Corporations around the world depend on STATISTICA Enterprise for their business intelligence and data mining needs. STATISTICA answers these needs by providing streamlined, single-tool access to data from a wide variety of sources. Once STATISTICA Enterprise is configured, verified users have easy access to data extraction, filtering, and drill-down tools — all from within STATISTICA. The data sources supported by STATISTICA Enterprise include the following:
- Enterprise Resource Planning (ERP) such as SAP, STATISTICA is SAP-Certified
- Laboratory Information Management Systems (LIMS)
- Manufacturing Execution Systems (MES)
- Any OLE DB or ODBC relational database such as Oracle, Microsoft SQL Server, or Microsoft Access
- Hierarchical Process Cubes
- Flat files such as CSV, Microsoft Excel spreadsheets, SAS, SPSS
- Excel spreadsheets can be imported or opened as an Excel spreadsheets within STATISTICA and processed in-place
- Data historian repositories such as the PI Data Historian from OSI Soft, Inc.
- In-Place Database Processing (IDP), query multidimensional databases containing terabytes of data and process data without importing to local storage
- Web-based data entry
When combined with STATISTICA Extract, Transform, and Load (ETL), STATISTICA Enterprise provides efficient processing of data from standard databases (Microsoft SQL, Oracle) as well as specialized process databases using the optional PI Connector tool (e.g., OSI Pi), providing powerful STATISTICA data processing capabilities for data filtering, aggregation, and analyses.
STATISTICA Enterprise also provides convenient tools for filtering data and for viewing the associated metadata, including Hierarchical Process Cubes that contain information about how material moved through the sequence of steps for meaningful monitoring, qc-charting, reporting, root cause and other analyses to be performed. Engineers and analysts are empowered to select the relevant process and easily gain access to necessary data, and to review the movement of materials and batches through the production process. Read more...
The STATISTICA Enterprise administration application, STATISTICA Enterprise Manager, supports a multi-layered role-based approach, providing precision access control to data. For example, your database administrators can be given permission to create and modify database connections and queries, while your engineers can be given permission to run those queries and analyze the resulting data. Additionally, each engineer can be assigned permissions to perform only the analyses that pertain to his or her work.
STATISTICA Query is used to easily access data from a wide variety of databases (including many large system databases such as Oracle, Microsoft SQL Server, Sybase, etc.) using Microsoft's OLE DB conventions. OLE DB is a powerful database technology that provides universal data integration over an enterprise's network, from mainframe to desktop, regardless of the data type. OLE DB offers a more generalized and more efficient strategy for data access than the older ODBC conventions because it allows access to more types of data and is based on the Component Object Model (COM).
STATISTICA Query supports multiple database tables; specific records (rows of tables) can be selected by entering SQL statements, which STATISTICA Query automatically builds for you as you select the components of the query via a simple graphical interface and/or intuitive menu options and dialogs. Therefore, an extensive knowledge of SQL is not necessary in order for you to create advanced and powerful queries of data in a quick and straightforward manner. Multiple queries based on one or many different databases can also be created to return data to an individual spreadsheet, and you can maintain connections to multiple external databases simultaneously.
In-Place Processing of Data on Remote Servers
The query facilities, when offered as part of the enterprise versions of STATISTICA, are additionally enhanced by options to process data from remote servers, i.e., "in place" without having to import them and create a local datafile. This technology is useful for processing extremely large datafiles where it can produce significant performance gains and enable you to process datafiles that exceed the storage capacity of the local device.