5 Critical DevOps Bottlenecks Caused by Traditional Test Data Practices

As enterprises scale their DevOps initiatives, DevOps bottlenecks are emerging from an unexpected source: inadequate test data management practices. But what can you do about it?

In this blog, we will explore the common drawbacks in traditional test data management practices. Then, let’s dive into how real-life enterprises are overcoming DevOps bottlenecks with proven solutions that accelerate delivery and reduce operational risks.

How Test Data Creates DevOps Bottlenecks in Modern Development

Traditional test data management methods are becoming increasingly problematic for many businesses. They're opening organizations up to legal, security, and operational risks.

As a result, test data management is evolving rapidly and playing a more significant role in enterprise software development. 

According to the DevOps Research and Assessment program, successful teams approach test data management with the following core principles:

1. Adequate test data is available to run full automated test suites.
2. Test data for automated test suites can be acquired on demand.
3. Test data does not limit or constrain the automated tests that teams can run.

5 DevOps Bottlenecks Caused by Traditional Test Data Management

In the companies we work with, there are (at least) five ways that traditional practices for managing test data hold development teams back. These bottlenecks don’t just slow teams down and affect developer productivity. They have significant impacts on software quality, compromise regulatory compliance, and even introduce security risks.

Let’s examine each test data bottleneck in greater detail. 

1. Slow Test Data Provisioning 

The Problem: Manual Processes Create Extended Delays

Traditional processes are often manual or poorly automated. They begin with copying data from production databases into non-production environments. Sensitive data is secured through customized masking or obfuscation scripts, but these approaches leave security gaps and make it difficult to triage production defects. 

Business Impact

As data volumes have increased, the time needed for these processes has grown for many companies. Development pipelines are often halted for days or weeks for test data provisioning or to set up test environments. DevOps and CI/CD practices are incompatible with such delays.

Solution Approach: Automation Accelerates Provisioning

To accelerate provisioning and overcome these bottlenecks, teams need to automate test data management steps and integrate them with existing toolchains such as GitHub, Jenkins, and Terraform. Automating eliminates manual labor and speeds up provisioning significantly.

Proven Real-World Results with Perforce Delphix

According to an IDC study* of Perforce Delphix customers across multiple verticals, teams that automate their test data management with Delphix are 58% faster to develop a new application. This is crucial not only throughout sprint cycles but also during defect analysis and root cause analysis . With Delphix, developers get fast, self-service access to test data, eliminating the DevOps bottleneck with no need to delay testing for days, weeks, and months while data is provisioned or refreshed.

2. Running Afoul of Compliance Mandates

The Problem: Rising Risk of Data Breaches in Non-Production Environments

Data breaches involving non-production environments are on the rise. In fact, according to our upcoming 2025 State of Data Compliance and Security Report, 60% of organizations have experienced breaches or theft in non-production. That’s up 11% from last year.

Non-production environments are more vulnerable than others because they often go unprotected. This creates more opportunities for theft, regulatory violations, and operational risk.

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Business Impact: Protecting Sensitive Data Across All Environments

No matter where it resides, sensitive data must be protected to comply with various laws (like GDPR, HIPAA, CCPA) and industry standards (like ISO 27001:2022, PCI DSS) and to mitigate legal and financial risks. 

Solution Approach: Data Masking and Synthetic Data Generation

One of the most effective ways to protect sensitive data is to eliminate it from non-production environments altogether. Data masking or synthetic data generation are two methods for doing so. However, it’s not enough to simply mask sensitive data. You need masking with referential integrity across multiple masked test datasets.

Delphix Real-World Implementation: Worldpay's Approach

Using data masking to protect sensitive data in non-production environments is standard practice at Worldpay. Arvind Anandam, Director of Platform Engineering at Worldpay, shared:

"We've got one production environment, but we've got about anywhere between 12 to 20 non-production environments. We do have a very robust masking process that we have put in place. We do a lot of checks to ensure that no production data accidentally slips through into non-production. Masking is the center of everything that we do."

For example, you may have three databases with sensitive data that are used by an integrated system. Independently masking those databases in your non-production environments may destroy the links between them, breaking your system integration testing. 

