Wondering how to model a transformation? This article is for you. It presents a structural framework designed to organize an organization’s strategic and operational data. It defines a series of key entities, such as ambitions, challenges, and initiatives, while specifying their specific attributes and performance indicators. It emphasizes logical interconnections, illustrating how projects mobilize resources and transform business processes. This modeling makes it possible t

At Gabriel Greenfield, we work with organizations that have invested years in their data governance. And today, many of them are asking the same question: “Does our data governance prepare us to govern AI?” Yes, but not automatically. The foundations are there. The transition, however, must be built consciously. Here are the 10 pillars of this transition, along with what we’re seeing in practice. ① Data quality → Reliability of production models In traditional data governance

The last decade has been marked by the "data-first" revolution. Companies rushed to collect metrics, create dashboards, and let algorithms guide their decisions. But what if this approach had inadvertently sidelined something equally valuable: a deep understanding of business processes and human expertise? The Unforeseen Consequences of "Data-First" This paper reveals a trend that many organizations will recognize. The aggressive focus on data and metrics in recent years has

APIs are often highlighted in the field of integration and middleware. However, on their own, they cannot represent the full diversity of integration patterns. In fact, several integration technologies must be combined to cover all use cases. For example, we can combine the two major patterns in the integration market (modular logic supported by API Management and mass logic supported by Data integration) to form a common approach to integration: a second-generation hybrid in

APIs are frequently highlighted in the integration and middleware fields. However, on their own, they cannot represent the full diversity of integration patterns. Indeed, it is necessary to combine several integration technologies to cover all use cases. For example, we can bring together the two main patterns in the integration market (modular logic supported by API Management and mass logic supported by Data Integration) to form a common integration approach: a second-gener

In recent years, the field of process mining has emerged, with techniques aimed at translating data captured during process execution into actionable insights. What is process mining? What data should be used for process mining? What types of analysis are possible with process mining tools? The article below will answer all these questions. Definition of Process Mining Modern information systems allow us to track, often in great detail, the execution of processes (sequences o

Data-as-a-Service and data quality driving innovation: the new Data Management platforms The new generation of data platforms is being rolled out with the motto “self-service data, for everyone, in real time.” The meaning behind the many initiatives we are seeing in the data market demonstrates a desire to make business users more autonomous in their use of data. This does not mean that technology and IT are fading into the background. On the contrary, they have never been so