Once AI technology came to the forefront of innovation, there was no slowing it down. Within ChatGPT’s first five days, it already had over 1 million users. This upward trend continues to trickle into other areas of businesses, from marketing, analytics, writing, you name it.

All of these variables reflect AI’s high-level, multi-usage versatility for companies. AI can work efficiently within various business roles, streamline informational pathways, and help decision-makers make better-informed choices. 

At the foundation of all aforementioned AI innovation is the cement that holds everything together—data. 

This article explains why data engineering is more critical than ever and how it enables AI to scale, process real-time data, integrate diverse sources, and adapt to new challenges.

• Why This Matters (What Your Competition Is Thinking)
• The Role of Data Engineering in AI
• Scalability and Real-Time Data Processing
  • Real-World Impact
• Integration of Diverse Data Sources
• New Challenges and Opportunities
• Conclusion

Why This Matters (What Your Competition Is Thinking)

A recent Gartner study shed light on the impact of quality data when merged with AI, reporting the following:

• 38% of Chief Data and Analytics Officers stated that their D&A architecture must be overhauled within the next 12 months
  
• 29% said they would revamp how they manage data assets to better meet governance policies and standards
• 49% of CDAOs now include generative AI in their primary responsibilities, up from 34% in 2023

Data changes the game– get ahead of data to get ahead of AI tech.

The Role of Data Engineering in AI

The impact of AI hinges on high-quality data and efficient access to that data. Data engineering ensures the data pipelines, infrastructure, and processing are built to fuel AI models. Data engineers are responsible for designing, developing, and maintaining the systems that capture, clean, and structure the massive datasets that AI relies on.

AI models need clean, structured, and reliable data to function correctly. Data engineers apply data cleansing, validation, and normalization techniques to reduce noise and enforce consistency. 

Example: A predictive AI model in healthcare needs accurate patient data to provide reliable diagnoses. If the data is flawed, the model’s predictions will be unreliable, possibly causing harm. This makes data governance and quality control pivotal in AI projects.

By maintaining rigorous data quality standards, data engineers can depend on AI models to perform their tasks accurately and effectively– creating a seamless pipeline for informed decision-making and high-level, automated analysis.

Scalability and Real-Time Data Processing

As AI systems are increasingly deployed in real-time applications, scalability and real-time data processing are non-negotiable. A scalable data pipeline ensures that as the volume of data grows, the system can handle the load without degrading performance. 

AI applications often depend on real-time insights. Predictive models need to process sensor data instantly to forecast equipment failures. To meet these demands, data engineers build systems capable of handling real-time data ingestion and processing while maintaining low-latency responses.

Real-World Impact

We’ve seen firsthand how scaling data operations can transform a business. One example is our Fast-Track Application Modernization service with a healthcare revenue cycle management (RCM) client. 

The company faced challenges in providing accurate financial data to oncology centers, which impacted its ability to secure payments from Medicare and private insurers. By implementing automated data ingestion and report generation processes, we eliminated manual reporting tasks that once took hours. Now, their system delivers real-time, error-free data insights weekly, allowing them to scale efficiently and provide more accurate financial information.

This case study illustrates how automating real-time data processes improves operational efficiency and scales AI’s ability to deliver actionable insights. Whether you’re managing complex data systems in healthcare or optimizing AI-driven traffic systems, scalability and real-time processing are critical—and data engineering is at the heart of it all.

Integration of Diverse Data Sources

Modern AI applications often need to integrate and analyze data from various sources—structured data from databases, unstructured data from text and images, and semi-structured data from logs and social media. This diversity presents many challenges, to which we propose many solutions:

New Challenges and Opportunities

AI is no longer just about feeding models with data. Now, AI is being integrated into the everyday tools data engineers use to enhance their workflows. Tools like AutoML platforms and AI-driven observability systems are helping engineers automate routine tasks such as data cleaning and pipeline monitoring.

Still, the need for real-time data processing in AI-driven applications presents new hurdles, particularly when managing large datasets in time-sensitive scenarios. 

One example is the AI traffic monitoring system we developed at Programmers Inc. for a road management client. This system utilizes computer vision to process videos from hundreds of cameras monitoring highways in real time. By detecting and reporting hazardous conditions, such as a dead animal on the track, a stopped truck, lane blockages, etc., the AI system helps enhance road safety and optimize resource allocation.

We implemented a sophisticated infrastructure running on the cloud to guarantee that these AI models can process vast amounts of video data without latency issues. However, due to the sheer volume and speed required for real-time processing, we also deployed Edge Computing technology. This decentralized data processing approach allows data to be processed closer to where it is generated—such as on local servers or IoT devices—reducing latency and bandwidth issues.

While edge computing is not traditionally part of data engineering, it introduces new layers of complexity. Data engineers must set up data pipelines and processing workflows that begin at the edge, confirming that AI models can process data in real time, even when the central cloud infrastructure cannot handle the load.

Conclusion

The relationship between AI and data engineering is one of mutual dependence. As AI technologies grow more powerful and pervasive, the demand for high-quality data and scalable, real-time pipelines grows with it. Data engineering is not just about supporting AI; it is integral to its success.

Likewise, data engineering will continue to evolve, but its importance in the AI ecosystem is, and will remain, paramount.

Don’t wait until data challenges slow down your AI initiatives. Book a demo with Programmers Inc. today and see how our data engineering solutions can help you propel your business with Generative AI technologies.