Your organization has likely invested millions of dollars into a modern data stack, providing your team with powerful cloud warehouses and cutting-edge tools. And yet, a persistent feeling of friction often remains, a sense that getting trusted insights is far harder than it should be. The source of this friction can sometimes be misdiagnosed as isolated bugs or bad code, but the reality is far more systemic. It more often arises from a more insidious class of technical debt, one that rarely shows up in a code review but is deeply embedded in your team’s processes, architecture, and strategy.
The most critical risks to your data platform are often invisible if you are only looking for flaws in the code, says Mayank Bhola, Co-Founder and head of products at LambdaTest, an AI Native software testing platform. “The true nature of data platform debt is multifaceted, living in the outdated access workflows, the scattered business logic, the brittle security foundations, and the strategic failure to turn passive data into an active asset.” This expanded understanding requires a new diagnostic approach for every data leader, engineer, and architect.
Instead of only asking about code quality, Bhola suggests the more pressing questions should be: Where are our processes creating friction and hindering access to value? Is our platform’s architecture promoting consistency and trust, or is it allowing logic and standards to fragment? Are our foundational systems, from infrastructure to security, a resilient bedrock or a single point of failure? And most importantly, is our data strategy creating an active, appreciating asset or a passive, costly liability?
So what is the most significant or insidious tech debt you’ve encountered in a data platform? I turned to senior engineering and notable data leaders from across the industry. Their insights paint a consistent and surprising landscape of non-obvious problems, suggesting that your platform’s biggest issues are likely not where you think they are.
When workflows turn into roadblocks
The first and most common source of this hidden debt originates in the human processes that govern how people interact with the platform, creating friction that stifles the very innovation the technology was meant to enable. Mo Plassnig, Chief Product Officer at Immuta—an integrated platform for sensitive data discovery, security and access control, and data use monitoring—describes a common scenario where organizations invest heavily in modern technology but fail to update the legacy processes that control access to it.
“One of the most significant issues is moving to a modern data lake or cloud-based warehouse without evolving the culture around data accessibility within the organization. Employees are given a shiny new car with a great engine and partially self-driving capabilities, but access to the data is still controlled by old-school IT systems and ticketing processes, which can take weeks or even months. It is like having a brand-new car sitting in the garage, and every time you want to drive it, you have to ask for the keys and then wait a month to receive them.”
This highlights the critical need for leaders to audit their data access workflows in parallel with their technology upgrades, ensuring that the time-to-access for data shrinks in proportion to the investment made in the platform itself. The goal is to deliver the keys with the car, not treat them as a separate, delayed transaction.
This procedural friction moves from a source of frustration to a direct tax on innovation—a point reinforced by David Forino, Co-Founder and CTO at Quanted, who explains how these delays have a quantifiable business cost.
“The biggest tech debt is often procedural. Trialling a new data set usually requires coordination across engineering, legal, compliance, and research. A single evaluation can take weeks, causing even the most established funds to cap out at around 100 trials per year. That ceiling is purely due to limited bandwidth. So the real cost is twofold: missed opportunity and sunk engineering effort on trials that don’t have less than 25% conversion rates. As data volume grows, this becomes a compounding bottleneck.”
Leaders should therefore quantify the business cost of their internal processes, identifying the direct effect on innovation velocity and operational waste. Treating procedural improvements with the same urgency as infrastructure upgrades can unlock significant competitive advantages currently hidden within the organization.
When the blueprint itself is flawed
Another significant source of hidden debt lies in the very architecture of the platform itself. When foundational design principles are flawed, they create compounding issues that ripple through every pipeline and analysis, leading to widespread mistrust and duplicated effort. Srujan Akula, CEO of The Modern Data Company, argues that one of the most persistent architectural flaws is the failure to properly manage business logic, which becomes scattered and duplicated over time.
