One of the advantages of SCADA is the ability to collect masses of raw data from field devices and write this data into a standard SQL database. One of the biggest challenges, however, is the ability to manage and extract information from these masses of raw SCADA data. Adroit SCADA Intelligence is a data warehousing system which collects and processes data from disparate data sources and makes the resulting information available on a common platform to enable decision making.
Adroit SCADA Intelligence allows the user to define any tag (Item of data) in terms of a standard context model which basically defines ‘where’ in the business the raw data belongs which makes data filtering and grouping easy when retrieving information from the system. For each Item the type of processing is also defined so that data calculations and aggregations are also made easier (and quicker) when retrieving information from the system.
Raw data is collected from defined data sources every hour (by default) and processed through the ETL (Adroit SCADA Intelligence Server) into the data warehouse. The Microsoft SQL Server Analysis Services (SSAS) is then triggered to reprocess the OLAP Data Cube where information is available for reporting or analysis. The OLAP data cube is a standard format which means that the information is now available to most OLAP compliant reporting and analysis software tools.
Adroit SCADA Intelligence uses various modules of Microsoft’s BI technology stack, ASI Manager manages the configuration hosted in a Database engine, ASI Analyser connects to the Analyses Services and the ETL runs on Integration Services.
A data warehouse is a centralized repository that stores data from multiple information sources and transforms them into a common, multidimensional data model for efficient querying and analysis. SCADA Intelligence is a star schema database (also called a multi-dimensional schema) and consists of one or more fact tables referencing any number of dimension tables.
The star schema separates data into facts, which hold the measurable, quantitative data and dimensions which are descriptive attributes related to fact data. Star schemas are optimized for querying large data sets and are used in data warehouses and data marts to support OLAP cubes, business intelligence and analytic applications, and ad hoc queries.