Transformation Approach

In today’s fast-paced business landscape, enterprises face the challenge of navigating through intricate systems and processes while aiming to be agile and adaptive.

But what can help you to master this complexity?

The following diagram explains our approach in a nutshell, let’s walk through it together.

At transentis, we’ve been applying, testing and improving our transformation methodology for over two decades - from day one our approach to transformation was model-driven and architecture-centric:

• Model-Driven: Capture everything you learn about an enterprise in a visual model.
• Architecture-centric: Identify the key building blocks and the connections between them and show how they work together to create value, bevor getting into the details of each building block.

When it comes to modeling, we don’t just build static models - we love creating interactive simulation models that help you understand the dynamics of an enterprise.

The model we built as part of the Car Sharing Case Study was created using the ArchiMate modeling language. The Car Sharing Case Study pages are generated entirely from the underlying model, which show that models aren’t just useful for capturing in a information in a complete and consistent way, they are also useful as a basis for creating documentation that for different groups of stakeholders.

In a next step, we will be enhancing the model with live data, effectively turning it into a digital twin.

In our early consulting days, it was quite difficult to get good data - but thanks to the data science revolution, most companies are now drowning in data: our transformation approach utilizes all available data and gives it meaning by connecting it to our models and using it systematically to make better decisions in complex situations.

Building models is a human endeavour, because it is all about gathering information from different stakeholders, shaping that information and using it to create shared understanding. Just finding a common vocabulary is difficult in most organizations.

But, because of data abdunance, we find ourselves using machine learning techniques to build models, to learn from data and to find optimal scenarios in simulation models.

Today, our methodology is moving into its next stage, because we are systematically incorporating Generative AI: we use GenAI for general research, but also to have conversations about the enterprise we are transforming.

For this to work, we need to provide the relevant context information to the GenAI.

Because transformation is a long-running process, the information we have about an enterprise is permanently evolving.

• Our solution is to first capture the information we have about an enterprise in a visual model - visual models are a very efficient way of capturing structured information and - because they are visual - they are a very good basis for discussion with stakeholders.
• We sync the information we have in our models into a graph databases, thus creating a knowledge graph of the enterprise - this is easy from a semantic point of view, because the models we build have a graph-like structure.
  • One advantage of having a graph database is that you can make them available via an API and can query them.
  • Another advantage is that we can extend the data in the graph database with “live” data from the enterprise - this is very powerful and is something you cannot easily do with models themselves.
• Now that we have the information in a knowledge graph, we can make that information available to Generative AI using Retrieval-Augmented-Generation (RAG). This effectively means that you can now use Generative AI to reason about your enterprise interactively.