Data

Context As part of a Data Science/Artificial Intelligence project, Gabriel Greenfield is supporting a client on renewable energy projects. To date, our work has focused on solar energy (photovoltaic) projects and purely technical projects (statistics, software development) within the client's organization. Our Achievements Automated daily calculation of metrics (e.g., average sensor temperature) at a photovoltaic site Creation of a module for detecting outliers in solar data

The Data Catalog is a data repository that captures the business context for the company. It allows you to capture the characteristics of data (concepts, definitions, terms, semantics) as used by a business domain, associating them with those of its implementation(s) and/or use(s) by systems (identifier, concepts, definitions, terms, semantics, format, syntax, type, list of allowed values, etc.). It can be an application or an application suite comprised of, among other thing

Defining a "Data Catalog" is a tricky exercise, as leaders and experts generally envision different use cases and characteristics. However, all Data Catalogs on the market seem to share two key characteristics: the management of business metadata (context, business, solution, technology, etc.) and the management of metadata for technical solutions or implementations (operational data storage systems, digital applications, transactional systems, analytical systems, etc.). What

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