Written by: Ben DeBow | Founder and CEO of Fortified | Author of End of Abundance in Tech
Blog Series: The Evolution of Technology’s Role in Business.
In this installment of The Evolution of Technology’s Role in Business, Ben DeBow shines a light on an emerging paradox: while data has become the lifeblood of modern business, the professionals who manage, protect, and optimize it are being increasingly sidelined.
Ben explores how organizations have deprioritized roles like DBAs, data engineers, and analysts, leading to costly inefficiencies and missed opportunities for optimization. Through a compelling analysis of real-world scenarios, he makes the case for reinvesting in data professionals to strengthen the foundation of business innovation, improve system efficiency, and unlock the full potential of data as a strategic asset.
Introduction
When I started working in data nearly 30 years ago, the term “data” barely resonated outside the walls of technology teams. Most people didn’t fully grasp what data was or why it mattered. Fast forward to today, and data has become the lifeblood of modern business. It’s often referred to as the “new currency” or the “new oil,” and for good reason. Data powers everything—our servers process it, our storage systems house it, and our networks transfer it. It’s the foundation of Artificial Intelligence (AI), fueling models that are shaping industries and redefining innovation. Without data, we wouldn’t need any technology at all.
Yet, amidst this data explosion, many organizations are quietly scaling back on one of their most critical assets: the trained professionals who manage, understand, and protect that data. The DBAs, data engineers, and data analysts—once the gatekeepers of data integrity and architecture—are seeing their roles minimized or even eliminated. And the consequences of this shift are becoming increasingly clear.
How We Got Here
In the early days of my career, database teams were the guardians of the data ecosystem. They owned the data models, reviewed and approved code, and were integral to architectural decisions for new applications. Back then, data ecosystems were relatively small. There was less data to store, fewer applications to support, and far fewer transactions to process.
Fast forward to today, and everything has changed. We are generating and storing more data than ever, with the global volume growing at an astonishing 23% CAGR. Transactions are exploding year over year, and businesses are under immense pressure to implement AI and Machine Learning (ML) faster than ever before. Nearly every company is a tech company at its core. And yet, while the demand for data-driven decision-making has increased, the resources dedicated to managing and optimizing data have not kept pace.
The Erosion of the Data Professional
Today’s DBAs have been relegated to reactive roles—focused primarily on keeping systems running, responding to issues, and patching servers. They’re the ones who get the call when something breaks, and they bear the brunt of the pressure to fix it fast. Yet, their responsibilities have been stripped down over the years. They no longer own the data model, the application code, or even the architectural decisions that shape the system’s health. How can DBAs be held accountable for the health, capacity, and performance of systems when they aren’t even empowered to reject poorly designed schemas or code?
Here’s a fictional but all-too-common example: A successful SaaS company wanted to ensure their application could run on both Oracle and SQL Server, so they decided to design their own globally unique identifiers (GUIDs). To make the solution “future-proof,” they implemented GUIDs as a decimal with 23 characters in length, which took up 13 bytes of storage per record. What they didn’t realize was that reducing the length by just one character would have reduced the storage to 9 bytes—a 30% savings per record.
This decision had far-reaching consequences. Since the GUID was used as the primary key, it was also replicated as a foreign key in every related table, compounding the storage and memory efficiency. For a SaaS company storing billions of rows across thousands of clients, the storage, compute and memory consumption grew at higher rates. Worse still, the larger data footprint degraded performance and increased system capacity requirements, resulting in higher infrastructure costs for all the clients. Unfortunately, clients continue to bear the cost of a “resource tax” imposed by running poorly designed third-party applications in their environments.
Had a DBA been involved in the design process, they would have identified this issue early. They could have recommended a more efficient data type or structure, saving tens of millions of dollars in storage costs, improving performance, and extending the capacity of the system. Instead, the lack of input from trained data professionals led to a suboptimal design with a higher total cost of ownership (TCO) over the lifetime of the application. This application just had its 25th birthday recently.
This example illustrates the risks of sidelining DBAs and other data experts. Their expertise is critical not just for maintaining systems but for ensuring that foundational decisions—like schema design and code patterns—support long-term scalability, efficiency, and performance. Yet, as their roles are diminished, companies unknowingly open themselves to technical debt and inefficiencies that can cripple their systems in the long run.
Why We Must Reinvest in Data Professionals
It’s time to rethink how we leverage our data professionals. DBAs, data engineers, and analysts should be elevated and empowered to participate in the design, building, and stewardship of data ecosystems. These individuals are not just maintenance workers—they are architects of data integrity, efficiency, innovation and have the keys to help unlock the value of the data for the business.
When we involve them in foundational tasks like schema design, code optimization, and data governance, we create a ripple effect. A well-designed schema doesn’t just improve performance; it enhances code patterns, reduces resource consumption, and ensures data integrity. It also supports better analytics, stronger data governance, and a healthier data ecosystem overall.
The long-term benefits are immense. By strengthening the foundation of our data, we reduce the total cost of ownership (TCO) for our technology systems, accelerate the pace of innovation, and unlock the full value of data as a strategic asset. In a world where data is the cornerstone of competitive advantage, investing in the people who know and manage it best is not just a good idea—it’s a necessity.
It’s time to stop sidelining the stewards of data and start empowering them. Our businesses—and our future—depend on it.