minimize the hazards discussed:
- Comprehensive Up-Front Planning: Invest heavily in the planning and analysis phase before writing any code or moving any data. This includes a detailed assessment of the existing IMS environment (data structures, program inventory, dependencies) and a well-thought-out migration plan covering data conversion, application changes, testing, and cutoverzmainframes.comzmainframes.com. Adequate planning is “required to keep risk at a minimum”virtualusergroups.com – it forces you to surface and address potential problems on paper first. Define the scope clearly and get buy-in from stakeholders on timelines and expectations. A formal project plan with phases and milestones (and some contingency built in) will guide the effort and help manage cost/time overruns.
- Secure Subject Matter Experts (SMEs): Involve people who deeply understand the IMS data and applications throughout the project. Their knowledge of data nuances and business rules is invaluable for designing the new schema and verifying correctness. Having access to IMS veterans “significantly decreases risk” because you can leverage their insight to avoid mistakesvirtualusergroups.com. As these experts may be scarce, secure their time early and consider retaining retirees or external consultants familiar with IMS. Use their input for data mapping specifications, validation criteria, and ensuring no functionality is lost in translation.
- Iterative Data Mapping and Automated Conversion: Approach data migration methodically. Start by reverse-engineering the IMS schema into a logical data model. Identify how each segment and field will map to the relational model – document source-to-target mappings. Use automated tools to assist in extract-transform-load (ETL) where possible, but also be prepared for manual intervention on complex structurescmr-journal.orgcmr-journal.org. For example, handle repeating groups by normalizing them into child tables and consider generating surrogate keys for segments without unique identifiersibm.com. A best practice is to keep the target design as simple as possible – don’t over-engineer the new schema. Overly complex re-design can become a “never ending project” and confuse usersvirtualusergroups.com. It’s often wise to first aim for a schema that closely mirrors the existing data (in normalized relational form) rather than introducing sweeping new data models. Simplicity will aid validation. Ensure that your conversion tooling (custom scripts or vendor products) does most of the heavy lifting for data transformation, and verify that the tool can handle IMS-specific data types (e.g., packed decimals, binary fields).
- Phased Migration and Parallel Run: Wherever feasible, use a phased approach instead of a big-bang cutover. This could mean migrating in functional increments (module by module) or doing a parallel run of IMS and the new relational database. Running both systems in parallel for a period allows you to cross-verify outputs and performance, greatly reducing the risk at final cutoverdelta-software.com. It provides a safety net – if issues are found in the new system, you can fall back to IMS until they’re fixed, without data loss. Parallel running is resource-intensive, but it “greatly de-risks the outcome”techcommunity.microsoft.com by ensuring business continuity. Many successful migrations have used techniques like replicating changes from IMS to the new database in real-time, so that when users are switched over, the data is already up-to-date and proven. If parallel operation of the full system isn’t possible, consider at least a pilot parallel run for a subset of the system.
- Robust Testing and Data Validation: Testing is one of the largest and most critical tasks in the migration. Plan for multiple levels of testing: unit tests for individual data mappings and program changes, system integration tests for end-to-end business processes on the new database, performance tests under production-like loads, and parallel comparisons with the IMS outputs. It’s advisable to create a comprehensive validation plan that defines how you will ensure the migrated data matches the source. For instance, you might run reports from both systems and compare results, or use data validation tools to do table-by-table checksums. “Have a reliable method of data validation,” as noted in best-practice guidelinesvirtualusergroups.com. This could involve sampling certain records and comparing every field, verifying record counts, and reconciling aggregate totals (e.g., sum of amounts in IMS vs sum in new DB). Automate the comparison where possible to handle large volumes. Additionally, plan multiple mock migrations in a test environment to refine the process. The final cutover should not be the first time you execute the migration procedures; rehearsals will help iron out scripts and timing. By the time you go live, the team should be confident because the migration has essentially been “practiced” before.
