TRAIDA
Learning Framework
TRAIDA Framework
An educational reference for Enterprise Architecture and AI
The TRAIDA Framework (Transformative AI and Data Solutions) is an educational collection of practical reference cards designed to help architects, engineers and technology leaders understand the main building blocks of Enterprise Architecture and their relationship with Artificial Intelligence.
TRAIDA introduces the essential concepts required to understand how information systems evolve toward Enterprise AI. It is particularly intended for professionals who are beginning their journey in enterprise architecture, data management or AI transformation.
Unlike the Engage-Meta Reference Vocabulary, TRAIDA is not intended to define the official architectural language of the Engage-Meta framework. Instead, it provides a broader educational perspective covering established concepts, architectural patterns and governance practices commonly encountered in Enterprise Information Systems.
Relationship with the Engage-Meta Reference Vocabulary
The Engage-Meta Reference Vocabulary and TRAIDA have complementary purposes.
TRAIDA
- Educational framework
- Introduction to Enterprise Architecture
- Covers established industry concepts
- Learning-oriented
Suitable for newcomers
Engage-Meta Reference Vocabulary
- Reference architectural language
- Defines the official concepts of the Engage-Meta Framework
- Focused on Enterprise AI semantic architecture
- Intended to support experts designing Enterprise AI systems
The TRAIDA cards
Click HERE or on the image to download the PDF of the global map. The TRAIDA framework consists of 20 cards and 65 topics to address AI and the associated data solutions. Here you will find 9 technical cards (30 topics), 6 governance cards (17 topics) and 5+ business cards (18 topics). Each TRAIDA card is accompanied by a concise documentation that explains its importance in improving data quality and the use of AI on a large scale within the company. With its 20 cards and 65 topics, it offers a comprehensive view of enterprise architecture approached through the lens of data management and AI.
Here is the introductory slide deck for the TRAIDA cards. You can freely use it in your projects, courses, and commercial offers. By doing so, you contribute to the alignment of IT and Business for AI, thanks to the blue, red, and green cards!
Download HERE (PDF) (last update: November 04, 2024)
TRAIDA relies on a semantic platform architecture
Click HERE or on the image to download the PDF of the semantic platform architecture.
TRAIDA is based on an architectural vision that places a semantic platform at the center of the business system, essential for complete data quality control and scaling up AI.
TRAIDA white paper
Reconciling expertise in data governance, Enterprise Architecture (EA), and Artificial Intelligence represents a considerable challenge. This is the theme of our white paper, which proposes a comprehensive approach for the large-scale deployment of AI in companies.
You can download the deck of this video here (PDF).
MAINTAIN CONTROL OF YOUR IT + AI THROUGH THE MINIMUM VIABLE SCALE (MVS) ARCHITECTURE
Rather than forcing the definition of technical and business EA targets, the company first compares itself to a set of essential topics for large-scale deployment of AI and associated data solutions.
The goal is not to try to describe targets on a wide range of topics, but to limit the analysis to AI and data management. We start from the principle that the minimally viable technical architecture is based on these two devices: AI and data management.
It is important to emphasize the significance of this concept of “minimally viable architecture”, also qualified as “Minimum Viable Scale – MVS”, which aptly illustrates the idea of progressively scaling the architecture.
Download the executive summary (PDF): In ENGLISH and FRENCH.
Enterprise AI Integration Scenarios
The AI Add-on scenario is deployed as a starting point to implement initial AI automation that addresses simple but tactically significant cases. It supports the operations of an early-stage deployment. In this scenario, the AI automations simply invoke the existing systems.
The AI Booster scenario is deployed to support a medium-sized business with a simple core activity, or one already supported by an ERP solution. It serves to boost a rigid core system by adding a more agile, low-code AI layer on the front end.
The AI Core scenario is used to deploy an alternative to the conventional core-system and ERP approach, enabling greater flexibility through the native integration of AI across the organization.