According to the IDC study, organizations using Delphix achieved 77% more data and data environments masked and protected, demonstrating the effectiveness of comprehensive masking solutions that maintain referential integrity.

3. Compromising the Quality of Testing

The Problem: Irregular Data Refresh Cycles Undermine Testing

Due to the effort and complexity of providing test data to developers, many enterprises do so very infrequently. It is not uncommon for teams to refresh test data only once or twice per year. Some teams instead rely on partial sets of data, but those are often refreshed infrequently. 

Business Impact

Incomplete or outdated data often leads to poor test results. Both false positives (defects that aren't real) and false negatives (real defects that are missed) are common outcomes of poor test data.

Solution Approach: Automated Solutions for Fresh Test Data

With the proper technology, teams can easily establish weekly, daily, or even near-real-time test data updates that are created from full production datasets. Important capabilities include automated data synchronization, sensitive data discovery, and automated masking. The solution must also synchronize schema changes so that application code can be tested against current database designs.

Some enterprises use other solutions, like synthetic data, in combination with data masking for additional flexibility. 

Delphix Real-World Results

Establishing strong test data environments is key to successful software development. Research from IDC highlights the power of automation in this area. According to that same IDC study, Delphix helps teams automate testing environments, cutting operational times by giving faster, easier access to data. After adoption, software development teams experienced an average 53% reduction in the days needed to build usable testing environments.

4. Eating Into the Application Development Budget

The Problem: Infrastructure Costs Spiral Out of Control

In our experience, companies often spend more on non-production environments (software, services, and hardware) than they do on their actual production systems, whether these are on-premises or in the cloud. Consider the infrastructure footprint of your unit, user acceptance and system integration test environments — does it exceed that of your production systems?

Business Impact

This forces many organizations to share environments, significantly slowing down development and testing. During these delays, teams are left working on tasks that do not move the release forward, resulting in substantial time loss. A majority of these non-production systems typically serve development and testing purposes. 

This allocation of resources to non-production environments not only increases the fully burdened costs of your applications; It also robs your business of resources that could be better spent on innovation and productivity.

Solution Approach: Infrastructure-as-Code and Data-as-Code

An infrastructure-as-code approach takes advantage of more recent technical advances, such as public cloud elasticity, containerization, and orchestration to expedite development environments and reduce costs. They take advantage of the ephemeral nature of development environments (e.g., the fact that they're not needed 100% of the time and can be "torn down" when not in use) to get more out of their infrastructure dollars. Similarly, teams should utilize a "data-as-code" approach that provides test datasets on demand and destroys them when not needed.

Quantifiable Savings from Implementing Delphix Database Virtualization

Delphix database virtualization allows teams to spin up virtual test datasets directly from their pipelines and destroy them as soon as software is delivered. These virtual datasets consume a trivial amount of storage and can be provisioned in minutes instead of days, weeks, or months. Additionally, Delphix self-service capabilities eliminate bottlenecks by allowing developers and testers to execute database refreshes within their own environments without waiting on other teams or impacting parallel workstreams. This accelerates development timelines and reduces cross-team dependencies.

Together, these capabilities lead to substantial savings. A large enterprise we’ve worked with recently had massive development environments that racked up huge cloud costs. By implementing Delphix, they gained cost-efficient, compliant data environments. That allowed them to reduce cloud compute and storage run costs by roughly £2 million annually. 

They’re not the only ones who have been able to find savings by using Delphix. According to the IDC study, Delphix's customers generated $8.4 million in additional revenue from improved software development productivity. 

5. Halting Innovation with Rigid Test Data Processes 

The Problem: Rigid, Outdated Processes Stall Innovation

Innovation stalls and your competitive advantage is compromised if you can’t scale. This inability to scale is particularly evident when rigid, outdated test data processes are at play. Test data serves many different use cases. It helps developers build and test their own code prior to any user acceptance or system integration testing. It also helps quality engineers test software during each phase of testing, including security testing. Engineers often run hundreds or thousands of automated tests for these use cases, which are best served by having the same data during each run. 

Business Impact

Since tests are run both parallel (many tests at once) and sequentially, duplicate test data sets need to be available, and they must be rewound to their starting state for subsequent sequential tests. Unfortunately, many test data management approaches lack this capability, or they are very difficult to manage.

