Data Platform Modernization: Essentials for Data Leaders

Clive Humby’s claim that “data is the new oil” has sparked debate, but from a data management perspective, it’s proving to be eerily accurate—though perhaps not exactly as Humby envisioned.

Across industries, data leaders face growing challenges in steering their organizations as the sheer volume of data surges alongside escalating stakeholder demands to optimize its use. Advances in AI, machine learning, and global digital transformation have further compounded these challenges, driven by the proliferation of smart devices, IoT technologies, and a growing consumer appetite for digital media.

In this environment, “big data” has given way to what can only be described as a “data boom.”

Yet, much like during North America’s early 20th-century oil boom, organizations struggle to identify valuable resources and harness them effectively. Many data leaders now stand at this crossroads, aware—or at least suspicious—of the value of data piling up beneath them while being blocked from accessing its value by the very data platforms they’re entrusted to support.

Meanwhile, competitors are gaining ground and turning data into a competitive advantage. They leverage data quality to fuel real-time insights, enable advanced analytics, and accelerate revenue growth.

Fortunately, widening cracks in the viability of traditional data platforms often allow data professionals to identify and analyze weaknesses more easily. This means that today’s data leaders face a clear message: modernizing your data platform is no longer optional—it’s essential.

However, data leaders must understand their current position to determine their next steps. In this sense, modernization isn’t just about adopting new tools—it also requires rethinking how we manage, govern, and leverage data to unlock business value.

This article launches a three-part series on data platform modernization. First, as leaders, we must confront two converging factors that are currently driving a critical shift in data engineering.

We can then explore data platform maturity models—pros, cons, application, and best practices—and, finally, practical data platform strategies that leaders can use to make the changes they need to modernize their data platforms and unleash the true value of their organizational data.

Right intentions, wrong lessons: Correcting for our software-inspired past

First, let’s address the operative elephant in the room: Traditional legacy software engineering models can no longer meet the demands of modern data quality management—or the professionals responsible for it—especially in industries where success hinges on access to high-quality data. Today’s modern data ecosystems demand scalability, agility, and seamless integration with cloud services to manage growing data volumes and increasing complexity.

In the early days of our industry, modeling emerging data management processes after proven software engineering principles made sense. It was clear that placing speed, rapid iteration, and product development in relative isolation drove success in software development. But now, these same practices are holding data professionals back instead of empowering them as their forebears envisioned.

This experiment is over, and after nearly two decades of practical experience, we have an abundance of evidence showing that a software-centric mindset conflicts with the demands of data-dependent market demands. Today’s data management frameworks require a steadfast focus on fostering trust, ensuring consistency, and driving cross-functional collaboration across platforms like data warehouses and data lakes.

These requirements increasingly run counter to the needs of data management and managers. As a matter of principle, software engineering teams often utilize data with little or no understanding of that data’s provenance or lineage. The need for agility and speed takes precedence for the software engineer who, proverbially, benefits by moving fast and breaking things. Software professionals can thrive in this sense because their work undergoes rigorous quality checks during development. But data products do not.

In modern data environments, this lack of context quickly becomes dangerous—resulting in poor data quality, siloed workflows, and fragmented systems. This, in turn, undermines efforts to implement advanced analytics and machine learning solutions. In fact, compartmentalization in this way can poison an entire data environment, as it takes only a single upstream change by an isolated engineer to trigger massive downstream failures, breaking dashboards, disrupting operations, and eroding trust in data. This is now a simple reality of modern data management: real-time data environments require seamless collaboration to maintain integrity and ensure cost-effective operations.

In short, software development can—and sometimes should—involve moving fast and breaking things. But data can’t afford to break. To support a resilient and scalable data ecosystem, it’s time to rethink how we manage it. 

Data access vs. data quality: Why publicly available data is no longer enough

The second issue driving the urgency for data platform modernization is the declining reliability of public data sources. 

Historically, organizations supplemented their internal data with publicly available datasets to fuel analytics and machine learning models. However, this strategy is failing as the public internet continues to buckle under ad-driven incentivization models—prioritizing clicks over quality for decades now. 

To this ongoing assault on quality information, companies continue to take the path of least resistance and are now flooding the internet with derivative AI-generated content, further diluting the utility and trustworthiness of publicly available data. Generative AI content in particular is now, ironically, specifically problematic to Generative AI companies. We now know that using content created by older models to train newer models creates a net-negative, self-reinforcing loop—mirroring mad cow disease—for large language models. This leads to stagnant, average outputs that drag innovation and utility into a downward spiral.