- Performance Tuning and Optimization: Mitigate performance risks by addressing them early. As you design the relational schema, involve DBAs and developers to plan indexing and query optimization for high-volume transactions that were identified in IMS. If IMS had any particularly performance-critical functions (like very high TPS transactions or heavy batch jobs), simulate those on the new database and see if the design or SQL needs tweaking. Sometimes denormalizing certain tables or adding summary tables can help to preserve performance – these decisions should be made before finalizing the design. Also, size the hardware appropriately: ensure the new system has the CPU, memory, and storage I/O throughput to handle the workload, keeping in mind that Db2 (or the chosen RDBMS) may use more CPU than IMS for the same task due to SQL processing overheaddb2portal.blogspot.com. Engage performance experts to do a capacity planning exercise. It’s much safer to tune the system in pre-production than to react to issues in production. After migration, continue to monitor performance closely – have the team on hand to quickly add indexes or adjust queries if real-world usage reveals bottlenecks.
- Gradual Application Refactoring: For application migration, consider a gradual or tool-assisted approach. If using a bridging solution (IMS calls to SQL translator), that can allow you to move data first and then refactor code step by step. If not, then prioritize which programs to convert first (often the most critical ones) and perhaps run some less-critical ones in read-only mode on IMS until they can be ported. The key is to avoid a rushed, error-prone code rewrite of everything at once. Use modern code analysis tools to find all IMS calls in the codebase and create a map of what needs changing. Then establish a factory-like process to convert and test each program. Automated refactoring tools can be very helpful – e.g., products that convert COBOL + DL/1 calls into COBOL with embedded SQL – but budget time to manually fine-tune and test the output of those tools. Employ thorough regression testing on applications: the business users should perform parallel tests (perhaps using copies of nightly reports or screens from IMS vs new system) to confirm the applications behave identically. It is often effective to involve end-users in acceptance testing to gain confidence. Remember the lesson: don’t shortcut analysis/design or testing in the application domain eithervirtualusergroups.com.
- Change Management and Training: Mitigate resistance and ensure a smooth transition by preparing the people. Implement a training program for IT staff to get them comfortable with the relational database, SQL, new tools (e.g., a modern IDE or data modeling tool), and new operational procedures. This training should happen well before cutover so that by the time the new system is live, the team is ready to support it. Also train end-users if the way they interact with the system is changing (for example, if they will query the new database for ad-hoc info, teach them basic SQL or provide user-friendly query tools). It may be beneficial to run the new system in parallel internally (with some users testing it) to build familiarity. Address the cultural aspect by highlighting the benefits (e.g., easier reporting, future innovation) and, if possible, preserving some continuity (perhaps keep certain interfaces the same while the backend changes). Management should communicate clearly that the migration is a strategic necessity and that the staff’s roles will evolve, not vanish. Consider establishing a support helpdesk during the transition so that if any user or developer encounters an issue with the new system, help is readily available. Post-migration, conduct retrospectives and share success stories to reinforce adoption.
- Engage Experienced Partners and Use Proven Tools: If your organization lacks in-house experience with such migrations, it is often worth engaging specialized vendors or consultants who have done IMS-to-relational conversions. Many case studies showed success when using proven tools and expert services – for instance, automated conversion frameworks (from vendors like Precisely, mLogica, Delta, etc.) and experts who know the common pitfallsmlogica.comdelta-software.com. These tools can handle much of the tedious conversion work (both data and code) and come with methodologies honed over multiple projects. When selecting tools, ensure they support your specific IMS features (e.g., do they handle IMS secondary indexes? Can they convert GSAM files if you have any? etc.). Also, ensure the vendor provides support during the migration and ideally after (for a period) to assist with any issues – “make sure that your tool vendor has the capability to assist you” during the processvirtualusergroups.com. Essentially, don’t reinvent the wheel; leverage lessons and tools from others to reduce risk.
