It is 2025 — a year destined to be marked by AI and quantum computing breakthroughs. Yet, one of the most persistent challenges facing enterprises remains the same: Data. Some constant struggles I still see in almost every enterprise are lack of data availability and difficulty in complying with data regulation. The common denominator? Outdated test data practices continue to hold back organizations, creating bottlenecks in delivery and compromising software quality.
Why Should Enterprises Care?
Put simply, outdated test data practices are detrimental to your productivity, competitiveness and reputation.
Last year, Curiosity Software innovated the notion that software delivery is constructed of an inner and an outer loop. The idea highlights a critical gap: While most organizations heavily invest in their inner loop — development processes — the outer loop is often neglected. This outer loop, encompassing requirements, testing and data, plays a pivotal role in ensuring successful and efficient delivery.
One of the biggest culprits in this is test data. When test data is unavailable, insufficient or misunderstood, it creates a ripple effect:
- Quality suffers — teams cannot thoroughly define, develop and test critical scenarios.
- Productivity plummets — developers and testers waste time searching for, waiting on or manually manipulating the data they need.
- Agility is lost — teams want to develop quickly and not wait for other teams. Having test data available immediately allows teams to work in parallel.
The result? Delays, inefficiencies and a fragmented delivery pipeline — something modern enterprises cannot afford to ignore in today’s landscape where virtually every organization competes on software.
A fourth and critical consequence has recently emerged: Failure to adopt AI — something entirely dependent on having high-quality, unified, well-understood and accessible data.
What are the New Challenges of Test Data?
Test data isn’t a new industry —it has been around since the early 1980s. My co-founder, Huw Price, has founded five test data companies (yes, you read that correctly — five!), each addressing a unique technological need as the industry evolved. Over the past four decades, technology has transformed radically. We have moved from Mainframe copybooks to relational databases and NoSQL and now operate in a world dominated by APIs. Today, data is transmitted via complex message payloads where backend datastores are often black boxes to the teams that consume their data.
The rise of AI has introduced yet another wave of complexity, creating new challenges, demands and, the scary word, regulation. The simple truth? The old world of test data practices no longer meets the needs of modern technology and enterprise demands.
Here are the key challenges we see enterprises facing going into 2025:
1. Data is Bigger Than Ever
Data volumes are growing exponentially, with organizations often managing billions of rows of data. These massive datasets are becoming harder to process, understand, manage and provision effectively. Traditional test data tools simply can’t handle the scale and complexity promptly.
2. Increasingly Complex Application Landscapes
A typical enterprise today runs hundreds — if not thousands — of applications across hybrid ecosystems, including SaaS platforms, legacy systems, NoSQL and SQL databases and APIs. This fragmented landscape makes understanding, managing and governing test data a monumental challenge. This puts organizations at risk for costly security and compliance breaches, quality issues and release delays.
3. Unifying Data to Enable Successful AI Adoption
AI thrives on clean, unified datasets, but most organizations struggle to bring disparate data sources together in a compliant, usable way. Without unified data streams, businesses risk falling behind in their AI initiatives.
4. Meeting Compliance and Regulatory Standards
The growing web of data privacy regulations — such as GDPR, CCPA and the emerging AI Act — requires organizations to not only protect sensitive data but also govern how it is is used. Compliance failures are costly, both financially and reputationally, making it essential for test data solutions to meet stringent regulatory requirements and auto-detect new potentially sensitive data as it comes onstream.
5. Reactive Centers of Excellence (CoE)
Test data teams often operate reactively, firefighting as they are parachuted into projects. Working within silos, they must get up to speed quickly while navigating fragmented information and unclear business terminology. This constant reactive state limits their ability to drive proactive, high-value solutions.
6. Tightened Budgets, Heightened Expectations
The era of ‘growth at all costs’ is over. Organizations are under pressure to achieve a higher ROI with leaner teams and reduced budgets. Capital is increasingly being reallocated from traditional software projects to AI-driven initiatives, reflecting a shift in priorities toward innovation. Test data teams are facing the impossible challenge of adapting to these changes, proving their value while doing more with less, and balancing resources to meet risk reduction, productivity and delivery quality goals.
