As they say, data is the new oil in the business world. It is being used to run every crucial operation across industry verticals. From developing products to tracking supplies and customer transactions, the data never stops. How do we derive value from such vast data and make it actionable? Well, the answer lies in using Artificial Intelligence (AI). AI-powered data engineering enables businesses to automate data collection and analyses, helping them make data-driven superior decisions in real time. The use of Generative AI (Gen AI) is specifically prominent as it automates workflows and allows engineers to rapidly develop, deploy, and refine predictive models with desired speed and accuracy. Further, the technology enables engineers to focus on creative work by freeing up crucial time.
Advanced AI Techniques for Streamlined ETL Processes
Data engineers structure, clean, and store data. They manage large-scale databases, create ETL (Extract, Transform, Load) pipelines, and maintain data integrity. While these tasks remain essential, the use of AI has significantly changed the working approach of data engineers. Using AI, data engineers can automate routine tasks such as data ingestion, transformation, and cleansing. AI systems can identify anomalies, flag errors, and even clean data more efficiently than manual processes. AI enhances the ability to structure data dynamically, which is especially useful in the era of unstructured data—such as video, text, and sensor data. Unsurprisingly, KPMG found that 77% of executives believe Gen AI to have a bigger impact than any other technology.
AI-Powered Data Pipelines: Automation is Key
A typical data pipeline involves several steps – ingesting data, transforming formats, and loading into systems. Accomplishing these tasks manually causes bottlenecks and slower processing times. However, the use of AI results in the automation of the pipeline with the help of the following procedures:
- Data Ingestion: Automated tools scrape and process raw data from various sources in real time.
- Data Transformation: AI recognizes patterns in the data and formats without human intervention.
- Data Integration: Complex data from various systems is seamlessly integrated by AI-powered platforms.
- Real-Time Updates: Business intelligence tools, dashboards, and decision-making systems receive near-instantaneous updates.
Intelligent Data Accuracy: Leveraging AI to Maintain Data Integrity
Traditional data processing involved error-prone human interventions, especially as data scaled to vast levels and complex patterns. This could lead to wrong reports, bad insights, and erroneous decisions. However, data becomes much more accurate with the help of AI. The technology can flag inconsistencies and anomalies in real-time, thereby offering cleaned and structured data. Predictive algorithms can catch errors before they enter the system, thereby further enhancing the accuracy of the data entering the pipeline. This, in turn, helps businesses with better decision-making.
Real-Time Decision Making and Predictive Insights
AI is transforming data engineering through real-time decision-making. AI creates real-time data pipelines that allow businesses to analyze data on the fly. For example, AI-powered systems can track customer behavior and allow brands to adjust their strategies accordingly. These systems can also predict stock movements and offer buy/sell recommendations to brokers. Similarly, AI can be used to track supply chains, manage inventories, and source supplies, among other things.
Data Governance: AI to Address Quality Challenges
Integration of AI in data engineering poses is as good as the quality of the data. The accuracy of AI models is directly proportional to the quality of data it’s trained on. No wonder, data quality management is a priority for AI-driven data engineering. Data engineers must ensure that data governance practices are in place before integrating AI models with their processes and systems. Practices such as maintaining formats, verifying accuracy, and ensuring compliance are necessary for keeping data quality intact and results accurate.
Future of AI in Data Engineering: Strategic and Smarter
As AI gets more advanced, automation will get broader in both its scope and applications. In the future, AI systems will manage data infrastructures, create data pipelines, optimize workflows, and even predict and prevent system failures before they happen. AI will be at the heart of data-driven approaches, enabling firms to deliver superior value to their end consumers. Businesses will use AI to find trends in the market, predict customer needs, and create personalized experiences, thereby becoming more efficient in the process.
Authored by: Dhwanit Shah, Senior Vice President, Sales and Delivery –Digital Solutions, MSys Technologies
