The perception versus reality of data masking in mainframe environments is usually caused by a significant gap between what organizational leaders think data masking requires and what it actually entails.

Many assume that data masking is complex, expensive, and time-consuming. In reality, data masking is easy, fast, and cost-effective with the right technology.

The mistaken perception stems from the fact that the difficulty isn’t the data masking operation itself. Instead, complexity lies in the upfront analysis when organizations determine what to mask and how to do it. Likewise, challenges come from data context and multiple use cases for each data set, not from the data masking technology.

For example, after mainframe data masking requirements are defined, DataVantage DME Data Masking Express™, handles the process quickly, efficiently, and affordably. Understanding this reality is important, because when organizations see how easy data masking actually is, they can modernize their data protection strategy quickly, efficiently, and cost-effectively.

Simplicity: Hit the ‘Easy Button’ for Data Protection

Masking with DataVantage DME is straightforward. Users specify the rules, then the technology handles the masking automatically. Another benefit is that because DataVantage DME works independently of other mainframe solutions, no changes are made to existing workflows.

Plus, DataVantage DME focuses solely on data masking, so it doesn’t require unnecessary features that could potentially complicate data masking processes or increase costs. Data teams can use the DataVantage DME  solution to mask sensitive information without disrupting production environments or having deep technical expertise.

Most organizations need to configure their data just once, and then schedule recurring jobs. The process is performed as scheduled, with minimal oversight. This way, whether organizations need to mask data nightly, weekly, or monthly, they can schedule the process to become part of existing workflows.

Because DataVantage DME makes data masking easier, organizations don’t need to take a brute force approach that masks all of their data. Instead, they can mask only sensitive personally identifiable data.

Speed: Fast, Focused, and Scalable

Data masking with DataVantage DME can be performed quickly. It runs as a standalone batch process, so it doesn’t interfere with other systems or cause operations to slow down. Organizations can schedule data masking during off-peak hours or as part of existing batch workflows, ensuring sensitive data is protected without impacting business operations.

DataVantage DME is also designed to scale. It can mask full-size production databases when needed. It also supports focused data masking for specific applications, lines of business, or data domains. This flexibility allows organizations to target their data masking strategy based on each use case rather than taking an all-or-nothing approach.

Subsetting adds another layer of efficiency. With subsetting, organizations can use a small, representative slice of a masked data set for testing, development, or analytics. Because the subset contains only the data that’s needed, rather than a full copy, jobs run faster, consume fewer resources, and require less storage.

Subsetting and data masking often get mashed together, but they’re separate processes. Masking protects sensitive values. Subsetting reduces the volume of data organizations work with. Some DataVantage DME customers mask an entire production database. Others mask smaller subsets for specific applications or teams.

Cost Effectiveness: Purpose-Built and Resource-Light

DataVantage DME offers data masking capabilities at a low cost compared to other data obfuscation options. One reason is because competing tools often bundle masking as an add-on to a larger solution. This forces organizations to purchase an expensive, full-featured product, rather than a single, dedicated data masking solution.

DataVantage DME is different. It takes a single-focused approach, which is only data masking. Not having extra functions minimizes licensing, training, and resource expenses. DataVantage DME’s low overhead and standalone operation mean organizations don’t need extra infrastructure, which minimizes costs.

DataVantage DME Differentiation: Power Without Disruption

DataVantage DME runs independently from other mainframe systems, so there is zero conflict with existing software. As a result, the data masking solution offers strong performance without affecting the mainframe operating system or interfering with other applications.

DataVantage DME’s simplicity, adaptability, and affordability make it the preferred choice for organizations seeking reliable, compliant data masking that “just works.”

Five Takeaways for Business Leaders

1. Masking isn’t the hard part. The challenge is deciding what to mask and how.
2. Speed and agility can coexist. Masking can be a fast, standalone process.
3. Decision makers decide scope. Mask full databases or targeted subsets based on need.
4. Cost stays under control. A purpose-built masking tool minimizes licensing costs.
5. Compliance becomes routine. Masking is a repeatable part of standard operations.

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In today’s distributed computing landscapes, protecting sensitive data has become more challenging and urgent. Regulatory pressures are increasing, third-party services are expanding, and artificial intelligence (AI) is accelerating the risk of re-identification.

For industries like finance, insurance, retail, and healthcare, it’s no longer enough to only mask the obvious stuff. That’s because AI can correlate anonymized data with public or purchased sources to re-identify individuals.

AI accelerates how quickly attackers can exploit indirect identifiers. In this environment, effective data masking has become both a regulatory compliance necessity and a business enabler. At the same time, the best practice is to mask only what’s necessary. Taking a strategic approach, not indiscriminate masking, ensures data remains both secure and useful.

The Business Case for Data Masking

Data masking on distributed systems offers a critical safeguard by ensuring that personal and sensitive data is obfuscated, while preserving its usefulness for testing, analysis, and other business processes supporting AI initiatives. Masked data is no longer personally identifiable, but it still behaves like actual data for testing and other use cases.

Common types of sensitive data on distributed systems that often require masking include:

• Personally identifiable information (PII) such as names, social security numbers, addresses, and birthdates.
• Financial information like credit card numbers.
• Answers to credentials and security questions, such as a mother’s maiden name, that can be matched with data records.

The decision of what to mask depends on industry regulations, intended use, and where the data will be stored or shared. There’s no one-size-fits-all answer for when to mask data or what data to mask.

Data Masking Use Cases in Distributed Environments

Business reasons to mask data in distributed environments include: testing and development, third-party statistical analysis, data migrations, and compliance-driven requirements.

Testing and Development

Quality assurance teams need realistic but anonymized data to validate application accuracy. For example, insurers need accurate data without exposing customer PII to calculate premiums. Tools like those from DataVantage allow subtle adjustments, such as offsetting dates, to keep tests accurate while protecting privacy.

