Intelligent Data Platform: How to Boost Data Engineering with AI?

Explore how AI is redefining data engineering through intelligent data platforms, unlocking new perspectives on how businesses harness the power of data to accelerate innovation, optimize performance, and stay competitive in a constantly evolving digital world.

What is an Intelligent Data Platform?

What is a Traditional Data Platform?

To understand the concept of an Intelligent Data Platform, it’s essential to first explore what a traditional data platform is. These platforms are designed to centralize and manage an organization’s data, making it easier to collect, store, analyze, and utilize that data for various purposes such as business intelligence, data repositories, or predictive analytics.

Traditional data platforms integrate data from a variety of internal and external sources, structure it, and make it accessible for in-depth analysis. Their main objective is to provide organizations with easy access to reliable, actionable data.

The contribution of Artificial Intelligence to Data Platforms

The integration of Artificial Intelligence into data platforms marks a significant evolution in how organizations manage and exploit their data. AI doesn’t merely optimize existing processes—it transforms the core functions of traditional data platforms, ushering in new paradigms for data usage and value creation.

Whereas earlier generations of data platforms focused primarily on basic tasks like data collection, storage, and visualization, AI enhances this process dramatically. It enriches every stage of the data value chain by introducing advanced capabilities such as predictive analytics, personalized recommendations, and intelligent process automation.

As a result, Artificial Intelligence is radically transforming data platforms—making them not only more powerful, but also smarter. These advancements turn data platforms into true engines of innovation and competitiveness.

Intelligent Data Platform: Augmented Data Engineering

By optimizing every stage of the data value chain—from identification to analysis—an Intelligent Data Platform (IDP) enables organizations to fully harness the power of their data. It not only simplifies data collection and quality enhancement but also streamlines transformation, analysis, and governance, ultimately enabling faster and more informed decision-making. In doing so, it redefines the roles and workflows of all data-related professions.

Augmented Development

Modern data platforms now integrate AI agents, such as Copilot in Microsoft Fabric, designed to support data engineers in building and optimizing data pipelines.

A concrete example of this integration is found in deployment workflows, particularly through CI/CD testing.

The CI/CD (Continuous Integration/Continuous Deployment) process automates the stages of application development, testing, and deployment to ensure rapid and reliable updates. AI enhances this process by enabling self-healing during testing. As code is deployed, automated tests detect and fix anomalies without disrupting the pipeline, ensuring a smooth transition from development to production environments.

Data Identification and Collection

An Intelligent Data Platform significantly improves efficiency at the very first step of the data value chain: data identification and collection. Data analysts spend less time on tedious manual data gathering and more on high-value tasks such as data interpretation, insights generation, and action planning. Advanced tools automate these processes by efficiently retrieving data from various public sources. This automation allows analysts to focus on interpreting and leveraging insights rather than chasing down raw data.

Ensuring Data Quality

Next, the IDP ensures that data goes through a rigorous governance and reliability process, making it ready for in-depth analysis. AI can update outdated code libraries, resolve conflicts between them, and detect quality issues that traditional methods might miss. It can even generate synthetic data to fill in gaps—a practice known as data augmentation.

Data Transformation and Processing

The Intelligent Data Platform simplifies the creation of processing logic in various contexts:

• For developing new data processing workflows
• For technological migrations aimed at overcoming legacy system limitations
• For enabling non-technical users to perform data analysis more easily, such as generating queries without needing deep SQL knowledge

This capability is particularly relevant for transforming unstructured data.

While structured data is organized in predefined formats (like relational databases), unstructured data lacks a specific schema—examples include text, images, and video. AI embedded within the platform helps convert unstructured into structured data. For instance, it can extract key information from reports or perform web scraping to gather and reorganize data from online sources. It can even analyze video content to extract meaningful insights.

Furthermore, AI can extract and categorize metadata from technical documents, associating it with specific assets or products—enhancing how data scientists analyze and manage information. For example, it can automatically detect and tag relevant content from a technical document based on the associated product or service.

Data Analysis and Visualization

The IDP then plays a central role in optimizing data analysis and visualization. Tools like Microsoft Power BI provide smart visualizations based on the data sets provided. By leveraging natural language interfaces via AI agents, users can request specific analyses—for example, trends in revenue growth over recent years—and instantly generate appropriate charts and forecasts. This greatly improves analysis efficiency and helps users quickly grasp trends and insights.

Documentation et refactoring

The IDP also improves code refactoring—the generation and rewriting of code during modernization phases. It creates recontextualized, personalized documentation tailored to the user’s level of expertise, whether junior or senior. This helps eliminate technical debt and ensures consistency in the information shared across teams. The documentation is always up to date and relevant, boosting both the accuracy and efficiency of analysis workflows.

Data Governance

Finally, the IDP strengthens data governance by analyzing data obsolescence, verifying compliance with business rules, and ensuring adherence to current regulations. For example, it identifies data that needs manual compliance actions and flags content that may be inappropriate for certain audiences.

Observability

Observability within a Data Platform ensures data remains reliable, available, and secure. It allows for rapid detection of anomalies, preemptive problem resolution, and compliance with data protection laws. Continuous monitoring of pipelines prevents disruptions, enhances performance, and ensures smooth, compliant use of the platform.

To better understand this approach, here are some key use cases:

FinOps and Cost Forecasting

In the realm of FinOps, AI is used to forecast future costs and highlight areas for improvement—enabling more accurate and efficient financial management. AI models analyze historical financial data to predict future trends and suggest strategies for cost optimization.

Managing Data Drift

Data drift refers to the phenomenon where the data used by a machine learning model evolves over time, potentially degrading the model’s performance. Since ML models are often trained on static datasets, any change in real-world data can render them less accurate or even obsolete.

To maintain model accuracy, it's essential to detect, measure, and mitigate data drift using techniques such as regular retraining, automated drift detection, and continuous algorithm tuning.

AI models make this process more manageable by automating the monitoring of drift, anticipating potential issues, and gathering new data while archiving stale inputs—thus ensuring consistent model performance.

AI presents a valuable opportunity for businesses to enhance the productivity and efficiency of their Data Platforms, while also standing out in a competitive environment. It also augments the skills of data professionals, contributing to the rise of “augmented humans.”

Adopting an Intelligent Data Platform supports improved data quality, accelerates data availability, and streamlines the development of new products and services. It also optimizes time to market, enabling companies to respond more quickly to market demands and create value more effectively. In this way, the IDP becomes a strategic lever to drive business growth.

Arthur Lecras, Data Consultant
Elodie Kunz, Data Practice Leader
Victorien Goudeau, Full Stack Engineer
Anas Nasir, Lead Data Scientist
Guillaume Wattellier, Data & AI Service Line Manager