“The most persistent and underestimated tech debt in data platforms is logic that’s scattered and embedded in the wrong places. Business logic often ends up buried in dashboards, spreadsheets, or tucked away in pipeline code with no visibility or version control. Over time, different teams define the same metrics slightly differently, and those differences compound. You also see phantom pipelines, jobs that still run, but no one remembers why, and no one wants to be the one to turn them off. And siloed datasets emerge when teams rebuild logic in isolation because they don’t trust what’s upstream or can’t access it. At the core of all of this is the same issue: the logic that drives decisions isn’t visible, isn’t reusable, and isn’t governed. That creates friction, slows down delivery, and makes scaling things like AI far more risky than it should be.”
Akula suggests that the real lesson is that logic will fragment if it is treated like a side effect of pipelines or reports. But if it’s treated as a first-class asset with ownership, context, and structure, you can build systems that are far more durable and adaptable. This calls for a strategic shift to centralize and manage metric definitions as a core component of the platform, building a more durable and trustworthy data foundation.
This internal fragmentation is often compounded by external pressures, a point raised by Angshuman Rudra, Director of Product at TapClicks, who identifies the silent but constant issue of schema drift from uncoordinated upstream teams.
“One of the most persistent and quietly damaging forms of tech debt I’ve seen in data platforms is schema drift – especially when upstream teams make changes without coordination. As companies grow and data-producing teams move faster, this becomes a recurring problem. Take one situation I encountered: a product team added a new dimension to their data source – a new field that should have been used in downstream joins. But there was no communication about the change. The data pipelines didn’t break, but the downstream logic started to fail. Reports and models did not capture the right insight, and the issue was only caught weeks later. The root cause? The upstream team needed to move fast, and involving central data governance would’ve slowed them down – which is understandable. But the downstream consequences were missed insights and reactive firefighting.”
When this happens at scale, it quickly becomes an operational nightmare. The only sustainable solution, Rudra suggests, is finding a delicate balance between upstream velocity and centralized governance. This requires the hard, often unglamorous work of fostering “constant cross-team communication, clear data contracts, thoughtful documentation, and building a culture where data changes are treated with the same rigor as code changes,” because in fast-moving organizations, “data governance can’t be a gate. It has to be a shared commitment.”
Data platform leaders must therefore champion the implementation of clear data contracts between upstream data producers and downstream consumers. Treating data changes with the same rigor and communication protocols as code changes is essential to preventing the silent decay of data pipelines and the erosion of trust in the data they produce.
While uncontrolled change creates one set of problems, an inability to adapt creates another, especially when dealing with legacy systems, shares Ganeshkumar Palanisamy, Principal Software Architect at Reltio, reflecting on his own experience with rigid, unadaptable data models.
“At Reltio, one of the most insidious tech debts was legacy MDM systems with rigid data models that couldn’t scale for real-time use cases like Customer 360 or fraud detection. This caused slow transaction processing, delayed go-lives, and higher operational costs, directly impacting customer experience. We addressed this by overhauling the architecture to a cloud-native, multi-cloud platform (AWS, GCP, Azure), reducing transaction processing time by 300%.”
The most important lesson here is to prioritize flexible and extensible data modeling from the very beginning of any modernization initiative, particularly when migrating from legacy systems to avoid scalability bottlenecks. Deferring architectural decisions to fix rigid models only compounds business risk and ensures that the platform cannot keep pace with real-time business demands.
Cracks in the core
Even a well-designed platform with perfect processes can be crippled by weaknesses in its most fundamental layers. This foundational debt often goes unnoticed precisely because it is considered “boring infrastructure,” yet it represents a single point of failure for the entire data ecosystem. Alan DeKok, founder of the FreeRADIUS project and CEO of InkBridge Networks, provides a crucial outside perspective, arguing that the most dangerous debt is often in the security systems we take for granted.
“Companies spend millions on fancy data lakes and AI platforms, then protect them with authentication infrastructure held together with duct tape and prayer. I’ve seen Fortune 500 companies running critical data platforms behind RADIUS servers with 8-character shared secrets – essentially putting a $5 padlock on a vault containing billions of dollars’ worth of data. The business consequence? When your authentication fails at 2 a.m., your entire data platform goes dark. Your machine learning models stop training, your analytics dashboards go blank, and your data scientists can’t access anything. But because authentication is ‘boring infrastructure,’ nobody documents it properly, so when the one person who understands it leaves, you’re stuck.”