7. Increased Demand
AI has accelerated development processes, enabling teams to write and deliver code faster than ever before. The downside to this is it puts enormous strain on the rest of the delivery (the outer loop). This speed creates a critical demand for high-quality test data to be delivered efficiently and ahead of time. Without this, bottlenecks emerge, resulting in slow delivery times and compromised quality.
8. The Human Factor
Test data often requires deep expertise — not only in specific industries (e.g., payments standards like SWIFT or ISO 20022) but also in foundational test data processes. Unfortunately, central test data teams often lack sufficient training, resources or support to fully leverage modern platforms. As such, these teams often struggle to create demand and build the case for more projects, as it is hard to demonstrate the value being delivered.
Organizations must understand that simply establishing a test data service isn’t enough. Like running a business, test data teams need internal marketing. This includes educating stakeholders, enabling teams and consistently proving ROI to attract projects and ensure the service becomes an indispensable part of the organization’s delivery pipeline.
The New World of Enterprise Test Data
What does all this mean? Put simply, modern test data practices are no longer just about provisioning datasets or anonymizing sensitive information. Teams must work faster, smarter and with confidence. Here are the key capabilities test data teams need to thrive in 2025 and beyond.
Comprehensive Data Discovery and Cataloging
Enterprises can no longer afford to operate without a clear understanding of their data and application landscape. Visibility into data and application landscapes is essential for cataloging, tracking and auditing data across diverse ecosystems. Without this, creating reliable test environments becomes nearly impossible.
Proactive Test Data Creation
Defining data requirements early in the development lifecycle eliminates bottlenecks. Techniques like visual modeling help teams map business requirements to actionable data requests. This proactive approach also means teams spend less time firefighting, and more time driving high value outcomes.
Holistic Data Coverage
Generating comprehensive test data ensures scenarios — including edge and negative cases — are automatically created. Enterprises must move away from the tendency to rely on production data — which typically covers only 20% of requirement variations — as this directly impacts software quality.
AI-Powered Efficiencies
Automating understanding of your estate (your data) using interactive AI allows you to explore, enhance and iterate, to prevent and remove bottlenecks in software development. If you can understand where you are (data at rest), define where you are going (a process) and validate (test data validation) in a fraction of the time, then you can truly harness the power of AI.
Centralized Infrastructure for Test Data Teams
Centralizing test data processes across fragmented systems reduces operational overhead and ensures consistency and scalability. Gather up all your current processes, enhance and add state-of-the-art data activities and expose them via APIs, dynamic models and portals.
Monitoring and Governance
Real-time insights into compliance, data usage and schema and API changes help teams mitigate risks, prove ROI and enable proactive governance.
ROI Demonstration
Dashboards showcasing efficiency gains, cost savings and delivery acceleration make the case for continued investment in test data solutions.
Closing the Gap Between Data Needs and Delivery
The shift to modern test data practices is more than an operational upgrade — it is a strategic initiative. As enterprises navigate increasingly complex challenges, outdated approaches can no longer keep pace. High-quality, well-understood and accessible test data is not just a nice to have, it is a crucial component in an organization’s ability to adopt AI.
Enterprises must invest appropriately in outer loop activities to meet rising software quality and delivery expectations and future-proof their delivery process. Neglecting the outer loop often results in unavailable or misunderstood test data, which leads to quality gaps, delays and a fragmented pipeline — ultimately impeding the agility modern businesses require. Insufficient test data also undercuts AI adoption by failing to provide the high-quality, unified and accessible data it needs. By adopting a step-by-step approach to seamless, secure and intelligent test data solutions, organizations can bridge the gap between data needs and delivery demands, positioning themselves to thrive in a fast-evolving, data-centric and AI-driven world.
