Traditionally, data engineers have played a caretaking role, ensuring data is properly cleaned, stored, and managed. But as organizations scale AI and machine learning initiatives, these routine tasks become a barrier to innovation. Data engineers are often spending up to 70-80% of their time on tasks that don’t directly contribute to business value or AI model development. This inefficient allocation of time creates a disconnect between investment in data management and the ability to drive meaningful business outcomes.

The issue is not a lack of data or technology, but an outdated approach to data engineering. To truly leverage AI’s potential, businesses must shift their focus from simply processing data to activating it for growth.

The Strategic Shift: Data Products as the Solution

A promising solution to this challenge is treating data as a product. By doing so, organizations can automate and standardize tasks that currently take up much of the data engineers’ time. Data products are not just raw data; they are fully formed assets with built-in governance, quality metrics, and clear context for their use. This approach allows business teams to use data more quickly and efficiently, whether for building AI models, creating dashboards, or developing new applications.

Shifting to a productized approach ensures that data is consistently high-quality and governed, reducing the need for time-consuming data cleaning. This frees data engineers to focus on strategic tasks, such as data architecture, optimizing AI solutions, and driving innovation.

Accelerating AI Development with Data Products

The move to data products is especially important for AI development. Traditional data bottlenecks, like the time-consuming process of preparing data, often slow down AI initiatives. With data products that provide clean, high-quality data ready for AI model training, organizations can accelerate their AI efforts. Data engineers can shift from preparing data to optimizing models, creating more efficient and scalable AI solutions.

Data products also address common concerns around data governance, trust, and security. These are critical factors in AI adoption, and by building governance into the data products, companies can ensure their AI models are trained on reliable, secure, and compliant data. This not only speeds up deployment but also reduces risks associated with poor-quality data.

Learning from Software Engineering: Agile Practices in Data Engineering

The evolution of data engineering mirrors the transformation in software development. Just as software engineers have embraced agile methodologies and reusable components, data engineering must adopt similar practices. By treating data like software—with reusable, well-documented, and maintained data products—data engineers can transition from a project-based mindset to a more ongoing, product-focused approach.

This shift also transforms the role of the data engineer. As data products become the norm, data engineers will take on a new role as “Data Product Owners.” These engineers will manage the entire lifecycle of data products, ensuring they are updated and refined to meet business needs. This includes integrating new data, adding governance features, and iterating on products as requirements change.

A Future Focused on Data-Driven Business Value

The future of data engineering is shifting from maintaining infrastructure to driving business transformation. As AI and machine learning become central to business strategy, organizations need their data engineers to focus on innovation, not just data maintenance. Data engineers will be empowered to develop data products, optimize AI solutions, and create measurable business value.

By adopting the data product approach, organizations can streamline their data processes, accelerate AI adoption, and unlock the full potential of their data. This shift is not just about increasing efficiency—it’s about transforming the role of data engineers to fuel business growth.

Conclusion

The role of the data engineer is undergoing a significant transformation. As companies invest more in AI and data technologies, the traditional focus on data management and maintenance is no longer sufficient. By adopting a product-driven approach to data, businesses can empower data engineers to focus on higher-value tasks, such as driving innovation and creating business value. This shift is crucial for organizations seeking to stay competitive in a world where AI and data-driven insights are key to success. The future of data engineering lies in activation—not maintenance—and by embracing this change, companies can unlock new opportunities for growth and innovation.