- Plan for a Safe Cutover: Lastly, have a detailed cutover plan and rehearse it. Decide on a cutover strategy (big bang vs phased) appropriate for your situation, and document every step for the switchover, including backups, final data sync, verification, and contingency. If doing a big bang, aim to minimize downtime and have extra hands on deck to quickly resolve any surprise. If doing phased, clearly define how you’ll keep systems in sync and how you’ll decide it’s safe to decommission IMS. A good practice is to schedule cutover at a low-traffic period and communicate to all stakeholders well in advance. Also, have a rollback plan: know the criteria for aborting the cutover and falling back to IMS, and how that would be done (e.g., restore any changes that happened in interim). Having this fallback can be a psychological safety net, even if you don’t end up using it. After cutover, don’t immediately disband the migration team – maintain a “war room” for a week or two to quickly tackle any production issues on the new system. This ensures any minor glitches don’t turn into major problems.
By following these best practices, organizations can address the main areas of risk: data integrity, performance, application correctness, project overruns, people issues, and cutover stability. A successful IMS-to-relational migration is typically the result of meticulous preparation, the right expertise, and careful execution with plenty of validation. In essence, plan thoroughly, use the right tools, involve the right people, and take a phased, tested approach – this maximizes the likelihood of a smooth transition with minimal surprises.
Conclusion and Recommendations
Migrating from a hierarchical IMS database to a relational database is undoubtedly a challenging endeavor, but it can be achieved successfully with diligent planning and risk management. In this paper, we explored the key hazards that organizations face during such migrations: the intricacies of mapping hierarchical data to relational schemas, potential performance regressions, extensive application refactoring, the danger of cost and schedule overruns, the necessity of retraining staff, and the critical decisions around cutover strategy. Each hazard carries significant implications for project success. If ignored or underestimated, any one of these factors – be it a data integrity issue or an unhappy user base – could derail the migration or negate its benefits.
The experiences of organizations that have completed IMS-to-relational migrations highlight that mitigating these risks is possible. Successful projects universally stressed up-front analysis, use of automated tools and expert support, incremental or parallel migration techniques, and thorough testing. On the flip side, failed or troubled projects often can be traced to shortcuts in planning, insufficient understanding of the legacy system, or attempting a “big bang” change without proper safety nets. Thus, the overarching recommendation for any organization considering this transition is to approach it as a strategic, well-resourced project rather than a simple technology upgrade. That means securing executive sponsorship, funding, and allocating a dedicated team that can focus on the migration. It also means being realistic about the effort: migrating decades-old critical systems is non-trivial, but the rewards – in terms of future agility, cost savings, and risk reduction from legacy dependence – are substantial when done right.
To ensure success, organizations should follow a structured roadmap: (1) Do your homework (know your IMS environment in detail and design the target carefully); (2) Mitigate upfront (address known challenges like data quality or code conversion with the right tools/skills before they become problems); (3) Test and validate at every step (never assume the converted data or programs are correct until proven so; maintain parallel runs or backups until confidence is earned); (4) Go in phases (whenever feasible, break the migration into smaller launches to limit risk exposure); (5) Don’t skimp on training and change management (a modern database is only as effective as the people using and supporting it). By following these steps and the best practices outlined, the common hazards can be managed.
In conclusion, migrating from IMS to a relational database is like performing open-heart surgery on an organization’s IT core: it requires precision, expertise, and careful monitoring, but it can rejuvenate the patient. Companies that have navigated this journey successfully now enjoy modern, flexible systems that meet today’s business needs, freed from the constraints of 50-year-old technology. The path is challenging but navigable. With meticulous planning, the right team and tools, and a prudent approach to risk, organizations can modernize their IMS legacy systems to relational databases without compromising their data integrity, performance, or business continuity. It is a voyage that should not be taken lightly, but for many enterprises, it is a necessary one – and ultimately, with proper execution, a highly rewarding one that paves the way for future innovation and growth.
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