Together, these shifts pose serious challenges for data-driven organizations. Public data can no longer serve as a cornerstone for innovation. The most valuable data is now proprietary, high-quality data, made up of unique insights that companies collect directly from their products, services, and customer interactions. 

This is the proverbial crude oil pooling beneath nearly every modern organization. Unlike public datasets, this internal data cannot be replicated by competitors since it holds the key to unlocking competitive advantages, which makes it even more valuable.

However, simply possessing proprietary data isn’t enough. Oil stuck underground is oil that doesn’t spend. Likewise, most organizations struggle to fully leverage their existing data due to fragmented systems, poor data quality, and weak governance. Without the right infrastructure and processes, this valuable data remains underutilized. Companies must instead invest in modern data platforms that can securely manage, transform, and scale proprietary data to drive business outcomes.

This is why, beyond stepping out from the shadow of traditional software engineering practices, data leaders must focus on building systems that treat data as a strategic product. This shift requires confronting the challenges of modernizing organizational data platforms, such as implementing robust data governance, enforcing quality standards, and ensuring seamless integration across the data ecosystem. Emergent tools like data contracts must also strengthen accountability between data producers and consumers to prevent disastrous downstream failures.

These sweeping changes in the data landscape are disrupting the old guard at a sobering pace. Organizations that prioritize modernizing their data platforms and managing proprietary data effectively will clearly lead to innovation. But those that are still relying on public datasets risk falling behind in a market that increasingly values exclusive, high-integrity data.

In a global economy where data-driven decision-making defines market leaders, the path forward is clear: Companies must modernize their data platforms to secure, govern, and leverage proprietary data—the fuel for their next wave of growth. 

Data platform modernization as a modern business imperative, not data engineering best practice

Fortunately, the path forward for data leaders and their organizations depends on a shift in mindset, not on some yet-to-be-developed technology. Specifically, data leaders must either invest in building a modern data platform or champion efforts to modernize existing systems.

When building from the ground up, companies must prioritize modern data architecture by adopting scalable cloud platforms that support real-time processing, integrating data across systems, and enabling advanced analytics. This approach goes beyond upgrading tools—it also demands a flexible, future-proof infrastructure that adapts to evolving business needs.

Effective data management is central to this modernization. Organizations must establish consistent processes for data transformation, quality control, and secure storage to ensure that data stays accurate, accessible, and actionable across the business. Additionally, data leaders need to champion organizational transitions from siloed systems to unified, cloud-based platforms—streamlining data migration, consolidating scattered datasets, and unlocking deeper insights in the process.

In practice, this can prove exceptionally difficult. That is why data leadership must spearhead these organizational reformations, as stakeholders may not fully understand that modernization goes beyond a technological changing of the guard. Data governance and quality controls are essential at every stage of the process. 

As part of these vital changes, implementing a self-service data platform empowers business teams to access and analyze data without relying solely on engineering teams. Automation not only enhances efficiency but also minimizes human error, ensuring consistent and reliable data. By integrating intuitive analytics tools and automating routine data processes, organizations eliminate bottlenecks and accelerate decision-making. 

Ultimately, data leaders must clarify that unlocking business value requires more than infrastructure and governance. The entire organization must embrace the shift toward shared data ownership, which necessitates involving data engineers in quality and governance efforts from the start. 

Only with this mindset can companies advance from descriptive analytics to predictive and prescriptive insights, driving innovation and directly fueling revenue growth.

Tackling modernization modeling as the essential first step

Like oil, which gains value only after refinement and distribution, enterprise data is valuable only when supported by the right infrastructure for access and use. For data leaders, the call to modernize is clear: Optimizing data platforms is no longer just a technical upgrade—it’s a strategic imperative for driving real-time insights and unlocking competitive advantages.

But modernization is only the first step in this transformation journey. To truly maximize the impact of their data initiatives, organizations need a clear roadmap to assess where they stand and where they need to go. Doing so ensures that they can benefit from their data as a product—holistically, sustainably, and ethically.

In our next article, we’ll explore data platform maturity models and how they can guide your organization in building a scalable, future-proof data infrastructure. Join us as we dive deeper into how data leaders can use data platform maturing modeling to clarify their path forward and take actionable steps toward shifting data left.