Data modeling and the AI lifecycle

Data modeling contributes to better Artificial Intelligence by providing a structured framework for organizing, interpreting, and leveraging data effectively. It helps define the relationships between different data elements, ensuring that AI systems can access clean, consistent, and relevant information for training and decision-making.  By establishing clear data schemas and reducing ambiguity, data modeling improves the quality and accuracy of machine learning models, reduces bias, enhances interpretability, and enables scalability.  Ultimately, it lays the foundation for more intelligent, reliable, and efficient AI systems by aligning data with the goals and logic of AI algorithms.

 

Conversely, AI can contribute to better data modeling by enabling advanced automation and intelligence throughout the modeling lifecycle.  It can reverse-engineer Mermaid ERD code potentially generated by GenAI outside Hackolade Studio.  The application can in turn generate new Mermaid diagrams from existing Hackolade data models (with some limitations, e.g., no support for composite keys or constraints). GenAI can be leveraged for metadata enrichment by generating meaningful descriptions for entities and attributes to be edited by subject-matter experts, and by recommending attributes based on industry standards. Furthermore, AI can propose dimensional models optimized from transactional schemas and suggest improvements such as better partition key choices, laying the groundwork for more efficient and standards-aligned data architectures.

Data modeling contributes to better AI

Developing an AI solution involves several iterative steps. The process begins with understanding the business problem and clearly defining objectives. Next, data is collected and explored to assess its structure, quality, and potential value, including identifying missing values, anomalies, biases, and uncovering patterns. The data is then prepared through cleaning and transformation, addressing issues such as incomplete data and bias. These early stages are supported by traditional data modeling practices: conceptual, logical, and physical data modeling that provide structure and context. 

 

 

 

Diagram courtesy of Dave Wells dwells@infocentrig.org

 

 

This foundational work enables the development and training of algorithms using the prepared data. Model performance is then optimized through parameter tuning and evaluated using accuracy, precision, and alignment with business goals. Finally, the AI model is deployed in a real-world environment, where its performance is continuously monitored and refined in response to new data and feedback.

 

AI contributes to better data modeling

AI can contribute to better data modeling by providing the ability to automate and enhance some aspects of the data modeling process. 

It can analyze large and complex datasets to identify hidden patterns, relationships, and anomalies that might be missed by human analysts. AI-driven tools can recommend optimal data structures, detect inconsistencies, and suggest schema improvements based on usage patterns and historical data. Machine learning algorithms also help in predictive modeling, enabling dynamic and adaptive models that evolve with new data. Additionally, AI can streamline tasks like data cleaning, entity recognition, and metadata generation, making the data modeling process faster, more accurate, and more scalable.

Currently on our roadmap, are the following features:

• Upcoming: reverse-engineer Mermaid ERD code that could have been produced by GenAI in response to some prompt executed outside of Hackolade
• Then: generate Mermaid ERD code from a Hackolade Studio model to be used in a GenAI prompt.  Note that Mermaid has some limitations, such as lack of composite PKs/FKs, Not Null constraints, etc.
• Still to be scheduled: use GenAI to create descriptions for selected entities and attributes of a model
• Use GenAI to suggest attributes for given entities, according to industry -specific standards
• Longer term: use GenAI to suggest an optimal dimensional model, given a transactional schema.  This could be done indirectly with the first 2 points in the list above.
• Use GenAI to suggest more optimal modeling, choice of partition keys, etc..  But this has not yet been designed.
• and more, based on customer feedback and suggestions.