This serves as a critical reminder for data leaders to rigorously audit and invest in their authentication infrastructure with the same seriousness as their data processing frameworks. Ensuring that security foundations are strong, well-documented, and not dependent on a single person is paramount to protecting the entire data platform from catastrophic failure.
This risk of foundational failure extends beyond security to the very infrastructure the data platform is built upon, a point emphasized by Jeremiah Stone, CTO of SnapLogic, who sees outdated systems as the primary roadblock to innovation.
“As new technologies continue to advance, businesses that fail to modernize their infrastructure will find themselves at a competitive disadvantage, facing higher operational costs, decreased efficiency, and the inability to innovate. In many cases, outdated apps are completely blocking AI adoption. The open secret among CIOs is that a huge chunk of investment going into AI is being spent with service partners building modernization strategies or upgrading outdated systems. A recent head of data and analytics at a global 2000 company told me, ‘I’m sure this is valuable, but I am also sure that our data is in no condition to be useful due to the poor management of our applications over the years.'”
The clear directive for leaders is to conduct a thorough inventory of their existing IT infrastructure to identify and prioritize the modernization of legacy systems that are blocking strategic initiatives like AI. Framing these upgrades not as a cost center but as a necessary investment to unlock future value is key to securing executive buy-in for what is often the most critical prerequisite to innovation.
When your biggest asset becomes a liability
All of these processes, architectural and foundational debts, culminate in the most dangerous issue of all: strategic debt. This occurs when the data itself, the very asset the platform was built to manage, ceases to be a source of value and instead becomes a costly liability due to a failure of strategic vision. Jared Peterson, Senior Vice President of Platform Engineering at SAS, reframes the entire concept of data debt away from technical problems and toward the massive opportunity cost of failing to activate these assets.
“In the context of a data platform, underutilized data is the most insidious tech debt. Organizations often have TONS of data, but they don’t know where it all is, or it’s locked up in legacy formats, not rationalized or well understood. That leaves you with unrealized, unrecognized competitive value, just sitting there.”
Data leaders should shift from merely storing data to actively mapping and understanding their data estate as a portfolio of competitive assets. Organizations must invest in discovery and cataloging, not just for governance, but also to continuously identify and unlock the unrecognized value sitting dormant in their systems.
This concept of dormant value is taken a step further by Amit Saxena, VP and General Manager of Workflow Data Fabric at ServiceNow, who argues that when data isn’t connected to action, it becomes worse than useless.
“A common misstep in data modernization is treating centralization as the finish line. It’s not. Aggregating data can provide insight, but orchestration is transformational. Real value is found when data is connected to context, accessible in real time, and able to move across systems, workflows, and decision points, regardless of its source system. If your data just sits in a warehouse without driving action, you’ve built a liability, not a platform.”
Therefore, the strategic mandate is to design data platforms not as passive repositories for analysis but as active engines for orchestration and action. Leaders must prioritize investments that connect data directly into the business workflows and decision points where it can generate tangible value and transform operations.
Always treat data as a product
From his executive purview at LambdaTest, Bhola stresses that the most effective way to prevent these debts is to adopt a product management mindset for data itself.
“We must stop treating data as a byproduct of business operations and start treating it as a product in its own right. Like any good product, data should have a dedicated owner, clear versioning, documented APIs for access, and be designed to serve the needs of its consumers. When you adopt a product thinking approach, issues like quality, accessibility, and reliability are addressed by design.”
Adopting a “data as a product” strategy fundamentally realigns an organization’s priorities. It forces teams to consider who their data consumers are and to build trustworthy, easy-to-find, and simple-to-use products, naturally mitigating the root causes of process, architectural, and strategic debt.
And to this end, the journey from identifying hidden processes and architectural debts to challenging the dogma that creates them leads to a new, strategic vision for data platform leadership, Bhola underscores. “It requires moving beyond managing technology to orchestrating a value-generating ecosystem. The core challenge, therefore, is not simply to modernize a tech stack, but to modernize a mindset, ensuring the platform can adapt and thrive in the face of future innovation.”