Third-Party Statistical Analysis

Organizations often share data sets with external partners for research or analysis. A best practice is to minimize data fields, providing only what’s essential and omitting what’s not needed. For example, to support drug research without compromising privacy, healthcare providers can mask patient identifiers while sharing demographic and prescription data with pharmaceutical partners.

Data Migrations

Large-scale data migrations often require multiple test runs. Instead of moving terabytes of sensitive production data, organizations can create subsets. This speeds iteration, reduces risk, and preserves referential integrity. A financial services firm migrating customer records, for instance, can use masked subsets of data to validate schema integrity and troubleshoot issues without exposing actual social security numbers.

Compliance-Driven Requirements

Highly regulated industries must demonstrate data protection. Regulations such as HIPAA, PCI DSS, and GDPR may require that masked values never match real values in an organization’s production systems. This allows organizations to protect sensitive data, even in non-production environments.

Top Challenges When Implementing Data Masking

Successful data masking requires solving these common obstacles:

• Cross-domain collaboration. DBAs, developers, and compliance officers must align on what to mask and how.
• Configuration expertise. Masking often involves replacement tables, date offsets, and rules that fit the way the business actually uses the data.
• Striking a balance. Over-masking makes data useless, while under-masking creates compliance gaps.

Data masking is not a “check-the-box” exercise. It requires planning, context, and organization-wide alignment.

How DataVantage Global® Delivers Effective Masking

DataVantage Global® provides powerful masking, de-identification, and data management tools tailored to distributed environments. The software supports secure, compliant, and efficient operations for your business.

DataVantage Global® Key Features

Data masking: Protect sensitive data with built-in masking and de-identification functions, ensuring safe use in development, testing, analytics, and AI ingestion.

Data management: Subset, sample, and process data while maintaining referential integrity across complex environments.

Data migration: Manipulate and transfer data across platforms including Oracle, SQL Server, Db2, or CSV files.

Data unification: Combine data from diverse environments, like Oracle, SQL Server, Db2, or CSV.

Data transformation: Modify data structures for analytics or AI training, while preserving critical relationships and protecting sensitive data.

Process automation: Automate any function with internal or external task schedulers to optimize workflows and reduce manual intervention.

In distributed environments, effective data masking is both a privacy safeguard and a business enabler. With DataVantage Global, organizations can protect sensitive data, meet compliance requirements, and still derive business value from their data.

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How Did DataVantage for IMS originate?

When I began my career as a programmer in the late 1960s, data processing looked very different. Businesses relied heavily on mainframe IMS environments. While IMS was groundbreaking for its time, it also presented some major challenges. Navigating hierarchical structured databases was a cumbersome task

Programmers like myself were forced to rely on paper diagrams to understand the layers buried within the data. The process was slow, error-prone and frustrating. In some ways it was guesswork. I knew there had to be a better way to handle IMS database testing.

When consulting in the 1970s, I realized we needed to create test data to parallel the application logic. However, the program that came with IMS, DLTO, lacked the power to do this because it was two-dimensional. Developers were receiving error messages in response to calls they were using and, further, we needed to be able to compare data between databases to see how the logic was working in the application programs.

My solution was to develop a product with the larger goal of creating an easy-to-use catalog for IMS that could be created from COBOL copy books or manually from printed programs making the process straightforward, multi-dimensional, and more intuitive. My idea was to employ commands and instructions that paralleled IMS itself.

The most important product design decision I made was to build DataVantage for IMS as an “application program.” Moving forward, we understood that IBM might change databases, operating systems, or even the mainframe itself. Applications were different, however, and I was confident that IBM would make sure they would persist throughout time. That’s why many versions of DataVantage for IMS that were sold in the 1980s are still running today. 

Adapting Through Time

Innovation is not static. The world changes, and successful companies evolve with it. I’ve always believed that listening to our customers is key to staying relevant. Over the decades, we’ve expanded our offerings into other areas of enterprise software, as well as distributed computing environments.

Today, our suite of DataVantage software solutions helps organizations efficiently view, edit, copy, manage, migrate, scramble, mask, and de-identify sensitive data across various database environments

Each of these additions to our portfolio came from identifying a need. Data masking, for example, rose as a critical capability for protecting businesses from data breaches. Expanding into this space meant not just meeting customer expectations, but exceeding them, with tools that were secure, reliable, and easy to use.

Another reality of today’s computing environments is that many of our clients work through third-party outsourcers. This adds complexity, but it also reaffirms the value of creating flexible solutions. By ensuring our tools can integrate seamlessly into diverse workflows, we’ve been able to maintain strong partnerships with both end-users and their outsourcing providers.

Remaining Relevant Today

DataVantage’s legacy remains a core part of our identity, but it’s never just about the past. What we began over four decades ago remains just as relevant today because we’ve consistently evolved alongside our clients’ needs. We’ve maintained our position as a trusted provider of data management tools and as experts in data masking and security.

This isn’t just about building new features; it’s about communicating our value clearly and ensuring our clients understand that we’re here to solve their problems, now and in the future.

A Legacy of Innovation

Reflecting on this journey, I am reminded that what started as an effort to solve a personal frustration has turned into a legacy I am immensely proud of. Innovation, practicality, and a commitment to solving real-world problems have defined us from the beginning and will continue to define us in the years to come.

For more than 45 years, hundreds of Fortune 500 and Global 1000 companies across industries such as finance, healthcare, government, education, and retail have trusted us to manage and protect their data. I invite you to join us on this ongoing journey. Whether you’re navigating IMS database solutions or tackling modern challenges like data security and compliance or AI, DataVantage has the experience and tools to guide you every step of the way

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