Asset Data Modeling - Asset Data Modeling rapidly turns raw data into workable data in 3 steps: Tag Mapping, Event Labeling, and Sensor Auditing. Semi-automated tag mapping allows you to rapidly map tags in bulk to an Asset Data Model, which reflects the relationships between people, process, and physical equipment. Event Labeling adds the context necessary for analytics by enriching time-series data with event metadata. Sensor Auditing surfaces data model and value issues for IT and engineering to address. The resulting Asset Data Model is continuously and automatically updated to ensure it remains up to date as tag changes occur, equipment comes online, etc. It can be exported in diverse forms (e.g., PI Asset Framework) for use by anyone or any application. Data Transformation - To lay groundwork for advanced analytics such as predictive modeling, data undergoes treatment to ensure data is high quality and free from issues such as gaps, stale and null values. A series of transformations can be performed on numerous datasets to create advanced metadata events and prepare data for business intelligence. Predictive Modeling with Machine Learning - Using prepared data, our team of experienced, industrial data scientists can help you develop and operationalize custom predictive models using R and Python scripts. Predictive models may include time until equipment failure, efficiency recommendations, forecasting, and system modeling. Application Connection for Visualizing Predictive Insights - The Element Platform connects predictive models as well as selected subsets of PI System data to your preferred applications to display actionable insights. Insights may include: production by asset, time until equipment failure, prioritized asset maintenance, short- and long-term cost impact of various fixes, and the utilization of service team members in the field.