You want to:
- Access relevant information in real time
- Aggregate and combine heterogeneous data from multiple sources
- Eliminate tedious data preparation
- Break silos and allow your teams to easily share the same data
- Build advanced indicators calculated from your data, based on your business knowledge
- Have a single reliable and shared data source for your teams
We have developed a unique contextualization engine, 100% configurable and designed for industrial processes.
Contextualization engine: 100% configurable, 100% industrial
Our contextualization engine (formerly Process Data Lake) is there to simplify your daily life around industrial data:
- It allows you to structure and store your different types of data (temporal data, events, production data, traceability, genealogy, equipment...) based on a data model specifically designed for industrial processes and completely standardized.
- It allows you to integrate your data continuously and, if necessary, to transform it to adapt it to the standard data models of the contextualization engine.
- Thanks to its standard data models, It offers you predefined treatments that allow you to easily combine and transform your data into relevant information.
- With its continuous data processing you can always have up-to-date information at all times.
- It is 100% configurable to give you maximum autonomy and agility around your data to meet the new challenges that come your way.
Ready to take care of all your data regardless of your needs
Our contextualization engine is designed specifically for industrial processes. It can natively process many types of data:
- Les time series data from process sensors and automated systems (temperature, pressure, flow, program status, etc.).
- The information related to traceability context (when and where the production was carried out, details of the durations of the various production phases, recipes used...) and genealogical context (to understand the data throughout a complex production process).
- The data of traceability (quality control of finished products, intermediate products and raw materials, real use of raw materials in the recipe...)
- Event data (alerts from supervision systems, events recorded by operators...)
- The context of equipment or assets to describe the data associated with factory assets (equipment, IoT sensors, production lines, etc.) in order to simplify the processing and use of data on similar assets.
- Vectorial data from advanced sensors (spectrophotometers, chromatography, vibration sensors, PSD sensors...)
Details that make a difference
- A great wealth of standard data models adapted to industry, surpassing traditional industrial data historian solutions.
- Ready-to-use business data models that are more relevant than generic data solutions.
- An approach to continuous data processing to effectively meet the challenges of industrial operations.
- Native management of physical quantities, traceability and unit conversion.
- A 100% no-code configuration with bulk configuration tools, including audit trail to ensure auditability.
- Total openness: all the operations performed in the contextualization engine are immediately accessible to other applications thanks to the API.