Solution Approach: Automation to Simplify Complex Scenarios

Luckily, automation can help. Advanced solutions should provide the ability to bookmark and rewind test data, create test data sets to run tests in parallel, and support schema changes for different versions of software testing.

Delphix for On-Demand Refresh Capabilities

With Delphix, you can bookmark and rewind test data to a particular point in time. Sharing these bookmarks across teams helps reduce defect triage time by enabling consistent reproduction of issues. Additionally, bookmarks support rapid test iteration, as QA can quickly reset the database to its exact state before the previous test case was executed.

The ability to refresh data on demand allows teams to test with the latest data and schemas. Having fresh data helps improve the effectiveness of software testing at each stage and shifts left quality. This results in fewer defects making their way into production. 

In the IDC study, participants summarized the most significant data testing benefits of the platform. One customer from the hospitality industry said, “Fewer defects are going into production because QA staff can now test against a full-size production database without using real data. This leads to better testing results, as they get more accurate, production-like data instead of fabricated entries like 'Mickey Mouse at 123 Walt Disney Square.'"

Don't Let Test Data Management Bottlenecks Derail Your DevOps Success

Is your test data strategy driving your organization forward? Or is it creating DevOps bottlenecks? 

Ask yourself: 

• Is your test data provisioning integrated into CI/CD pipelines? 

• Can your developers spin up masked datasets in minutes instead of days? 

• Does your approach support parallel testing and rapid iterations? 

Get four actionable checklists to eliminate common test data management bottlenecks. Transform your test data practices from a DevOps bottleneck into a competitive advantage. 

Get Your Test Data Under Control

3 Practical Examples from Real Delphix Customer Implementations

Now that we've explored the key bottlenecks, let's see how real enterprises have successfully addressed these challenges in practice. These examples demonstrate how organizations across different industries have transformed their test data management to eliminate DevOps bottlenecks.

Choice Hotels

Choice Hotels needed a solution that would speed up development cycles while still protecting sensitive guest information. By implementing Delphix's data virtualization and masking platform, they achieved 90% faster database refreshes, reducing a two-week process to less than a day while significantly reducing storage costs.

Gain Capital

Gain Capital struggled with data delivery bottlenecks hindering continuous integration. With eight simultaneous projects, delivering data environments took up to four hours each. After adopting Delphix, they reduced database copy creation from four hours to three minutes, resulting in 75% faster development cycles and 20% increase in business output.

Mattel

Mattel faced growing pressure to modernize its legacy systems to support rapid business transformation. Their legacy ERP and warehouse management systems were creating bottlenecks with 2-5 day environment refreshes. Through Delphix's data virtualization platform, they reduced refresh times to 4-8 hours, achieved a 600-800% reduction in person-hours for environment support, and cut costs by reducing storage by 67% through virtualized data.

Eliminate DevOps Bottlenecks with Next-Generation Test Data Management from Perforce Delphix

The five DevOps bottlenecks we've explored (slow provisioning, compliance risks, compromised testing quality, budget drain, and limited scalability) all stem from outdated test data management practices. These challenges don't just slow teams down. They fundamentally compromise your organization's ability to compete in today's fast-paced digital landscape.

Transform Application Delivery with Automated, Compliant Data

The solution lies in embracing a modern approach like Delphix and leveraging advanced technologies for automated data virtualization, integrated data masking, self-service capabilities, and API-driven workflows. These capabilities work together to deliver compliant test data at scale while accelerating development velocity. 

Achieve Faster Innovation and Strategic Business Outcomes

By addressing these issues through test data automation, businesses can accelerate application releases, boost developer productivity, ensure data privacy and security compliance, and reduce costs through efficient virtualization. Ultimately, effective test data management becomes a strategic asset that enables high-performance software delivery and empowers organizations to stay competitive in today's fast-paced digital landscape.

The evidence is clear. Organizations that modernize their test data management see measurable improvements in development speed, cost reduction, and software quality. The question isn't whether you can afford to upgrade your test data practices — it's whether you can afford not to.

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Eliminate DevOps Bottlenecks

This blog was originally written by Ugo Polio and was updated by Corey Brune.

*IDC Business Value White Paper, sponsored by Delphix, by Perforce, The Business Value of Delphix, #US52560824, December 2024