The Evolution of Data Engineering: Making Data AI-Ready
By Saket Saurabh, co-founder and CEO at Nexla
Industries across the globe are in the midst of a transformation driven by the commercialization of generative AI (GenAI) technologies. GenAI has already changed how data engineering works by automating many of the steps involved in building data pipelines including data access and workflows. However, GenAI has also introduced new challenges, specifically concerning security and governance.
To achieve all the productivity gains made possible by GenAI, businesses must first overcome the associated risks, including AI hallucinations, data leaks, and regulatory compliance.
Data engineers are now more than just system builders; they need to orchestrate what GenAI does, oversee security, governance, and data quality, and ensure that AI-generated outputs are accurate and reliable, especially as GenAI tools become widely adopted across organizations. Ultimately, it is essential to design appropriate human-in-the-loop workflows to ensure trust for enterprise-critical applications.
Also Read: Why multimodal AI is taking over communication
Plan Your Work and Work Your Plan: Preparing Data for AI
As organizations pour resources into GenAI initiatives, successfully deploying AI means making sure it has access to AI-ready data. Retrieval augmented generation (RAG), the most common approach today, allows you to use general-purpose large language models (LLMs) that haven’t been trained on your data by augmenting prompts with the best data as context. That requires preparing, encoding, and loading the best data into a vector database so it can be retrieved through search and passed along with each prompt into your LLM of choice.
The fact that this approach is already getting outdated with more agentic approaches is a testament to the high speed of evolution in AI-managed processes.
Agentic retrieval spans disparate data systems and is governed, and automated by using AI to ensure the right context. Model Context Protocol (MCP) and Agent to Agent Protocol (A2A) are rapidly capturing the imagination of engineers as they try to orchestrate multiple data systems and applications to drive advanced automation in business processes.
Regardless of whether an organization utilizes RAG, fine-tuning, or full-scale model training, there are a handful of key requirements to meet:
- Data access. LLMs are only as good as their data. In the case of RAG, that means providing the best data as context. Keeping your vector database up to date using incremental or streaming updates is important. But you’ll never get all your data into a single database. You also need to try and allow the direct retrieval of data from other data sources as needed. Remember that even your key enterprise systems including CRM, ERP, HRIS, and JIRA are data systems behind APIs. That information can be critical context in improving the quality of LLM output.
- Data format. LLMs perform best when data is formatted in a specific, structured way that facilitates easy ingestion and processing. A crucial part of this preparation is “chunking” the data effectively so models can optimize how they interpret and utilize it. The goal is to structure the smaller chunks of data in a way that the LLM will understand the underlying meaning. This is arguably the second-most important part of RAG after loading all the data in the first place.
- Security and governance. This applies to all aspects of business, but handling enterprise data demands stringent security and governance measures. Implementing robust controls and policies to prevent unauthorized access or potential data breaches is a mandate. Complying with evolving regulations and internal security protocols is a never-ending challenge that becomes more complex when data is being used by LLMs.
- Scalability. The ability to scale AI initiatives is an increasingly pressing business challenge because apps can be both data- and compute-intensive, so the underlying infrastructure must also scale. Between processing large datasets and tackling complex AI workloads, systems need to meet demand without compromising performance or increasing costs prohibitively.
The Next Step: Integrating AI-Ready Data
Gartner says that nearly one-third of GenAI projects will be abandoned after an initial proof of concept, with reasons including poor data quality, inadequate risk control, and skyrocketing costs. How do organizations navigate this in the face of mounting pressure to not only utilize AI but also excel at it to build a competitive advantage?
Every organization is different, but there are six fundamental and foundational best practices each should follow to streamline data preparation and integration, avoid costly missteps and accelerate deployment:
1. Implement dynamic access to data: Ensuring seamless integration with various data sources keeps models up to date, but it requires a flexible data access framework that supports multiple integration styles and speeds. This helps AI models retrieve the most relevant data in real time, emphasizing both speed and accuracy.
2. Thorough data preparation: We discussed the “how” in the previous section, but effective data preparation, including data chunking, improves model effectiveness in processing and generating accurate responses.
3. Embrace collaboration: Establishing a collaborative environment where users share and reuse structured data helps ensure consistency and productivity across teams.
4. Automate your workflows: Automating data integration and transformation reduces complexity and streamlines the preparation of large datasets, minimizes manual effort, and enhances efficiency.
5. Prioritize security: Allocating significant resources to robust governance and security frameworks can be a tough sell within organizations, but it’s much better than the alternative—a devastating breach that ends up becoming even costlier down the road. Encapsulating all data access via AI-ready data products not only makes security and governance easier. It lets an LLM discover and use data via data products as well.
6. Build your infrastructure for scalability: The data infrastructure that supports AI must be capable of scaling efficiently. It’s a delicate balance, but the goal is to handle high volumes of data cost-effectively without compromising performance. Make sure you evaluate the underlying integration frameworks for scale.
Also Read: Why AI’s Next Phases Will Favor Independent Players
Do Data Engineers Hold the Key to AI’s Future?
Data engineering is not going away. Their expertise in data and data pipelines is critical to the success of any AI initiative.
Data Engineering is evolving, and that role is changing. Data engineers are no longer just enablers of data and data technology. They have a bigger role to play in DataOps, including complex orchestration, governed access, monitoring, and running enterprise-scale agents. They will be key architects who enable businesses to leverage the power of AI responsibly and effectively. As engineers lean into more strategic parts of data engineering and let GenAI do more automation, they must ensure LLMs have access to well-structured, up-to-date, secure, and AI-ready data.
Organizations that invest in the talent and training to develop modern data engineers will be better positioned to overcome things like poor data quality or unsustainable costs, which will result in more successful implementations. A key skill for Data Engineers will be the ability to straddle between no-code, low-code, and developer-oriented data tools, interconnecting them into a system. This means learning enough Python to be dangerous and then adopting code generation tools to be effective with leverage.
Data engineers have always needed to adapt quickly to the latest techniques. This is more important than ever as AI advances at such a blistering speed. It’s critical to continue to adapt or be left behind.
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